CN114897246A - Airship multi-task point route planning method based on genetic algorithm and greedy strategy - Google Patents
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Abstract
The invention relates to an airship multi-task point route planning method based on a genetic algorithm and a greedy strategy, wherein the minimum turning radius of an airship is calculated according to the minimum flying speed and the maximum turning slope angle when the airship turns; obtaining the optimal flight sequence of the airship from the flying point to the landing point through all task points according to the global search capability of the genetic algorithm; the route planning method based on the greedy strategy sequentially obtains the optimal route between every two adjacent task points in the optimal flight sequence, connects the optimal route between every two adjacent task points, and finally obtains the optimal route for returning the airship to the landing point from the departure point through all the task points. The invention accurately passes through the multi-task point and quickly and accurately generates the optimal air route under the influence of the large turning radius of the airship, thereby improving the flight efficiency; the global search capability of the genetic algorithm and the local optimal characteristic of the greedy strategy are combined, and the operation efficiency is improved.
Description
Technical Field
The invention relates to the technical field of flight path planning, in particular to an airship multi-task point flight path planning method based on a genetic algorithm and a greedy strategy.
Background
The heavy-load semi-hard airship is different from a common unmanned aerial vehicle for aerial photography, and the heavy-load semi-hard airship is large in size, large in load and long in flight, and can be determined to span multiple cities (or regions) to perform tasks such as observation, transportation and the like, for example, a Germany Ceberlin airship, an American ML866 airship, a Chinese HA380 and the like. The heavy-load semi-hard airship is different from an airplane, is large in size and large in windward area, and when a flight task is executed, the airship inevitably has a large turning radius after passing through each task point. Therefore, when the heavy semi-rigid airship passes through a multi-task point to perform environment monitoring or simulation test, under the influence of the turning radius, the airship is difficult to be ensured to accurately pass through the multi-task point and improve the flight efficiency.
Disclosure of Invention
The invention solves the problems: the method overcomes the defects of the prior art, provides the airship multitask point route planning method based on the genetic algorithm and the greedy strategy, and can ensure that the airship accurately passes through the multitask point and improve the flight efficiency under the influence of the turning radius.
The technical scheme of the invention is as follows: a method for planning multi-task point routes of an airship based on a genetic algorithm and a greedy strategy comprises the following steps:
s1: acquiring the minimum flight speed and the maximum turning slope angle parameter information of the airship and the coordinate information of a multi-task point;
s2: calculating to obtain the minimum turning radius of the airship passing through each task point according to the acquired minimum flight speed and maximum turning slope angle parameter information of the airship;
s3: according to the provided global search method of the improved genetic algorithm, the global optimal flight sequence of the airship returning to the landing point from the takeoff point through all task points is obtained according to the acquired coordinates of the multi-task points;
s4: on the basis of the overall optimal flight sequence, a route planning method based on a greedy strategy is provided, the overall optimal solution for solving the route planning of the airship at the multiple task points is decomposed into a local optimal solution for solving the route planning between every two adjacent task points by adopting the greedy strategy, and the shortest path between every two adjacent task points in the optimal flight sequence, namely the optimal route, is sequentially obtained by adopting the minimum turning radius of the airship; then connecting the optimal routes between every two adjacent task points, and finally obtaining the optimal route of the airship returning to the landing site from the departure point through all the task points, for example, the optimal flight sequence obtained by S3 is: starting from-3-1-5-6-2-4-descending, and sequentially obtaining the shortest path between the starting-3, the starting-3-1, the starting-5-6, the starting-6-2, the starting-2-4 and the starting-4-descending in the step S4; when the shortest path is calculated in S4, the minimum turning radius of the airship calculated in S2 is used in consideration of the influence of the turning radius.
Preferably, in the step S3, since the flight path planning problem of the airship may be described as a closed loop traversing n task points from the departure point, a global search method for improving a genetic algorithm is adopted, so as to solve the disadvantage that the route planning method based on a greedy strategy proposed in S4 obtains a local optimum and cannot realize a global optimum, and obtain a global optimum flight sequence passing through all task points. The improvement of the genetic algorithm comprises the steps of randomly generating a plurality of individuals with starting points as flying points when initializing a population, sequencing the population by using adaptive values when performing cross operation, and performing cross interchange on partial node sequences with the same length in two adjacent individuals according to single-point cross, namely performing single-point cross on the large adaptive values and the large adaptive values, so that the convergence precision of the algorithm can be improved.
Preferably, in the step S4, the greedy-strategy-based route planning method decomposes the global optimal solution for solving the route plan of the airship at the multiple task points into a local optimal solution for solving the route plan between every two adjacent task points by using the idea of the greedy strategy on the basis of the optimal flight sequence of the multiple task points, decomposes the global optimal solution with a large computation amount into a local optimal solution with a small computation amount, improves the computation efficiency, and finally obtains the optimal route between every two adjacent task points in the optimal flight sequence in sequence.
Preferably, the step S4 includes the following sub-steps:
s41: determining an optimal flight path from the starting point to the 1 st task point, wherein the airship can take off in any direction at the starting point, and the optimal flight path from the starting point to the 1 st task point is a straight line flight path from the starting point to the 1 st task point;
s42: determining every two adjacent task points P between the 1 st task point and the nth task point i And P i+1 1,2,3, an optimal course between n-1;
s43: and determining the optimal route from the nth task point to the landing point, wherein the calculation process of the step S43 is the same as that of the step S42 for the convenience of calculation.
Specifically, according to two adjacent task points P in the upper section of the optimal flight sequence i-1 To task point P i Is optimized to the task point P in the route i Center O of the arc i Coordinates, the task point P can be obtained through step S42 i To task point P i+1 Then sequentially obtaining every two adjacent task points P in the optimal flight sequence i 、P i+1 The optimal route between the two task points is finally connected with every two adjacent task points P in sequence i 、P i+1 And obtaining the optimal air route for returning the airship to the landing site from the starting point through all the task points.
Preferably, in the step S41, since it is assumed that the airship can take off in any direction at the departure point, the optimal route from the departure point to the 1 st mission point is a straight route from the departure point to the 1 st mission point.
Preferably, in step S42, two adjacent task points P are i And P i+1 The optimal route between includes a mission point P i The arc course, the middle straight course and the task point P i+1 The arc route is located, thereby determining two adjacent task points P i And P i+1 The optimal course line between the two needs to determine the circle center O i ', center O i+1 Tangent point T i And tangent point T i+1 The coordinates of (a). Step S42 therefore includes the following sub-steps:
s421: determining the center of a circle O i The coordinates of the' are two adjacent task points P in the upper section of the optimal flight sequence i-1 To task point P i Optimal route of, available mission Point P i Center O of the arc i Coordinates by judging from the center O i Task point P i And a task point P i+1 Angle O of i P i P i+1 To determine the task point P i To task point P i+1 Is optimized to the task point P in the route i Center O of the arc i ' coordinates;
s422: determining the center of a circle O i+1 Translating the obtained local coordinate system OXY of the multi-task point to the origin and the task point P i+1 The coincident position is obtained as the center O i+1 The local coordinate system O ' X ' Y ' of the task point P i+1 Center of circle O of i+1 Is expressed using a parametric method, wherein the parameter θ ∈ [0,2 π);
s423: determining tangent point T i And tangent point T i+1 According to the task point P i Task point P i+1 The center O obtained in step S421 i ' and center O obtained in S422 i+1 To determine a task point P i The tangent point of the arc route and the straight line route is T i And a task point P i+1 Tangent point T of arc route and straight line route i+1 To obtain a task point P i To task point P i+1 Is contained in the optimal course P i T i Straight line route T i T i+1 And a circular arc course T i+1 P i+1 ；
S424: in order to prevent the circular arc course P in step S423 i T i And arc course T i+1 P i+1 For major arcs, i.e. circular arc navigations T i+1 P i+1 The line is larger than the semicircle, the circular arc route is equally divided into two sections of minor arc routes, namely the circular arc route smaller than the semicircle, and the circular arc route P is determined i T i Middle point M of i And arc course T i+1 P i+1 Middle point M of i+1 The coordinates of (a);
s425: determining the optimal route, and obtaining the center of circle O i ', center O i+1 Tangent point T i Tangent point T i+1 Middle point M i And midpoint M i+1 Coordinates of (2), available task pointsP i To task point P i+1 Of course P i P i+1 Is only at the center O i+1 The coordinates are correlated. Task point P i To task point P i+1 Of course P i P i+1 The total distance will follow the center of circle O i+1 The coordinates are changed by changing the task point P i+1 Center of circle O of i+1 The coordinate of (2) is changed, namely the value of the parameter theta epsilon [0,2 pi) is changed to obtain the task point P i To task point P i+1 Of course P i P i+1 Determines the task point P i To task point P i+1 The optimal route of (2);
s426: center of circle O i+1 The local coordinate system of (2) is translated to convert the center of the circle O i+1 The local coordinate system O ' X ' Y ' is converted back to the local coordinate system OXY for obtaining the multi-task point, and the task point P in the local coordinate system OXY is obtained i To task point P i+1 Optimal route, mission point P i+1 Center of circle O of i+1 The coordinates of (a);
s427: repeating the steps S421-S426, and sequentially determining the task point P 1 To the meridian P n Every two adjacent task points P between points i And P i+1 The optimal course in between.
And further, sequentially connecting the optimal routes between every two adjacent task points to finally obtain the optimal route for returning the airship from the departure point to the landing point through all the task points.
Compared with the prior art, the invention has the advantages that: the airship multi-task point route planning method based on the genetic algorithm and the greedy strategy can accurately pass through the multi-task points under the influence of a large turning radius of an airship and quickly and accurately generate the optimal route, so that the flight efficiency is improved; the global search capability of the genetic algorithm and the local optimal characteristic of the greedy strategy are combined, and the operation efficiency is improved; the method has wide applicability and can be suitable for various complex flight task conditions existing among multi-task points.
Drawings
FIG. 1 is a schematic flow chart of an airship multitask point route planning method based on a genetic algorithm and a greedy strategy according to the invention;
FIG. 2 is a schematic diagram of a greedy strategy-based route planning method of the present invention;
FIG. 3 is a diagram of determining a task point P according to the present invention i To task point P i+1 A flow chart of the optimal route of (1);
FIG. 4 is a schematic diagram of a trajectory of a route generated by the route planning method of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and specific embodiments.
As shown in FIG. 1, the method for planning the multi-task point route of the airship based on the genetic algorithm and the greedy strategy comprises the following steps:
s1: acquiring the minimum flight speed and the maximum turning slope angle parameter information of the airship and the coordinate information of a multi-task point;
s2: calculating to obtain the minimum turning radius of the airship passing through each task point according to the acquired minimum flight speed and maximum turning slope angle parameter information of the airship;
s3: according to the provided global search method of the improved genetic algorithm, the global optimal flight sequence of the airship returning to the landing point from the takeoff point through all task points is obtained according to the acquired coordinates of the multi-task points;
in step S3, since the flight path planning problem of the airship can be described as a closed loop traversing n task points from the departure point, the proposed global search method for improving the genetic algorithm is adopted to solve the disadvantages that the flight path planning method based on the greedy strategy proposed in S4 obtains the local optimum and cannot realize the global optimum, and the global optimum flight sequence passing through all task points is obtained.
Step S3 includes the following substeps:
s31: setting maximum evolution algebra, cross probability and mutation probability; calculating the distance between any two task points;
specifically, the maximum evolution algebra represents the genetic iteration times of a genetic algorithm, the cross probability represents the probability of cross variation between every two adjacent individuals in a population, and the variation probability represents the probability of variation of each individual in the population;
s32: initializing a population, and randomly generating a plurality of individuals with starting points as flying points;
s33: selecting operation, wherein excellent individuals are inherited to the next generation, the distance between any two task points is calculated according to S31, the total length of a path of each individual is obtained, the adaptive value of each individual in the population is the reciprocal of the total length of the path, the individuals in the population are sorted from large to small according to the adaptive value and divided into three parts, the individuals arranged in the front and having large adaptive value are copied into two parts, the individuals in the middle are copied into one part, the individuals behind and having small adaptive value are not copied, and a new population is formed to replace the original population;
s34: and (4) performing cross operation, wherein every two adjacent individuals in the population have a certain cross probability, and pairing and crossing the excellent individuals selected in the S33 to generate new excellent individuals. Firstly, the population is sequenced by adaptive values, and partial node sequences with the same length in two adjacent individuals are subjected to cross exchange according to single-point crossing, namely, the large adaptive value and the large adaptive value are subjected to single-point crossing, so that the convergence precision of the algorithm is improved, and a new individual is generated to replace a source individual;
s35: performing mutation operation, wherein each individual in the population has a certain mutation probability, performing mutation on new individuals generated in a crossing manner in S34 to generate new excellent individuals, firstly, calculating an average adaptation value of the population, multiplying the mutation probability of the individuals of which the adaptation value is greater than the average adaptation value by an operator randomly generating [0,1], reducing the mutation probability of a larger adaptation value in the individuals, performing mutation on the individuals of which the adaptation value is less than the average adaptation value according to the mutation probability set in S31, then performing partial mutation on the individuals, namely, randomly selecting two task points in an individual sequence, exchanging the positions of the two task points in the individual sequence, and performing mutation to obtain new excellent individuals;
s36: repeating the steps S33-S35 until the genetic algebra is the maximum evolution algebra, and outputting the individual with the maximum fitness in the evolution process, namely outputting the optimal flight sequence of the multi-task points;
s4: according to a greedy strategy-based course planning method, on the basis of an optimal flight sequence, decomposing a course planning problem of multi-task points of an airship into a course planning problem between every two adjacent task points by adopting a greedy strategy idea, and sequentially obtaining an optimal course between every two adjacent task points in the optimal flight sequence by adopting the minimum turning radius of the airship;
in step S4, the greedy-strategy-based route planning method decomposes the route planning problem of the airship into a route planning problem between every two adjacent task points by using the idea of the greedy strategy on the basis of the optimal flight sequence of the multiple task points, decomposes the global optimal solution with a large computation amount into a local optimal solution with a small computation amount, improves the computation efficiency, and finally obtains the optimal route between every two adjacent task points in the optimal flight sequence in sequence.
In order to simplify the problem and facilitate calculation, the invention does not consider the direction problem of the airship at the take-off and landing point, and proposes the following assumptions:
(1) the airship can take off in any direction at the flying starting point;
(2) the airship may return to the drop point from any direction;
(3) the takeoff and landing points of the airship are overlapped into the same point.
As shown in fig. 2, the method for planning routes based on greedy strategy in step S4 includes the following sub-steps:
s41: determining an optimal route from a starting point to a 1 st task point;
specifically, the airship can take off in any direction at the starting point by the assumption (1), and the optimal flight path from the starting point to the 1 st task point is a straight flight path from the starting point to the 1 st task point;
s42: determining every two adjacent task points P between the 1 st task point and the nth task point i And P i+1 1,2,3, an optimal course between n-1;
s43: the optimal route from the nth task point to the landing point is determined, and from the assumption (2), for the convenience of calculation, the calculation process of step S43 is the same as that of step S42.
Specifically, according to two adjacent task points P in the upper section of the optimal flight sequence i-1 To taskPoint P i Is optimized to the task point P in the route i Center O of the arc i Coordinates, the task point P can be obtained through step S42 i To task point P i+1 Then sequentially obtaining every two adjacent task points P in the optimal flight sequence i 、P i+1 The optimal route between the two task points is finally connected with every two adjacent task points P in sequence i 、P i+1 And obtaining the optimal air route for returning the airship to the landing site from the starting point through all the task points.
As shown in fig. 3, preferably, in the step S42, two adjacent task points P are i And P i+1 The optimal route between includes a mission point P i The arc course, the middle straight course and the task point P i+1 The arc route is located, thereby determining two adjacent task points P i And P i+1 The optimal course line between the two needs to determine the circle center O i ', center O i+1 Tangent point T i And tangent point T i+1 The coordinates of (c). Step S42 therefore includes the following sub-steps:
s421: determining the center of a circle O i The coordinates of the' are two adjacent task points P in the upper section of the optimal flight sequence i-1 To task point P i Optimal route of, available mission Point P i Center O of the arc i Coordinates by judging from the center O i Task point P i And a task point P i+1 Angle O of i P i P i+1 To determine the task point P i To task point P i+1 Is optimized to the task point P in the route i Center O of the arc i ' coordinates;
when & lt O i P i P i+1 Less than 90 deg., indicating a task point P i Is a convex point, i.e. the center of a circle O i ' coordinate and center of circle O i Coordinate coincidence; when & lt O i P i P i+1 Greater than 90 deg., indicating a task point P i Is a concave point, i.e. the centre of a circle O i ' coordinate and center of circle O i Coordinates in relation to the task point P i Symmetry;
s422: determining the center of a circle O i+1 Seat ofThe mark translates the local coordinate system OXY of the acquired multi-task point to the origin and the task point P i+1 The coincident position is obtained as the center O i+1 The local coordinate system O ' X ' Y ' of the task point P i+1 Center of circle O of i+1 Is expressed using a parametric method, where the parameter θ ∈ [0,2 π);
task point P in coordinate system O 'X' Y i+1 Center of circle O of i+1 Is expressed as using a parametric method
S423: determining tangent point T i And tangent point T i+1 According to the task point P i Task point P i+1 And a center O obtained in S421 i ' and center O obtained in S422 i+1 Can determine the task point P i The tangent point of the arc route and the straight line route is T i And a task point P i+1 Tangent point T of arc route and straight line route i+1 To obtain a task point P i To task point P i+1 Is contained in the optimal course P i T i Straight line route T i T i+1 And a circular arc course T i+1 P i+1 ；
In step S423, according to the task point P i+1 And a task point P i Center of circle O of i Distance between `
The geometrical relationship with the minimum turning radius R is divided into three cases:
in particular, when
Now known task point P i Task point P i Center of circle O i ', task point P i+1 Task point P i+1 Center of circle O i+1 Can find the tangent point T i With two unequal roots T i 1,T i 2, one root is a tangent point T i (ii) a Another unwanted root is T i ′；
To determine the tangent point T i By introducing a control point C i Over two roots T i 1,T i 2 are made parallel to P respectively i P i+1 Can obtain a control point C i To two roots T i 1,T i 2 are respectively d 1 ,d 2 Get d 1 ,d 2 Maximum value of d max The root corresponding to the root is a tangent point T i Coordinates; the tangent point T can be obtained by the same method i+1 Coordinates;
when in use
Center of circle O i ' it is impossible to find a task point P i To task point P i+1 Of course P i P i+1 The center of a circle O is required i ' about task point P i Symmetry is performed. Also for determining the tangent point T i By introducing a control point C i Solving for tangent point T i Coordinate, tangent point T i+1 Coordinate method and
the method in (1) is the same;
when in use
Task point P i And a task point P i+1 While being located at the center of circle O i On the arc of' i.e. centre O i ' with the center O i+1 Coincidence, at which point P of the task i To task point P i+1 Of course P i P i+1 Comprising only circular-arc courses P i P i+1 No tangent point exists;
s424: to prevent the circular arc course P in step S423 i T i And arc course T i+1 P i+1 For major arcs, i.e. circular arc navigations T i+1 P i+1 The line is larger than a semicircle and equally divides the circular arc route intoDetermining two sections of minor arc routes, i.e. arc routes smaller than semicircle i T i Middle point M of i And arc course T i+1 P i+1 Middle point M of i+1 The coordinates of (a);
when in use
At this time, task point P i To task point P i+1 Of course P i P i+1 Comprising only circular-arc courses P i P i+1 Only the arc P needs to be determined i T i Midpoint M of (A) i ；
S425: determining the optimal route, and obtaining the center of circle O i ', center O i+1 Tangent point T i Tangent point T i+1 Middle point M i And midpoint M i+1 Coordinate of (2), available task point P i To task point P i+1 Of course P i P i+1 Is only at the center O i+1 The coordinates are correlated. Task point P i To task point P i+1 Of course P i P i+1 The total distance will follow the center of circle O i+1 The coordinates are changed by changing the task point P i+1 Center of circle O i+1 The coordinate of (2) is changed, namely the value of the parameter theta epsilon [0,2 pi) is changed to obtain the task point P i To task point P i+1 Of course P i P i+1 Determines the task point P i To task point P i+1 The optimal route of (2);
s426: center of circle O i+1 The local coordinate system of (2) is translated to convert the center of the circle O i+1 The local coordinate system O ' X ' Y ' is converted back to the local coordinate system OXY for obtaining the multi-task point, and the task point P in the local coordinate system OXY is obtained i To task point P i+1 Optimal route, mission point P i+1 Center of circle O of i+1 The coordinates of (a);
s427: repeating the steps S421-S426, and sequentially determining the task point P 1 To the meridian P n Every two adjacent task points P between points i And P i+1 The optimal course in between.
Further, connectAnd (4) connecting the optimal route between every two adjacent task points to finally obtain the optimal route for returning the airship from the flying starting point to the landing point through all the task points. As shown in FIG. 4, the multitask point obtained in step S1 is P 1 、P 2 、P 3 、P 4 The optimal flight sequence obtained in step S3 is: p 0 →P 1 →P 4 →P 3 →P 2 →P 0 Then, the following steps are sequentially performed in step S4: p is 0 →P 1 、P 1 →P 4 、P 4 →P 3 、P 3 →P 2 、P 2 →P 0 The optimal course between two mission points. In FIG. 4, P 0 The landing point is the takeoff point and the return landing point of the airship; p 1 、P 2 、P 3 、P 4 Is a selected multi-tasking point; o is 0 、O 1 、O 2 、O 3 、O 4 Are respectively a point P 0 、P 1 、P 2 、P 3 、P 4 P 1 The center of the arc. Wherein, with P 1 →P 4 Optimal course P between two mission points 1 P 4 For example, T 1 For a circular course P 1 T 1 With straight course T 1 T 4 The tangent point of (2); t is 4 For a circular arc course T 4 P 4 With straight course T 1 T 4 The tangent point of (A); m 1 Is a circular arc route P 1 T 1 A midpoint of (a); m 4 For a circular arc course T 4 P 4 The midpoint of (a).
Although the illustrative embodiments of the present invention have been described in order to facilitate those skilled in the art to understand the present invention, it is to be understood that the present invention is not limited to the scope of the embodiments, and that various changes may be made apparent to those skilled in the art as long as they are within the spirit and scope of the present invention as defined and defined in the appended claims, and all matters of the invention using the inventive concepts are protected.
Claims (4)
1. A method for planning multi-task point routes of an airship based on a genetic algorithm and a greedy strategy is characterized by comprising the following steps:
s1: acquiring the minimum flight speed and the maximum turning slope angle parameter information of the airship and the coordinate information of a multi-task point;
s2: calculating to obtain the minimum turning radius of the airship passing through each task point according to the acquired minimum flight speed and maximum turning slope angle parameter information of the airship;
s3: based on the obtained coordinate information of the multi-task points, a global optimal flight sequence of the airship returning to the landing point from the takeoff point through all the task points is obtained by adopting the improved global search method of the genetic algorithm;
s4: on the basis of the overall optimal flight sequence, a route planning method based on a greedy strategy is provided, the overall optimal solution for solving the route planning of the airship at the multiple task points is decomposed into a local optimal solution for solving the route planning between every two adjacent task points by adopting the greedy strategy, and the shortest path between every two adjacent task points in the optimal flight sequence, namely the optimal route, is sequentially obtained by adopting the minimum turning radius of the airship; then, connecting the optimal route between each two adjacent task points, and finally obtaining the optimal route for returning the airship from the departure point to the landing point through all the task points, for example, the optimal flight sequence obtained in S3 is: starting from-3-1-5-6-2-4-descending, and sequentially obtaining the shortest path between the starting-3, the starting-3-1, the starting-5-6, the starting-6-2, the starting-2-4 and the starting-4-descending in the step S4; when the shortest path is calculated in S4, the minimum turning radius of the airship calculated in S2 is used in consideration of the influence of the turning radius.
2. The airship multitask point route planning method based on the genetic algorithm and the greedy strategy as claimed in claim 1, wherein: in step S3, a global search method for an improved genetic algorithm includes the following sub-steps:
s31: setting maximum evolution algebra, cross probability and mutation probability; calculating the distance between any two task points;
specifically, the maximum evolution algebra represents the genetic iteration times of a genetic algorithm, the cross probability represents the probability of cross variation between every two adjacent individuals in a population, and the variation probability represents the probability of variation of each individual in the population;
s32: initializing a population, and randomly generating a plurality of individuals with starting points as flying points;
s33: selecting operation, wherein excellent individuals are inherited to the next generation, the distance between any two task points is calculated according to S31, the total length of a path of each individual is obtained, the adaptive value of each individual in the population is the reciprocal of the total length of the path, the individuals in the population are sorted from large to small according to the adaptive value and divided into three parts, the individuals arranged in the front and having large adaptive value are copied into two parts, the individuals in the middle are copied into one part, the individuals behind and having small adaptive value are not copied, and a new population is formed to replace the original population;
s34: performing cross operation, wherein every two adjacent individuals in the population have certain cross probability, pairing and crossing the excellent individuals selected in the S33 to generate new excellent individuals, firstly sequencing the population by using an adaptive value, and performing cross interchange on partial node sequences with the same length in the two adjacent individuals according to single-point crossing, namely performing single-point crossing between the large adaptive value and the large adaptive value, so that the convergence precision of the algorithm is improved, and the new individual is generated to replace a source individual;
s35: performing mutation operation, namely performing mutation operation on each individual in the population with a certain mutation probability, and performing mutation on the new individuals generated in the S34 in a crossed manner to generate new excellent individuals, wherein the mutation operation specifically comprises the following steps: firstly, calculating an average adaptive value of a population, wherein the individual with the adaptive value larger than the average adaptive value is multiplied by an operator which randomly generates [0,1], the variation probability of a larger adaptive value in the individual is reduced, the individual with the adaptive value smaller than the average adaptive value is subjected to variation according to the variation probability set by S31, then the individual is subjected to partial variation, namely two task points in an individual sequence are randomly selected, the positions of the two task points in the individual sequence are exchanged, and new excellent individuals are obtained through variation;
s36: and repeating the steps S33-S35 until the genetic algebra is the maximum evolution algebra, and outputting the individual with the maximum fitness in the evolution process, namely outputting the optimal flight sequence of the multi-task points.
3. The airship multitask point route planning method based on the genetic algorithm and the greedy strategy as claimed in claim 1, wherein: in step S4, the greedy strategy is adopted to decompose the global optimal solution for solving the airship multi-task point route plan into the local optimal solution for solving the route plan between every two adjacent task points, so that the computation workload is greatly reduced, and the computation efficiency is improved, including the following substeps:
s41: determining an optimal flight path from the starting point to the 1 st task point, wherein the airship takes off in any direction at the starting point, and the optimal flight path from the starting point to the 1 st task point is a straight line flight path from the starting point to the 1 st task point;
s42: determining every two adjacent task points P between the 1 st task point and the nth task point i And P i+1 1,2,3, a, n-1;
s43: determining the optimal route from the nth task point to the landing point, wherein for calculation convenience, the calculation process of the step S43 is the same as that of the step S42; the method specifically comprises the following steps: according to two adjacent task points P at the upper section of the optimal flight sequence i-1 To task point P i Is optimized to the task point P in the route i Center O of the arc i Coordinates are obtained through step S42, and a task point P is obtained i To task point P i+1 Then sequentially obtaining every two adjacent task points P in the optimal flight sequence i 、P i+1 The optimal route between the two task points is finally connected with every two adjacent task points P in sequence i 、P i+1 And obtaining the optimal air route for returning the airship to the landing site from the starting point through all the task points.
4. The method for planning a multi-task route of an airship based on genetic algorithm and greedy strategy according to claim 3, wherein in the step S42, two adjacent task points P are i And P i+1 The optimal route between includes a mission point P i The arc course, the middle straight course and the task point P i+1 The arc route is located, thereby determining two adjacent task points P i And P i+1 The optimal course line between the two needs to determine the circle center O i ', center O i+1 Tangent point T i And tangent point T i+1 Said step S42 includes the following sub-steps:
s421: determining the center of a circle O i The coordinates of the' are two adjacent task points P in the upper section of the optimal flight sequence i-1 To task point P i The optimal route of (2), the available task point P i Center O of the arc i Coordinates by judging from the center O i Task point P i And a task point P i+1 Angle O of i P i P i+1 To determine the task point P i To task point P i+1 Is optimized to the task point P in the route i Center O of the arc i ' coordinates;
when & lt O i P i P i+1 If the angle is less than 90 degrees, the task point P is indicated i Is a convex point, i.e. the center of a circle O i ' coordinate and center of circle O i Coordinate coincidence; when & lt O i P i P i+1 If greater than 90 deg., the task point P is indicated i Is a concave point, i.e. the centre of a circle O i ' coordinate and center of circle O i Coordinates in relation to the task point P i Symmetry;
s422: determining the center of a circle O i+1 Translating the obtained local coordinate system OXY of the multi-task point to the origin and the task point P i+1 The coincident position is obtained as the center O i+1 The local coordinate system O ' X ' Y ' of the task point P i+1 Center of circle O of i+1 Is expressed using a parametric method, wherein the parameter θ ∈ [0,2 π);
s423: determining tangent point T i And tangent point T i+1 According to the task point P i Task point P i+1 And the center O obtained in step S421 i ' and center O obtained in S422 i+1 To determine a task point P i The tangent point of the arc route and the straight line route is T i And a task point P i+1 Tangent point T of arc course and straight course i+1 To obtain a task point P i To task point P i+1 Is contained in the optimal course P i T i Straight line route T i T i+1 And a circular arc course T i+1 P i+1 ；
S424: to prevent the circular arc course P in step S423 i T i And arc course T i+1 P i+1 For major arcs, i.e. circular arc navigations T i+1 P i+1 The line is larger than the semicircle, the circular arc route is equally divided into two sections of minor arc routes, namely the circular arc route smaller than the semicircle, and the circular arc route P is determined i T i Middle point M of i And arc course T i+1 P i+1 Middle point M of i+1 The coordinates of (a);
s425: determining the optimal route, and obtaining the center of circle O i ', center O i+1 Tangent point T i Tangent point T i+1 Middle point M i And midpoint M i+1 To obtain a task point P i To task point P i+1 Of course P i P i+1 The total distance is only at the center of circle O i+1 The coordinates are correlated. Task point P i To task point P i+1 Of course P i P i+1 The total distance will follow the center of circle O i+1 The coordinates are changed by changing the task point P i+1 Center of circle O of i+1 The coordinate of (2) is changed, namely the value of the parameter theta epsilon [0,2 pi) is changed to obtain the task point P i To task point P i+1 Of course P i P i+1 To determine the task point P i To task point P i+1 The optimal route of (2);
s426: center of circle O i+1 The local coordinate system of (2) is translated to convert the center of the circle O i+1 The local coordinate system O ' X ' Y ' is converted back to the local coordinate system OXY for obtaining the multi-task point, and the task point P in the local coordinate system OXY is obtained i To task point P i+1 Optimal route, mission point P i+1 Center of circle O of i+1 The coordinates of (a);
s427: repeating the steps S421-S426, and sequentially determining the task point P 1 To the meridian P n Every two adjacent task points P between points i And P i+1 The optimal course in between.
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