CN107169608B - Distribution method and device for multiple unmanned aerial vehicles to execute multiple tasks - Google Patents

Distribution method and device for multiple unmanned aerial vehicles to execute multiple tasks Download PDF

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CN107169608B
CN107169608B CN201710389675.3A CN201710389675A CN107169608B CN 107169608 B CN107169608 B CN 107169608B CN 201710389675 A CN201710389675 A CN 201710389675A CN 107169608 B CN107169608 B CN 107169608B
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unmanned aerial
aerial vehicle
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罗贺
梁峥峥
胡笑旋
朱默宁
王国强
马华伟
靳鹏
夏维
牛艳秋
方向
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Hefei University of Technology
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Abstract

The embodiment of the invention discloses a method and a device for distributing multiple tasks executed by multiple unmanned aerial vehicles. The method comprises the following steps: acquiring position information of a plurality of unmanned aerial vehicles and target points and motion parameters of the unmanned aerial vehicles and a wind field; constructing an initial population according to the position information and a preset genetic algorithm, wherein each chromosome in the initial population comprises European flight paths of the number of the unmanned aerial vehicles; determining the flight state of the unmanned aerial vehicle and the flight time for completing the European-type path flight path segment according to the initial population and the motion parameters, and acquiring the task completion time of all unmanned aerial vehicles corresponding to each chromosome according to the flight time and an MUAV-VS-EVRP model; and (3) crossing and mutating chromosomes in the population based on a genetic algorithm, and selecting the chromosome with the shortest task completion time of the unmanned aerial vehicle as a task allocation scheme of the unmanned aerial vehicle after a preset iteration number is reached. The embodiment of the invention combines the unmanned aerial vehicle flight path planning problem with the actual flight environment of the unmanned aerial vehicle, so that the optimal flight path scheme obtained by planning is superior to the scheme of constant speed of the unmanned aerial vehicle.

Description

Distribution method and device for multiple unmanned aerial vehicles to execute multiple tasks
Technical Field
The embodiment of the invention relates to the technical field of unmanned aerial vehicles, in particular to a method and a device for distributing multiple tasks executed by multiple unmanned aerial vehicles.
Background
Currently, unmanned Aerial vehicles (uavs) (unmanned Aerial vehicles) are widely used in the military and civilian fields, and can complete various tasks such as target reconnaissance, target tracking, information collection, rescue after earthquake, geological exploration and the like. For example, when multiple UAVs cooperatively detect targets, each UAV must be optimally assigned the target to be detected and planned with an optimal flight path. The problem is a task allocation and flight path planning joint optimization problem which is constrained by multiple factors and is also a non-deterministic problem.
With the research of UAV, environmental factors are gradually included in the research of the problem, especially in the problems of UAV mission allocation, flight path planning, flight control, etc., and how to reduce energy consumption, control the flight state of UAV so that the UAV consumes the least fuel to perform the most mission, has a better mission performing state and higher safety under the influence of the environmental factors is the main work of the current UAV research. The models currently used to solve the UAV mission allocation and mission planning problem are: a TSP model, a TOP model and a VRP model, wherein the TSP model is a model that minimizes the path cost of a traveler after passing all given target points under the condition of only a single traveler; the TOP model is a model that, in the presence of multiple members, allows each member to visit as many target points as possible, thereby maximizing the total profit for all members; the VRP model is a model that, under the condition that the number of vehicles is fixed, the vehicles visit a certain number of target points, and each target point can only be visited once in the process, so that the total distance or total time of UAV navigation is finally minimized.
In the process of implementing the embodiment of the present invention, the inventor finds that in actual operation, it is generally assumed that the speed of the drone is constant in a constant time in the model. However, this assumption is obviously unrealistic, so that the model cannot accurately simulate the actual motion state of the drone, and thus cannot perform optimal track planning.
Disclosure of Invention
One purpose of the embodiment of the invention is to solve the problem that in the prior art, because the speed of the unmanned aerial vehicle is set to be constant during flight path planning, a model cannot accurately simulate the actual motion state of the unmanned aerial vehicle, and further, optimal flight path planning cannot be given.
The embodiment of the invention provides a method for distributing multiple tasks executed by multiple unmanned aerial vehicles, which comprises the following steps:
s1, acquiring position information of a plurality of unmanned aerial vehicles and a plurality of target points, and motion parameters of the unmanned aerial vehicles and a wind field;
s2, constructing an initial population according to the position information of the unmanned aerial vehicles and the target points and a preset genetic algorithm, wherein each chromosome in the initial population comprises European flight paths of the number of the unmanned aerial vehicles, and each European flight path is completed by different unmanned aerial vehicles;
s3, determining the flight state of the unmanned aerial vehicle and the flight time of the unmanned aerial vehicle for completing the track section of the European flight path according to the initial population, the unmanned aerial vehicle and the motion parameters of the wind field, and acquiring the task completion time of all the unmanned aerial vehicles corresponding to the chromosomes in the initial population according to the flight time of the track section and the MUAV-VS-EVRP model;
and S4, performing crossover and mutation treatment on chromosomes in the initial population based on a genetic algorithm, and selecting the chromosome with the shortest task completion time of all unmanned aerial vehicles as the optimal task allocation scheme of the unmanned aerial vehicles after the predetermined iteration times are reached.
Optionally, the constructing an initial population according to the position information of the multiple unmanned aerial vehicles and the multiple target points and a preset genetic algorithm includes:
carrying out chromosome coding according to a coding mode of a preset genetic algorithm to generate an initial population with a preset scale; the chromosome is composed of target point information and unmanned aerial vehicle information; wherein the target point belongs to a set
Figure GDA0002390600170000021
T0Denotes the start of UAVs, NTRepresenting the number of target points to which the drone belongs
Figure GDA0002390600170000022
NURepresenting the number of unmanned aerial vehicles;
the first behavior of the chromosome is the random full arrangement of the target points, the second behavior randomly selects the corresponding unmanned aerial vehicle for each target point according to the unmanned aerial vehicle set, and all the unmanned aerial vehicles in the unmanned aerial vehicle set are ensured to be selected at least once.
Optionally, determining a flight state of the unmanned aerial vehicle and a flight time of the unmanned aerial vehicle for completing a flight path segment of a european flight path according to the initial population, the unmanned aerial vehicle and the motion parameters of the wind field, and obtaining task completion time of all unmanned aerial vehicles corresponding to chromosomes in the initial population according to the flight time of the flight path segment and the MUAV-VS-EVRP model includes:
dividing the European flight path corresponding to each chromosome into a plurality of flight path segments according to the target point visited sequence of the European flight path;
determining the flight state of the unmanned aerial vehicle by combining wind field parameters according to the coordinates of the starting point and the coordinates of the ending point corresponding to each track section, and further acquiring the task completion time of all unmanned aerial vehicles of the unmanned aerial vehicle completing the track section;
and acquiring the task completion time of all unmanned aerial vehicles corresponding to the chromosomes according to the corresponding flight time of each track section.
Optionally, determining the flight state of the unmanned aerial vehicle by combining the wind field parameters according to the coordinates of the starting point and the coordinates of the ending point corresponding to each track segment, and further acquiring the flight time of the unmanned aerial vehicle for completing the track segment includes:
calculating and acquiring unmanned aerial vehicle U by adopting the following formulaiFrom target point TjFly to target point TkFlight time of the track segment:
Figure GDA0002390600170000031
wherein, UiRepresenting the unmanned aerial vehicle performing the above tasks, U representing the set of unmanned aerial vehicles, TjAs a starting point, the position of the probe,Tkto end point, T represents the set of target points, Vg iFor unmanned plane UiThe ground speed between the two target points;
calculating and acquiring unmanned aerial vehicle U by adopting the following formulaiThe ground speed of (2):
Figure GDA0002390600170000032
wherein, Va iRepresenting the magnitude of space velocity, βa iRepresenting airspeed heading angle, Vg iIndicating the magnitude of the ground speed, βg iWhich represents the heading angle of the ground speed,
Figure GDA0002390600170000033
the size of the wind speed is shown,
Figure GDA0002390600170000034
represents the wind direction;
calculating and acquiring unmanned aerial vehicle U by adopting the following formulaiAt TjAnd TkEuclidean distance between two points:
Figure GDA0002390600170000035
wherein, X and Y respectively represent the horizontal and vertical coordinates of the corresponding target point.
Optionally, obtaining task completion time of all unmanned aerial vehicles corresponding to chromosomes in the initial population according to the flight time of the flight path segment and the MUAV-VS-EVRP model includes:
acquiring the navigation time according to the MUAV-VS-EVRP model:
Figure GDA0002390600170000041
the constraint conditions are as follows:
Figure GDA0002390600170000042
Figure GDA0002390600170000043
Figure GDA0002390600170000044
wherein.
Figure GDA0002390600170000045
Representing unmanned aerial vehicle by target point TjFly to target point TkThe time of flight of the vehicle,
Figure GDA0002390600170000046
is a binary decision variable, and
Figure GDA0002390600170000047
when UAVUiWarp beam TjFly to TkWhen it is, then
Figure GDA0002390600170000048
Is 1, otherwise
Figure GDA0002390600170000049
Has a value of 0, NTRepresenting the number of target points, NURepresenting the number of drones.
Optionally, based on a genetic algorithm, performing intersection and mutation processing on chromosomes in the initial population, and after a predetermined number of iterations is reached, selecting a chromosome with the shortest task completion time of all the unmanned aerial vehicles as an optimal task allocation scheme for the unmanned aerial vehicles includes:
step 1, generating an initial solution by using the coding mode, generating an initial population with a preset scale, and calculating the fitness of the initial population according to the task completion time of all unmanned aerial vehicles corresponding to each chromosome in the population;
step 2, selecting two individuals (A, B) in a parent population to intersect by using a roulette method, wherein the intersection rule is that the intersection position in the individual A is randomly selected, then the first line of genes in the intersection position of the individual B, which is the same as the intersection position of the individual A, is searched, the intersection position genes in the chromosomes A and B are replaced to obtain new chromosomes C and D, whether the chromosomes C and D meet the constraint condition of the MUAV-VS-EVRP model is judged, if yes, the chromosomes A and B in the population are replaced by the chromosomes C and D, otherwise, the chromosomes which do not meet the constraint condition are subjected to constraint check, namely, when the number of unmanned aerial vehicles in the chromosomes A and B does not meet the constraint condition, one gene position is randomly selected and whether two or more unmanned aerial vehicle codes on the gene position exist or not is judged, if yes, the missing unmanned aerial vehicle codes are put into the gene position, otherwise, reselecting the gene position, generating chromosomes A and B in a chromosome replacement population meeting constraint conditions, and then continuously iteratively updating the population in the step 1 to obtain a new offspring population;
and 3, selecting one chromosome in the population in the step 2 for mutation by using a roulette method, wherein the method for mutating the chromosome is at least one of the following mutation methods, and the method comprises the following steps: performing target point variation on the first line of the chromosome; carrying out unmanned aerial vehicle variation on the second chromosome line;
the whole chromosome variation steps include: firstly, if the first line of the chromosome is mutated, randomly selecting two gene positions of the current chromosome and exchanging target point codes of the corresponding gene positions; selecting whether a second row is mutated or not and a mutation position, if so, randomly generating a value of the unmanned aerial vehicle code which is mutated and is different from the current position to replace an original value, judging whether the chromosome meets the constraint condition of the MUAV-VS-EVRP model or not, if so, replacing the chromosome in the population, otherwise, carrying out constraint check on the chromosome which does not meet the constraint condition, namely, randomly selecting a gene position and judging whether two or more unmanned aerial vehicle codes on the gene position exist or not aiming at the chromosome which does not meet the constraint condition when the number of the unmanned aerial vehicles in the chromosome is not met with the constraint condition, if so, putting the missing unmanned aerial vehicle code into the gene position, otherwise, reselecting the gene position, generating the chromosome which meets the constraint condition to replace the chromosome in the population, and continuously iterating and updating the population in the step 2 to obtain a new offspring population;
step 4, calculating the population fitness of the offspring and selecting the optimal solution of all solutions in the iteration;
step 5, judging whether the current iteration times reach a preset value or not, if not, combining the child population and the parent population in the step 3 according to a certain proportion to form a new parent population, and returning to the step 2; if so, ending the iteration, and taking the finally obtained optimal solution as a task allocation result of the unmanned aerial vehicle.
The embodiment of the invention provides a distribution device for multiple unmanned aerial vehicles to execute multiple tasks, which comprises:
the acquisition module is used for acquiring the position information of a plurality of unmanned aerial vehicles and a plurality of target points, and the motion parameters of the unmanned aerial vehicles and the wind field;
the first processing module is used for constructing an initial population according to the position information of the unmanned aerial vehicles and the target points and a preset genetic algorithm, and each chromosome in the initial population comprises European flight paths of the number of the unmanned aerial vehicles, and each European flight path is completed by different unmanned aerial vehicles;
the second processing module is used for determining the flight state of the unmanned aerial vehicle and the flight time of a track section of a European flight path of the unmanned aerial vehicle according to the initial population, the unmanned aerial vehicle and the motion parameters of a wind field, and acquiring the task completion time of all unmanned aerial vehicles corresponding to chromosomes in the initial population according to the flight time of the track section and an MUAV-VS-EVRP model;
and the third processing module is used for carrying out crossing and mutation processing on chromosomes in the initial population based on a genetic algorithm, and selecting the chromosome with the shortest task completion time of all the unmanned aerial vehicles as the optimal task allocation scheme of the unmanned aerial vehicles after the preset iteration times are reached.
Optionally, the first processing module is configured to perform chromosome coding according to a coding mode of a preset genetic algorithm to generate an initial population of a predetermined scale; the chromosome is composed of target point information and unmanned aerial vehicle information(ii) a Wherein the target point belongs to a set
Figure GDA0002390600170000061
T0Denotes the start of UAVs, NTRepresenting the number of target points to which the drone belongs
Figure GDA0002390600170000062
NURepresenting the number of unmanned aerial vehicles; the first behavior of the chromosome is the random full arrangement of the target points, the second behavior randomly selects the corresponding unmanned aerial vehicle for each target point according to the unmanned aerial vehicle set, and all the unmanned aerial vehicles in the unmanned aerial vehicle set are ensured to be selected at least once.
Optionally, the second processing module is configured to divide the european style flight path corresponding to each chromosome into a plurality of flight path segments according to the target point visited sequence of the european style flight path; determining the flight state of the unmanned aerial vehicle by combining wind field parameters according to the coordinates of the starting point and the coordinates of the ending point corresponding to each track section, and further acquiring the flight time of the unmanned aerial vehicle for completing the track section; and acquiring task completion time of all unmanned aerial vehicles corresponding to the chromosomes according to the corresponding flight time of each track section.
Optionally, the second processing module is configured to determine a flight state of the unmanned aerial vehicle according to coordinates of a start point and coordinates of a stop point corresponding to each track segment in combination with the wind field parameter, and further acquire a flight time for the unmanned aerial vehicle to complete the track segment, where the flight time includes:
calculating and acquiring unmanned aerial vehicle U by adopting the following formulaiFrom target point TjFly to target point TkFlight time of the track segment:
Figure GDA0002390600170000063
wherein, UiRepresenting the unmanned aerial vehicle performing the above tasks, U representing the set of unmanned aerial vehicles, TjAs a starting point, TkTo end point, T represents the set of target points, Vg iFor unmanned plane UiGround speed between the two target points；
Calculating and acquiring unmanned aerial vehicle U by adopting the following formulaiThe ground speed of (2):
Figure GDA0002390600170000071
wherein, Va iRepresenting the magnitude of space velocity, βa iRepresenting airspeed heading angle, Vg iIndicating the magnitude of the ground speed, βg iWhich represents the heading angle of the ground speed,
Figure GDA0002390600170000072
the size of the wind speed is shown,
Figure GDA0002390600170000073
represents the wind direction;
calculating and acquiring unmanned aerial vehicle U by adopting the following formulaiAt TjAnd TkEuclidean distance between two points:
Figure GDA0002390600170000074
wherein, X and Y respectively represent the horizontal and vertical coordinates of the corresponding target point.
According to the technical scheme, the method and the device for planning the flight path of the unmanned aerial vehicle accessing the multiple target points firstly analyze the wind field and the motion parameters of the unmanned aerial vehicle to obtain the actual flight state of the unmanned aerial vehicle in the wind field, and plan the flight path based on the actual flight state.
Drawings
The features and advantages of the present invention will be more clearly understood by reference to the accompanying drawings, which are illustrative and not to be construed as limiting the invention in any way, and in which:
fig. 1 is a schematic flow chart illustrating a method for allocating multiple drones to perform multiple tasks according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart illustrating a process for calculating the voyage time of the Dubins flight path according to an embodiment of the present invention;
FIG. 3 is a flow chart diagram illustrating a genetic algorithm provided by an embodiment of the present invention;
FIGS. 4 a-4 c show diagrams of operators in a genetic algorithm provided by an embodiment of the present invention;
FIG. 5 is a schematic diagram illustrating wind directions provided by an embodiment of the present invention;
FIG. 6 is a schematic diagram illustrating a velocity vector relationship provided by an embodiment of the present invention;
FIG. 7 is a schematic diagram illustrating an analysis of wind field effects on a UAV flying from A to C according to an embodiment of the present invention;
FIG. 8 illustrates a schematic diagram of segmenting a flight path provided by an embodiment of the present invention;
fig. 9 is a schematic structural diagram illustrating an allocation apparatus for multiple drones to perform multiple tasks according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
Fig. 1 shows a schematic flow chart of a flight path planning method for multiple drones to access multiple target points, where, referring to fig. 1, the method may be implemented by a processor, and specifically includes the following steps:
110. acquiring position information of a plurality of unmanned aerial vehicles and a plurality of target points, and motion parameters of the unmanned aerial vehicles and a wind field;
it should be noted that, before the task allocation and the flight path planning are performed, the technician may set or actually measure the position information of the drone and the target points, and then input the position information into the processor.
In addition, the motion parameter of unmanned aerial vehicle can be that the technical staff sets for according to actual flight needs, and the motion parameter of wind field can be that the technical staff measures to draw or according to actual conditions setting.
120. According to the position information of the unmanned aerial vehicles and the target points and a preset genetic algorithm, an initial population is constructed, each chromosome in the initial population comprises European flight paths of the number of the unmanned aerial vehicles, and each European flight path is completed by different unmanned aerial vehicles;
130. determining the flight state of the unmanned aerial vehicle and the flight time of the unmanned aerial vehicle for completing the flight path of the European flight path according to the initial population, the unmanned aerial vehicle and the motion parameters of the wind field, and acquiring the task completion time of all unmanned aerial vehicles corresponding to chromosomes in the initial population according to the flight time of the flight path and an MUAV-VS-EVRP model;
140. and performing crossing and mutation treatment on chromosomes in the initial population based on a genetic algorithm, and selecting the chromosome with the shortest task completion time of all the unmanned aerial vehicles as the optimal task allocation scheme of the unmanned aerial vehicles after the preset iteration times are reached.
It will be appreciated that each iteration of intersection, mutation, and the like may have new individuals present, and then the calculation of the voyage time for the new chromosome is based on step 130, so that each european flight path corresponds to one voyage time.
It can be seen that, this embodiment firstly analyzes the wind field and the motion parameters of the unmanned aerial vehicle to obtain the actual flight state of the unmanned aerial vehicle in the wind field, and then plans the flight path based on the actual flight state.
The following is a detailed description of the steps in the examples of the present invention:
first, step 120 is explained in detail:
carrying out chromosome coding according to a coding mode of a preset genetic algorithm to generate an initial population with a preset scale; the chromosome is composed of target point information and unmanned aerial vehicle information; wherein the target point belongs to a set
Figure GDA0002390600170000092
T0Denotes the start of UAVs, NTRepresenting the number of target points to which the drone belongs
Figure GDA0002390600170000091
NURepresenting the number of unmanned aerial vehicles;
the first behavior of the chromosome is the random full arrangement of the target points, the second behavior randomly selects the corresponding unmanned aerial vehicle for each target point according to the unmanned aerial vehicle set, and all the unmanned aerial vehicles in the unmanned aerial vehicle set are ensured to be selected at least once.
Then, referring to fig. 2, step 130 is explained in detail below:
210. dividing the European flight path corresponding to each chromosome into a plurality of flight path segments according to the target point visited sequence of the European flight path;
220. determining the flight state of the unmanned aerial vehicle by combining wind field parameters according to the coordinates of the starting point and the coordinates of the ending point corresponding to each track section, and further acquiring the flight time of the unmanned aerial vehicle for completing the track section;
230. and acquiring the task completion time of all unmanned aerial vehicles corresponding to the chromosomes according to the corresponding flight time of each track section.
Wherein step 220 comprises:
calculating and acquiring unmanned aerial vehicle U by adopting the following formulaiFrom target point TjFly to target point TkFlight time of the track segment:
Figure GDA0002390600170000101
wherein, UiRepresenting the unmanned aerial vehicle performing the above tasks, U representing the set of unmanned aerial vehicles, TjAs a starting point, TkTo end point, T represents the set of target points, Vg iFor unmanned plane UiThe ground speed between the two target points;
calculating and acquiring unmanned aerial vehicle U by adopting the following formulaiThe ground speed of (2):
Figure GDA0002390600170000102
wherein, Va iThe size of the space velocity is shown,
Figure GDA0002390600170000103
representing airspeed heading angle, Vg iIndicating the magnitude of the ground speed, βg iWhich represents the heading angle of the ground speed,
Figure GDA0002390600170000104
the size of the wind speed is shown,
Figure GDA0002390600170000105
represents the wind direction;
calculating and acquiring unmanned aerial vehicle U by adopting the following formulaiAt TjAnd TkEuclidean distance between two points:
Figure GDA0002390600170000106
wherein, X and Y respectively represent the horizontal and vertical coordinates of the corresponding target point.
In addition, the step of calculating the task completion time of the unmanned aerial vehicle corresponding to the chromosome in the initial population comprises the following steps:
acquiring the task completion time of the unmanned aerial vehicle according to the MUAV-VS-EVRP model:
Figure GDA0002390600170000107
the constraint conditions are as follows:
Figure GDA0002390600170000108
Figure GDA0002390600170000109
Figure GDA00023906001700001010
wherein.
Figure GDA00023906001700001011
Representing unmanned aerial vehicle by target point TjFly to target point TkThe time of flight of the vehicle,
Figure GDA00023906001700001012
is a binary decision variable, and
Figure GDA00023906001700001013
when UAVUiWarp beam TjFly to TkWhen it is, then
Figure GDA00023906001700001014
Is 1, otherwise
Figure GDA00023906001700001015
Has a value of 0, NTRepresenting the number of target points, NURepresenting the number of drones.
Step 140 is described in detail below:
step 1, generating an initial solution by using the coding mode, generating an initial population of a preset scale, and calculating the fitness of the initial population according to the task completion time of the unmanned aerial vehicle corresponding to each chromosome in the population;
step 2, selecting two individuals (A, B) in a parent population to cross by using a roulette method, wherein the cross rule is that the cross position in the individual A is randomly selected, then the gene in the individual B, which is the same as the first line of the cross position of the individual A, is searched, the cross position gene in the chromosomes A and B is replaced to obtain new chromosomes C and D, whether the chromosomes C and D meet the constraint condition of the MUAV-VS-EVRP model or not is judged, if yes, the chromosomes A and B in the population are replaced by the chromosomes C and D, otherwise, the chromosomes which do not meet the constraint condition are subjected to constraint verification to generate chromosomes which meet the constraint condition to replace the chromosomes A and B in the population, and then the population in the step 1 is continuously updated in an iterative manner to obtain a new offspring population;
and 3, selecting one chromosome in the population in the step 2 for mutation by using a roulette method, wherein the method for mutating the chromosome is at least one of the following mutation methods, and the method comprises the following steps: performing target point variation on the first line of the chromosome; carrying out unmanned aerial vehicle variation on the second chromosome line;
the whole chromosome variation steps include: firstly, if the first line of the chromosome is mutated, randomly selecting two gene positions of the current chromosome and exchanging target point codes of the corresponding gene positions; selecting whether a second row is mutated or not and a mutation position, if so, randomly generating a value of the unmanned aerial vehicle code which is different from the current position to replace the original value, judging whether the chromosome meets the constraint condition of the MUAV-VS-EVRP model or not after mutation, if so, replacing the chromosome in the population, otherwise, carrying out constraint check on the chromosome which does not meet the constraint condition, generating the chromosome which meets the constraint condition to replace the chromosome in the population, and continuously iterating and updating the population in the step 2 to obtain a new offspring population;
step 4, calculating the population fitness of the offspring and selecting the optimal solution of all solutions in the iteration;
step 5, judging whether the current iteration times reach a preset value or not, if not, combining the child population and the parent population in the step 3 according to a certain proportion to form a new parent population, and returning to the step 2; if so, ending the iteration, and taking the finally obtained optimal solution as a task allocation result of the unmanned aerial vehicle.
The principle of the genetic algorithm employed in the present invention will be described in detail below with reference to FIG. 3:
1. opening;
2. generating a population comprising a specified number of chromosomes, based on a technician's settings, the specified number may be specifically 100;
as shown in fig. 4a, chromosome a represents a possible solution for two unmanned aerial vehicles UAV to visit three target points in a stable wind field, i.e. UAV starts from starting point S (0,0), returns after visiting target point 3 and target point 1 in sequence, UAV starts from starting point S (0,0), returns after visiting target point 2. The second row in the code represents the code for the UAV to access the corresponding target point.
Wherein, chromosome A includes two european style flight paths and each european style flight path is accomplished by different unmanned aerial vehicle number one and No. two.
3. Calculating fitness of each chromosome;
it should be noted that, with the calculation method in step 140 in the corresponding embodiment of fig. 1, the flight time of the unmanned aerial vehicle to complete each european flight path is calculated, and the fitness of the chromosome is calculated based on the task time of the unmanned aerial vehicle, for example: the time for the unmanned aerial vehicle to complete the task is in inverse proportion to the fitness.
It should be understood that, after a specified number of populations are generated according to the encoding method in step 2, the fitness is calculated, and in the present invention, the calculation of the fitness is based on an objective function, and the calculation process is as follows:
Figure GDA0002390600170000121
4. selection operation
The selection operation is performed by roulette according to J'.
5. Crossover operation
By crossing the chromosomes of the parent generation, the superior genes in the parent generation can be inherited to obtain the superior offspring. Aiming at the MUAV-VS-EVRP problem, a single-point mapping method is adopted aiming at the current coding mode, namely, the crossed gene position of the parent chromosome A is randomly generated, the gene position corresponding to the same target point is found in the parent chromosome B, the child chromosome A, B is generated by crossing, and the constraint condition check is carried out on the child chromosome A, B.
Referring to fig. 4B, Parent agent a and agent B exist, the gene position randomly generated on agent a for crossing is 3, the gene position corresponding to the same target point on agent B is found, daughter chromosomes OffSpring a and OffSpring B are generated after crossing, constraint verification is performed on OffSpring a and OffSpring B, it is found that the unmanned aerial vehicle codes of OffSpring a all represent the same unmanned aerial vehicle and do not meet constraint conditions, therefore, mapping and crossing are performed on the randomly generated gene position of the unmanned aerial vehicle code of OffSpring a and the 3 rd gene position of agent a again, and OffSpring a meets the constraint conditions after crossing.
6. Mutation operation
The mutation is to prevent the genetic algorithm from falling into local optima. For the genetic algorithm for solving the sUav-DVS-VRP model, there are two cases of chromosomal variation: target point coding variation and course angle coding variation. Depending on the mutation probability, multiple mutations may or may not occur in the chromosome. The method meets the constraint that each target point in the model is accessed only once, ensures the feasibility of the sub-chromosomes, and adopts uniform variation for course angle coding.
Example mutation operations:
as shown in fig. 4c, Parent a exists, and performs target point mutation and unmanned aerial vehicle mutation on Parent a, first determines whether two types of mutations occur before performing mutation, randomly selects the coding gene positions when determining that the target point mutation occurs, where the selected gene positions are 1 and 3, and then exchanges the target values on the selected gene positions to obtain a new target point access sequence; and when the unmanned aerial vehicle mutation is judged to occur, randomly selecting a mutation gene position, wherein the selected gene position is 3, randomly generating an unmanned aerial vehicle code different from the current unmanned aerial vehicle code to replace the current value, and obtaining a new Parent A. And (3) carrying out constraint condition verification on the Parent A, finding that the unmanned aerial vehicle codes of the Parent A all represent the same unmanned aerial vehicle and are not in accordance with the constraint condition, carrying out mutation operation on the unmanned aerial vehicle codes of the Parent A again, selecting a mutation gene position of 2, randomly generating an unmanned aerial vehicle code different from the current unmanned aerial vehicle code to replace the current value, and obtaining that Offspring A meets the constraint condition.
7. Update operations
8. Selecting an optimal distribution scheme
9. Determine whether to terminate
10. Obtaining an optimal allocation scheme
11. End up
It should be noted that the above steps correspond to some of the steps in the embodiment corresponding to fig. 1, and therefore, the details of the similarities are not repeated here, and please specifically refer to the relevant contents in the embodiment corresponding to fig. 1.
The design principle of the present invention is explained in detail below with reference to the above genetic algorithm:
step one, in order to avoid the problem that the problem is too complex, the method adopts a regional fixed wind field to carry out wind field modeling, namely the wind speed and the wind direction of the wind field are unchanged in a specified region.
The wind field conditions for a known area may be expressed as:
Figure GDA0002390600170000141
wherein, VwRepresenting the wind speed in the wind park, βwIndicating the wind direction.
Wind speed VwThe unit is m/s which is the moving distance of wind relative to the ground in unit time and the wind direction βwIt refers to the direction from which wind blows, the unit of measurement of wind direction is generally expressed by azimuth, for example, on land, generally expressed by 16 azimuth, generally expressed by 36 azimuth on sea, and expressed by angle at high altitude, i.e. dividing the circumference into 360 degrees, where it is specified that westwind (W) is 0 degrees (i.e. 360 degrees), southeast wind (S) is 90 degrees, east wind (E) is 180 degrees, and northwest wind (N) is 270 degrees, as shown in fig. 5.
Step two, configuring the UAV
By using
Figure GDA0002390600170000142
Representing a quadrotor mhuav, the configuration of the mhuav in the air is defined as:
q＝(x,y,βg) (4)
wherein the content of the first and second substances,
Figure GDA0002390600170000143
Figure GDA0002390600170000144
wherein the content of the first and second substances,
Figure GDA0002390600170000145
and
Figure GDA0002390600170000146
representing the coordinates of a UAV in a cartesian inertial reference system; vgGround speed β representing UAVgRefers to the heading angle of the mqua.
To simplify the problem, the following assumptions are presented herein regarding the motion constraints that a UAV needs to satisfy during performance of a mission:
(1) the mUAVs are all isomorphic multi-rotor UAVs;
(2) considering the mUAV to have an obstacle avoidance function without considering the collision of the mUAV;
(3) consider that the mquavs are all flying at a fixed altitude;
(4) according to the flight envelope of the mUAV, there are upper and lower bounds on the flight speed of the mUAV at a given altitude, i.e., the flight speed of the mUAV at a fixed load
Figure GDA0002390600170000151
Va_minAnd Va_maxRespectively representing the minimum and maximum values of the mhuav airspeed at a certain altitude;
(5) the multi-frame mUAVs start from a starting point and return to the starting point after executing a finished task.
Step three, calculating the actual flight state of the UAV
The actual speed of the UAV considering the wind influence is defined as the ground speed of the UAV with the size VgAt this time, the heading angle of the UAV is βgUAV ground speed vector
Figure GDA0002390600170000152
Defining the UAV theoretical velocity without considering wind influence as the airspeed of UAV with magnitude VaAt this time, the heading angle of the UAV is βaUAV airspeed vector
Figure GDA0002390600170000153
UAV airspeed
Figure GDA0002390600170000154
Ground speed
Figure GDA0002390600170000155
Speed of wind in wind field
Figure GDA0002390600170000156
The vector relationship of (a) is shown in fig. 6.
The relationship between the speed and the angle is as follows:
Figure GDA0002390600170000157
in the absence of wind, the wind is forced to flow,
Figure GDA0002390600170000158
i.e. the UAV airspeed is equal to the ground speed.
The following example is illustrated in connection with fig. 7:
the UAV flies from S (0,0) to a (50,300) at an airspeed of 8m/S, is in a wind speed of 5m/S and a wind direction of south wind (Vw 5m/S, β w 90 °), and the airspeed and the ground speed of UAV in the process are obtained according to equation (7) as shown in table 4-1.
TABLE 4-1 airspeed and ground speed comparison table for four-rotor Uav in windless and south wind environments
Airspeed Ground speed
Environment of south wind 28.80km/h,35.5° 23.49km/h,80.5°
Windless environment 28.80km/h,80.5° 28.80km/h,80.5°
Step four, target point configuration
NTThe set of individual target points may be represented as:
Figure GDA0002390600170000159
wherein the positions and the task volumes of all target points in the set are known. In the invention, there may be different types of tasks that need to be executed by the UAV at each target point, and each UAV may only execute one task at one target point in the process, that is, each target point needs to be visited by a different UAV, and each UAV may only visit a certain target point once.
Step five, calculating the navigation time
In the problem of UAV task allocation and flight path planning by taking flight time as a target, the UAV task allocation scheme determines the sequence of UAV access target points, flight path planning is carried out according to the UAV target point access sequence, UAV flight time is calculated according to the result of the flight path planning, and then whether the current UAV task allocation and flight path planning scheme is superior to the known scheme or not is determined according to the UAV flight time.
Because the navigation track of Uav between two points is the Euclidean distance, the navigation direction of Uav between the two points is fixed, and further the ground speed of Uav under a fixed wind field between the two points is not changed, but the ground speed of Uav under the fixed wind field is changed in a plurality of target point scenes from a target point, and the flight time of Uav is calculated as follows:
Figure GDA0002390600170000161
wherein the content of the first and second substances,
Figure GDA0002390600170000162
denotes Uav at TjAnd TkEuclidean distance between two points, Uav ground speed between two points
Figure GDA0002390600170000163
The result is obtained from equation (7).
Thus, the task completion time of the mhuav may be calculated according to equation (8).
Figure GDA0002390600170000164
Wherein the content of the first and second substances,
Figure GDA0002390600170000165
denotes UAVUiAt Tj、TkThe time of flight of the two points;
Figure GDA0002390600170000166
Figure GDA0002390600170000167
is a binary decision variable, and
Figure GDA0002390600170000168
when UAVUiWarp beam TjFly to TkWhen it is, then
Figure GDA0002390600170000169
Is 1, otherwise
Figure GDA00023906001700001610
Is 0;
taking 0 for J and k in J indicates that the UAV starts from the starting point or the path end points to the starting point.
In the solving process, the following constraint conditions are also required to be met:
Figure GDA0002390600170000171
the above conditions ensure that all destinations can be visited and only once.
Figure GDA0002390600170000172
The above conditions guarantee that UAV routes of UAV numbers are launched from the starting point and UAV paths of UAV numbers point to the same point.
Figure GDA0002390600170000173
The above conditions guarantee that there are routes of UAV number and the path of each UAV is a closed loop based on other constraints, that is, the navigation trajectory of UAV is an ordered route and finally returns to the starting point.
Therefore, the task completion time corresponding to each chromosome can be obtained based on the above formula, and the task allocation scheme with the shortest task completion time is selected from the task completion times.
The following detailed description of specific examples of the invention:
first, all simulation experiments were run in the environment of MatlabR2014a on 4G memory, 3.4ghz cpu hardware. The concrete description is as follows:
the UAV model is based on a mathematical model of the UAV, the airspeed of the UAV is 8m/S, two UAVs take off from a starting point S (0,0), and return to the point S (0,0) after the access task is completed; the wind field environment is fixed wind field, and wind speed and wind direction are unchangeable promptly in an experimental process to in order to guarantee that the UAV can fly safely, the wind speed size is 5 meters per second, and the wind direction is got east, 180 promptly, and the three target point coordinates that the UAV need visit are respectively: a (100,300), B (200,150), C (350,50), D (500, 150), E (650,100), F (400, 200), G (50, 250), H (250, 350) and I (50, 450).
According to the model and the algorithm provided by the invention, experiments are carried out in the east wind field environment and the test scene, and the task allocation with the shortest task completion time of the unmanned aerial vehicle in each wind field environment is obtained as shown in the table 3-1 (see fig. 8).
Figure GDA0002390600170000181
Method embodiments are described as a series of acts or combinations for simplicity of explanation, but it should be understood by those skilled in the art that the present invention is not limited by the order of acts or acts described, as some steps may occur in other orders or concurrently with other steps in accordance with the embodiments of the invention. Furthermore, those skilled in the art will appreciate that the embodiments described in the specification are presently preferred and that no particular act is required to implement the invention.
Fig. 9 is a schematic structural diagram of a flight path planning apparatus for a drone to access multiple target points, where referring to fig. 9, the apparatus includes: an obtaining module 101, a first processing module 102, a second processing module 103, and a third processing module 104, wherein:
the acquiring module 101 is configured to acquire position information of a plurality of unmanned aerial vehicles and a plurality of target points, and motion parameters of the unmanned aerial vehicles and a wind field;
the first processing module 102 is configured to construct an initial population according to the position information of the multiple unmanned aerial vehicles and the multiple target points and a preset genetic algorithm, wherein each chromosome in the initial population comprises unmanned and quantitative european paths, and each european flight path is completed by a different unmanned aerial vehicle;
the second processing module 103 is configured to determine a flight state of the unmanned aerial vehicle and a flight time of a flight path segment of an european flight path completed by the unmanned aerial vehicle according to the initial population, the unmanned aerial vehicle and the motion parameters of the wind field, and obtain a task completion time corresponding to a chromosome in the initial population according to the flight time of the flight path segment and the MUAV-VS-EVRP model;
and the third processing module 104 is configured to perform intersection and mutation processing on chromosomes in the initial population based on a genetic algorithm, and select a chromosome with the shortest task completion time as an optimal task allocation scheme of the unmanned aerial vehicle after a predetermined number of iterations is reached.
It can be seen that, this embodiment firstly analyzes the wind field and the motion parameters of the unmanned aerial vehicle to obtain the actual flight state of the unmanned aerial vehicle in the wind field, and then plans the flight path based on the actual flight state.
The following describes each functional block of the apparatus in detail:
the first processing module 102 is configured to perform chromosome coding according to a coding mode of a preset genetic algorithm to generate an initial population of a predetermined scale; the chromosome is composed of target point information and unmanned aerial vehicle information; wherein the target point belongs to a set
Figure GDA0002390600170000191
T0Denotes the start of UAVs, NTRepresenting the number of target points to which the drone belongs
Figure GDA0002390600170000192
NURepresenting the number of unmanned aerial vehicles; the first behavior of the chromosome is a random full arrangement of the target points, and the second behavior is a set of unmanned aerial vehicles for each target pointAnd randomly selecting the corresponding unmanned aerial vehicle by the punctuations, and ensuring that all the unmanned aerial vehicles in the unmanned aerial vehicle set are selected at least once.
The second processing module 103 is used for dividing the European flight path corresponding to each chromosome into a plurality of flight path segments according to the target point visited sequence; determining the flight state of the unmanned aerial vehicle by combining wind field parameters according to the coordinates of the starting point and the coordinates of the ending point corresponding to each track section, and further acquiring the flight time of the unmanned aerial vehicle for completing the track section; and acquiring task completion time corresponding to the chromosome according to the corresponding flight time of each flight path segment.
Further, the second processing module 103 is configured to determine a flight state of the unmanned aerial vehicle according to coordinates of a start point and coordinates of an end point corresponding to each track segment in combination with the wind field parameters, and further acquire a flight time of the unmanned aerial vehicle completing the track segment includes:
calculating and acquiring unmanned aerial vehicle U by adopting the following formulaiFrom target point TjFly to target point TkFlight time of the track segment:
Figure GDA0002390600170000193
wherein, UiRepresenting the unmanned aerial vehicle performing the above tasks, U representing the set of unmanned aerial vehicles, TjAs a starting point, TkTo end point, T represents the set of target points, Vg iFor unmanned plane UiThe ground speed between the two target points;
calculating and acquiring unmanned aerial vehicle U by adopting the following formulaiThe ground speed of (2):
Figure GDA0002390600170000194
wherein, Va iRepresenting the magnitude of space velocity, βa iRepresenting airspeed heading angle, Vg iIndicating the magnitude of the ground speed, βg iWhich represents the heading angle of the ground speed,
Figure GDA0002390600170000201
the size of the wind speed is shown,
Figure GDA0002390600170000202
represents the wind direction;
calculating and acquiring unmanned aerial vehicle U by adopting the following formulaiAt TjAnd TkEuclidean distance between two points:
Figure GDA0002390600170000203
wherein, X and Y respectively represent the horizontal and vertical coordinates of the corresponding target point.
In addition, the step of calculating the task completion time of the unmanned aerial vehicle corresponding to the chromosome in the initial population comprises the following steps:
acquiring the task completion time of the unmanned aerial vehicle according to the MUAV-VS-EVRP model:
Figure GDA0002390600170000204
the constraint conditions are as follows:
Figure GDA0002390600170000205
Figure GDA0002390600170000206
Figure GDA0002390600170000207
wherein.
Figure GDA0002390600170000208
Representing unmanned aerial vehicle by target point TjFly to target point TkThe time of flight of the vehicle,
Figure GDA0002390600170000209
is a binary decision variable, and
Figure GDA00023906001700002010
when UAVUiWarp beam TjFly to TkWhen it is, then
Figure GDA00023906001700002011
Is 1, otherwise
Figure GDA00023906001700002012
Has a value of 0, NTRepresenting the number of target points, NURepresenting the number of drones.
A third processing module 104, configured to perform the following steps: step 1, generating an initial solution by using the coding mode, generating an initial population with a preset scale, and calculating the fitness of the initial population according to the task completion time corresponding to each chromosome in the population; step 2, selecting two individuals (A, B) in a parent population to intersect by using a roulette method, wherein the intersection rule is that the intersection position in the individual A is randomly selected, then the first line of genes in the intersection position of the individual B, which is the same as the intersection position of the individual A, is searched, the intersection position genes in the chromosomes A and B are replaced to obtain new chromosomes C and D, whether the chromosomes C and D meet the constraint condition of the MUAV-VS-EVRP model is judged, if yes, the chromosomes A and B in the population are replaced by the chromosomes C and D, otherwise, the chromosomes which do not meet the constraint condition are subjected to constraint check, namely, when the number of unmanned aerial vehicles in the chromosomes A and B does not meet the constraint condition, one gene position is randomly selected and whether two or more unmanned aerial vehicle codes on the gene position exist or not is judged, if yes, the missing unmanned aerial vehicle codes are put into the gene position, otherwise, reselecting the gene position, generating chromosomes A and B in a chromosome replacement population meeting constraint conditions, and then continuously iteratively updating the population in the step 1 to obtain a new offspring population; and 3, selecting one chromosome in the population in the step 2 for mutation by using a roulette method, wherein the method for mutating the chromosome is at least one of the following mutation methods, and the method comprises the following steps: performing target point variation on the first line of the chromosome; carrying out unmanned aerial vehicle variation on the second chromosome line; the whole chromosome variation steps include: firstly, if the first line of the chromosome is mutated, randomly selecting two gene positions of the current chromosome and exchanging target point codes of the corresponding gene positions; selecting whether a second row has variation and a variation position, if so, randomly generating a value of the unmanned aerial vehicle code which has variation different from the current position to replace the original value, judging whether the chromosome meets the constraint condition of the MUAV-VS-EVRP model, if so, replacing the chromosome in the population, otherwise, carrying out constraint check on the chromosome which does not meet the constraint condition, namely, when the number of unmanned aerial vehicles in the chromosomes A and B does not meet the constraint condition, randomly selecting a gene position and judging whether two or more unmanned aerial vehicle codes on the gene position exist, if so, putting the missing unmanned aerial vehicle codes into the gene position, otherwise, reselecting the gene position, generating the chromosome which meets the constraint condition to replace the chromosome in the population and continuously iterating and updating the population in the step 2 to obtain a new offspring population; step 4, calculating the population fitness of the offspring and selecting the optimal solution of all solutions in the iteration; step 5, judging whether the current iteration times reach a preset value or not, if not, combining the child population and the parent population in the step 3 according to a certain proportion to form a new parent population, and returning to the step 2; if so, ending the iteration, and taking the finally obtained optimal solution as a task allocation result of the unmanned aerial vehicle.
As for the apparatus embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
It should be noted that, in the respective components of the apparatus of the present invention, the components therein are logically divided according to the functions to be implemented thereof, but the present invention is not limited thereto, and the respective components may be newly divided or combined as necessary.
Various component embodiments of the invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. In the device, the PC remotely controls the equipment or the device through the Internet, and accurately controls each operation step of the equipment or the device. The present invention may also be embodied as apparatus or device programs (e.g., computer programs and computer program products) for performing a portion or all of the methods described herein. The program for realizing the invention can be stored on a computer readable medium, and the file or document generated by the program has statistics, generates a data report and a cpk report, and the like, and can carry out batch test and statistics on the power amplifier. It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims (6)

1. A multi-unmanned aerial vehicle multitask execution distribution method is characterized by comprising the following steps:
s1, acquiring position information of a plurality of unmanned aerial vehicles and a plurality of target points, and motion parameters of the unmanned aerial vehicles and a wind field;
s2, constructing an initial population according to the position information of the unmanned aerial vehicles and the target points and a preset genetic algorithm, wherein each chromosome in the initial population comprises European flight paths of the number of the unmanned aerial vehicles, and each European flight path is completed by different unmanned aerial vehicles;
s3, determining the flight state of the unmanned aerial vehicle and the flight time of the unmanned aerial vehicle for completing the track section of the European flight path according to the initial population, the unmanned aerial vehicle and the motion parameters of the wind field, and acquiring the task completion time of all the unmanned aerial vehicles corresponding to the chromosomes in the initial population according to the flight time of the track section and the MUAV-VS-EVRP model;
s4, carrying out crossover and mutation processing on chromosomes in the initial population based on a genetic algorithm, and selecting the chromosome with the shortest task completion time of all unmanned aerial vehicles as the optimal task allocation scheme of the unmanned aerial vehicles after the predetermined iteration times are reached;
wherein S3 includes:
dividing the European flight path corresponding to each chromosome into a plurality of flight path segments according to the target point visited sequence of the European flight path;
determining the flight state of the unmanned aerial vehicle by combining wind field parameters according to the coordinates of the starting point and the coordinates of the ending point corresponding to each track section, and further acquiring the flight time of the unmanned aerial vehicle for completing the track section;
determining the flight state of the unmanned aerial vehicle by combining wind field parameters according to the coordinates of the starting point and the coordinates of the ending point corresponding to each track section, and further acquiring the flight time of the unmanned aerial vehicle for completing the track section;
according to the coordinates of the starting point and the coordinates of the ending point corresponding to each track section, determining the flight state of the unmanned aerial vehicle by combining wind field parameters, and further acquiring the flight time of the unmanned aerial vehicle for completing the track section, the method comprises the following steps:
calculating and acquiring unmanned aerial vehicle U by adopting the following formulaiFrom target point TjFly to target point TkFlight time of the track segment:
Figure FDA0002471886910000021
wherein, UiRepresenting the unmanned aerial vehicle performing the above tasks, U representing the set of unmanned aerial vehicles, TjAs a starting point, TkTo end point, T represents the set of target points, Vg iFor unmanned plane UiThe ground speed between the two target points;
calculating and acquiring unmanned aerial vehicle U by adopting the following formulaiThe ground speed of (2):
Figure FDA0002471886910000022
wherein, Va iRepresenting the magnitude of space velocity, βa iRepresenting airspeed heading angle, Vg iIndicating the magnitude of the ground speed, βg iWhich represents the heading angle of the ground speed,
Figure FDA00024718869100000214
the size of the wind speed is shown,
Figure FDA00024718869100000213
represents the wind direction;
calculating and acquiring unmanned aerial vehicle U by adopting the following formulaiAt TjAnd TkEuclidean distance between two points:
Figure FDA0002471886910000023
wherein X and Y respectively represent the horizontal and vertical coordinates of the corresponding target point;
the step of obtaining task completion time of all unmanned aerial vehicles corresponding to chromosomes in the initial population according to the flight time of the flight path segment and the MUAV-VS-EVRP model comprises the following steps:
acquiring task completion time of all unmanned aerial vehicles according to the MUAV-VS-EVRP model:
Figure FDA0002471886910000024
the constraint conditions are as follows:
Figure FDA0002471886910000025
Figure FDA0002471886910000026
Figure FDA0002471886910000027
wherein T represents a set of drone target points,
Figure FDA00024718869100000215
T0indicating the start of the UAVs,
Figure FDA0002471886910000028
representing unmanned aerial vehicle by target point TjFly to target point TkThe time of flight of the vehicle,
Figure FDA0002471886910000029
is a binary decision variable, and
Figure FDA00024718869100000210
when in use
Figure FDA00024718869100000216
Warp beam TjFly to TkWhen it is, then
Figure FDA00024718869100000211
Is 1, otherwise
Figure FDA00024718869100000212
Has a value of 0, NTRepresenting the number of target points, NURepresenting the number of drones.
2. The method of claim 1, wherein constructing the initial population according to the location information of the plurality of drones and the plurality of target points and a preset genetic algorithm comprises:
carrying out chromosome coding according to a coding mode of a preset genetic algorithm to generate an initial population with a preset scale; the chromosome is composed of target point information and unmanned aerial vehicle information; wherein the target point belongs to a set
Figure FDA0002471886910000031
T0Denotes the start of UAVs, NTRepresenting the number of target points to which the drone belongs
Figure FDA0002471886910000032
NURepresenting the number of unmanned aerial vehicles;
the first behavior of the chromosome is the random full arrangement of the target points, the second behavior randomly selects the corresponding unmanned aerial vehicle for each target point according to the unmanned aerial vehicle set, and all the unmanned aerial vehicles in the unmanned aerial vehicle set are ensured to be selected at least once.
3. The method of claim 2, wherein the steps of performing crossover and mutation processing on chromosomes in the initial population based on a genetic algorithm, and selecting the chromosome with the shortest task completion time of all the unmanned aerial vehicles as the optimal task allocation scheme of the unmanned aerial vehicles after a predetermined number of iterations are achieved comprise:
step 1, generating an initial solution by using the coding mode, generating an initial population of a preset scale, and calculating the fitness of the initial population according to the task completion time of the unmanned aerial vehicle corresponding to each chromosome in the population;
step 2, selecting two individuals A and B in a parent population to intersect by using a roulette method, wherein the intersection rule is that the intersection position in the individual A is randomly selected, then the gene in the individual B, which is the same as the first line of the intersection position of the individual A, is searched, the gene at the intersection position in the chromosomes A and B is replaced to obtain new chromosomes C and D, whether the chromosomes C and D meet the constraint condition of the MUAV-VS-EVRP model is judged, if yes, the chromosomes A and B in the population are replaced by the chromosomes C and D, otherwise, the chromosomes which do not meet the constraint condition are subjected to constraint check, namely, when the number of unmanned aerial vehicles in the chromosomes A and B does not meet the constraint condition, one gene position is randomly selected, whether two or more unmanned aerial vehicle codes on the gene position exist is judged, if yes, the missing unmanned aerial vehicle codes are put into the gene position, otherwise, reselecting the gene position, generating chromosomes A and B in a chromosome replacement population meeting constraint conditions, and then continuously iteratively updating the population in the step 1 to obtain a new offspring population;
and 3, selecting one chromosome in the population in the step 2 for mutation by using a roulette method, wherein the method for mutating the chromosome is at least one of the following mutation methods, and the method comprises the following steps: performing target point variation on the first line of the chromosome; carrying out unmanned aerial vehicle variation on the second chromosome line;
the whole chromosome variation steps include: firstly, if the first line of the chromosome is mutated, randomly selecting two gene positions of the current chromosome and exchanging target point codes of the corresponding gene positions; selecting whether a second row is mutated or not and a mutation position, if so, randomly generating a value of the unmanned aerial vehicle code which is mutated and is different from the current position to replace an original value, judging whether the chromosome meets the constraint condition of the MUAV-VS-EVRP model or not, if so, replacing the chromosome in the population, otherwise, carrying out constraint check on the chromosome which does not meet the constraint condition, namely, randomly selecting a gene position and judging whether two or more unmanned aerial vehicle codes on the gene position exist or not aiming at the chromosome which does not meet the constraint condition when the number of the unmanned aerial vehicles in the chromosome is not met with the constraint condition, if so, putting the missing unmanned aerial vehicle code into the gene position, otherwise, reselecting the gene position, generating the chromosome which meets the constraint condition to replace the chromosome in the population, and continuously iterating and updating the population in the step 2 to obtain a new offspring population;
step 4, calculating the population fitness of the offspring and selecting the optimal solution of all solutions in the iteration;
step 5, judging whether the current iteration times reach a preset value or not, if not, combining the child population and the parent population in the step 3 according to a certain proportion to form a new parent population, and returning to the step 2; if so, ending the iteration, and taking the finally obtained optimal solution as a task allocation result of the unmanned aerial vehicle.
4. A multi-unmanned aerial vehicle carries out multitask distribution device which is characterized by comprising:
the acquisition module is used for acquiring the position information of a plurality of unmanned aerial vehicles and a plurality of target points, and the motion parameters of the unmanned aerial vehicles and the wind field;
the first processing module is used for constructing an initial population according to the position information of the unmanned aerial vehicles and the target points and a preset genetic algorithm, wherein each chromosome in the initial population comprises European flight paths of the number of the unmanned aerial vehicles, and each European flight path is completed by different unmanned aerial vehicles;
the second processing module is used for determining the flight state of the unmanned aerial vehicle and the flight time of a track section of a European flight path of the unmanned aerial vehicle according to the initial population, the unmanned aerial vehicle and the motion parameters of a wind field, acquiring the task completion time of all the unmanned aerial vehicles corresponding to chromosomes in the initial population according to the flight time of the track section and an MUAV-VS-EVRP model, and dividing the flight path into a plurality of track sections according to the target point visited sequence of the European flight path corresponding to each chromosome; performing the first step and the second step;
the first step comprises: calculating and acquiring unmanned aerial vehicle U by adopting the following formulaiFrom target point TjFly to target point TkFlight time of the track segment:
Figure FDA0002471886910000051
wherein, UiRepresenting the unmanned aerial vehicle performing the above tasks, U representing the set of unmanned aerial vehicles, TjAs a starting point, TkTo end point, T represents the set of target points, Vg iFor unmanned aerial vehiclesUiThe ground speed between the two target points;
calculating and acquiring unmanned aerial vehicle U by adopting the following formulaiThe ground speed of (2):
Figure FDA0002471886910000052
wherein, Va iRepresenting the magnitude of space velocity, βa iRepresenting airspeed heading angle, Vg iIndicating the magnitude of the ground speed, βg iWhich represents the heading angle of the ground speed,
Figure FDA00024718869100000514
the size of the wind speed is shown,
Figure FDA00024718869100000515
represents the wind direction;
calculating and acquiring unmanned aerial vehicle U by adopting the following formulaiAt TjAnd TkEuclidean distance between two points:
Figure FDA0002471886910000053
wherein X and Y respectively represent the horizontal and vertical coordinates of the corresponding target point;
the second step includes:
acquiring task completion time of all unmanned aerial vehicles according to the MUAV-VS-EVRP model:
Figure FDA0002471886910000054
the constraint conditions are as follows:
Figure FDA0002471886910000055
Figure FDA0002471886910000056
Figure FDA0002471886910000057
wherein T represents a set of drone target points,
Figure FDA0002471886910000058
T0indicating the start of the UAVs,
Figure FDA0002471886910000059
representing unmanned aerial vehicle by target point TjFly to target point TkThe time of flight of the vehicle,
Figure FDA00024718869100000510
is a binary decision variable, and
Figure FDA00024718869100000511
when in use
Figure FDA00024718869100000516
Warp beam TjFly to TkWhen it is, then
Figure FDA00024718869100000512
Is 1, otherwise
Figure FDA00024718869100000513
Has a value of 0, NTRepresenting the number of target points, NURepresenting the number of drones; and the third processing module is used for carrying out crossing and mutation processing on chromosomes in the initial population based on a genetic algorithm, and selecting the chromosome with the shortest task completion time of all the unmanned aerial vehicles as the optimal task allocation method of the unmanned aerial vehicles after the preset iteration times are reached.
5. The apparatus according to claim 4, characterized in that said first processing module is adapted to perform a predetermined genetic algorithmCarrying out chromosome coding in a coding mode to generate an initial population with a preset scale; the chromosome is composed of target point information and unmanned aerial vehicle information; wherein the target point belongs to a set
Figure FDA0002471886910000061
T0Denotes the start of UAVs, NTRepresenting the number of target points to which the drone belongs
Figure FDA0002471886910000062
NURepresenting the number of unmanned aerial vehicles; the first behavior of the chromosome is the random full arrangement of the target points, the second behavior randomly selects the corresponding unmanned aerial vehicle for each target point according to the unmanned aerial vehicle set, and all the unmanned aerial vehicles in the unmanned aerial vehicle set are ensured to be selected at least once.
6. The apparatus of claim 5, wherein the third processing module is configured to perform the following steps:
step 1, generating an initial solution by using the coding mode, generating an initial population of a preset scale, and calculating the fitness of the initial population according to the task completion time of the unmanned aerial vehicle corresponding to each chromosome in the population;
step 2, selecting two individuals A and B in a parent population to intersect by using a roulette method, wherein the intersection rule is that the intersection position in the individual A is randomly selected, then the gene in the individual B, which is the same as the first line of the intersection position of the individual A, is searched, the gene at the intersection position in the chromosomes A and B is replaced to obtain new chromosomes C and D, whether the chromosomes C and D meet the constraint condition of the MUAV-VS-EVRP model is judged, if yes, the chromosomes A and B in the population are replaced by the chromosomes C and D, otherwise, the chromosomes which do not meet the constraint condition are subjected to constraint check, namely, when the number of unmanned aerial vehicles in the chromosomes A and B does not meet the constraint condition, one gene position is randomly selected, whether two or more unmanned aerial vehicle codes on the gene position exist is judged, if yes, the missing unmanned aerial vehicle codes are put into the gene position, otherwise, reselecting the gene position, generating chromosomes A and B in a chromosome replacement population meeting constraint conditions, and then continuously iteratively updating the population in the step 1 to obtain a new offspring population;
and 3, selecting one chromosome in the population in the step 2 for mutation by using a roulette method, wherein the method for mutating the chromosome is at least one of the following mutation methods, and the method comprises the following steps: performing target point variation on the first line of the chromosome; carrying out unmanned aerial vehicle variation on the second chromosome line;
the whole chromosome variation steps include: firstly, if the first line of the chromosome is mutated, randomly selecting two gene positions of the current chromosome and exchanging target point codes of the corresponding gene positions; selecting whether a second row is mutated or not and a mutation position, if so, randomly generating a value of the unmanned aerial vehicle code which is mutated and is different from the current position to replace an original value, judging whether the chromosome meets the constraint condition of the MUAV-VS-EVRP model or not, if so, replacing the chromosome in the population, otherwise, carrying out constraint check on the chromosome which does not meet the constraint condition, namely, randomly selecting a gene position and judging whether two or more unmanned aerial vehicle codes on the gene position exist or not aiming at the chromosome which does not meet the constraint condition when the number of the unmanned aerial vehicles in the chromosome is not met with the constraint condition, if so, putting the missing unmanned aerial vehicle code into the gene position, otherwise, reselecting the gene position, generating the chromosome which meets the constraint condition to replace the chromosome in the population, and continuously iterating and updating the population in the step 2 to obtain a new offspring population;
step 4, calculating the population fitness of the offspring and selecting the optimal solution of all solutions in the iteration;
step 5, judging whether the current iteration times reach a preset value or not, if not, combining the child population and the parent population in the step 3 according to a certain proportion to form a new parent population, and returning to the step 2; if so, ending the iteration, and taking the finally obtained optimal solution as a task allocation result of the unmanned aerial vehicle.
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