CN107169608A

CN107169608A - Multiple no-manned plane performs the distribution method and device of multitask

Info

Publication number: CN107169608A
Application number: CN201710389675.3A
Authority: CN
Inventors: 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
Original assignee: Hefei University of Technology
Current assignee: Hefei University of Technology
Priority date: 2017-05-27
Filing date: 2017-05-27
Publication date: 2017-09-15
Anticipated expiration: 2037-05-27
Also published as: CN107169608B

Abstract

The embodiment of the invention discloses the distribution method and device that a kind of multiple no-manned plane performs multitask.Method includes：Obtain the positional information of multiple unmanned planes and target point, and unmanned plane and wind field kinematic parameter；Building each chromosome in initial population, initial population according to positional information and default genetic algorithm includes the European flight path of unmanned plane quantity；Unmanned plane during flying state is determined according to initial population, kinematic parameter and the hours underway of European path flight path section is completed, obtaining the corresponding all unmanned planes of each chromosome according to hours underway and MUAV VS EVRP models completes task time；Based on genetic algorithm, chromosome in population is intersected, made a variation, after predetermined iterations is reached, chosen unmanned plane and complete task time most short chromosome as the task allocative decision of unmanned plane.Unmanned aerial vehicle flight path planning problem is combined by the embodiment of the present invention with unmanned plane practical flight environment, and the optimal flight paths scheme for obtaining planning is better than the scheme of unmanned plane constant airspeed.

Description

Multiple no-manned plane performs the distribution method and device of multitask

Technical field

The present embodiments relate to unmanned air vehicle technique field, and in particular to a kind of multiple no-manned plane performs the distribution side of multitask Method and device.

Background technology

Currently, unmanned plane UAV (Unmanned Aerial Vehicle) has a wide range of applications in Military and civil fields, can be complete The polytype task such as rescue and geological prospecting after into target reconnaissance, target following, information acquisition, shake.For example assisted in multi rack UAV During with spot, the target that most reasonably should be scouted for needed for every frame UAV distributes it will also plan optimal flight for it Flight path.The problem is a task distribution and trajectory planning combined optimization problem by multifactor constraint, is also that uncertainty is asked Topic.

With going deep into that UAV is studied, environmental factor is gradually included the research of problem, the particularly distribution of UAV tasks, flight path Planning and flight control the problems such as in, how to be reduced under the influence of environmental factor power consumption, control UAV state of flight so that It is current UAV that UAV, which consumes minimum fuel and performs most tasks, possesses more preferable execution status of task and Geng Gao security, The groundwork of research.It is current to be usually used in solving the problems, such as that the model of the distribution of UAV tasks and mission planning has：TSP models, TOP moulds Type and VRP models, wherein, TSP models are under conditions of only single traveller so that traveller passes through all given mesh After punctuate, so that the minimum model of its path cost；TOP models are under conditions of it there are multiple members so that each Member accesses more target points as far as possible, so that the maximum model of the total revenue of all members；VRP models are in vehicle Under conditions of quantity is fixed so that vehicle accesses certain amount target point, and each target point can only be accessed in the process Once, the final total distance for causing UAV navigation or total time most short model.

During the embodiment of the present invention is realized, inventor has found existing technical scheme in practical operation, typically Assume that the speed of unmanned plane is constant in Time constant in model.But this hypothesis is clearly unpractical, causes Model can not accurately simulate the actual motion state of unmanned plane, and then can not carry out optimal trajectory planning.

The content of the invention

One purpose of the embodiment of the present invention is to solve prior art due to being setting unmanned plane carrying out trajectory planning Speed is constant, causes model can not accurately simulate the actual motion state of unmanned plane, so can not provide it is optimal Trajectory planning.

The embodiment of the present invention proposes the distribution method that a kind of multiple no-manned plane performs multitask, including：

S1, the positional information for obtaining multiple unmanned planes and multiple target points, and the multiple unmanned plane and wind field fortune Dynamic parameter；

S2, positional information and default genetic algorithm according to the multiple unmanned plane and the multiple target point, build just Each chromosome European flight path including unmanned plane quantity and the European flight of each bar in beginning population, the initial population Path is completed by different unmanned planes；

S3, unmanned plane during flying state and unmanned plane determined according to the kinematic parameter of the initial population, unmanned plane and wind field The hours underway of the flight path section of European flight path is completed, according to the hours underway and MUAV-VS-EVRP models of flight path section Obtain the corresponding all unmanned planes of chromosome in initial population and complete task time；

S4, based on genetic algorithm, chromosome in initial population is intersected, variation processing, and is reaching predetermined iteration After number of times, choose all unmanned planes and complete task times most short chromosome as the optimal task assignment side of the unmanned plane Case.

Optionally, according to the positional information and default genetic algorithm of the multiple unmanned plane and the multiple target point, structure Building initial population includes：

The initial population that chromosome coding generates pre-determined size is carried out according to the coded system of default genetic algorithm；The dye Colour solid is made up of target point information and unmanned machine information；Wherein described target point belongs to setT₀Represent UAVs starting point, N_TTarget point quantity is represented, unmanned plane belongs to setN_URepresent unmanned plane quantity；

The random fully intermeshing of target point described in the behavior of chromosome first, the second behavior is combined into each according to unmanned plane collection Target point randomly selects corresponding unmanned plane, and need to ensure that the unmanned plane in unmanned plane set is all at least chosen once.

Optionally, unmanned plane during flying state and nothing are determined according to the kinematic parameter of the initial population, unmanned plane and wind field The hours underway of the flight path section of the man-machine European flight path of completion, according to the hours underway and MUAV-VS-EVRP of flight path section Chromosome corresponding all unmanned planes completion task times include in model acquisition initial population：

The flight path is divided into by each corresponding European flight path of chromosome according to the accessed order of its target point Multiple flight path sections；

According to the coordinate of the corresponding starting point of each flight path section and the coordinate of terminating point, nobody is determined with reference to Wind parameters in wind Machine state of flight, and then obtain all unmanned planes completion task time that the unmanned plane completes the flight path section；

When completing task according to the corresponding all unmanned planes of each flight path section corresponding hours underway acquisition chromosome Between.

Optionally, according to the coordinate of the corresponding starting point of each flight path section and the coordinate of terminating point, with reference to Wind parameters in wind Unmanned plane during flying state is determined, and then the hours underway of the acquisition unmanned plane completion flight path section includes：

Calculated using below equation and obtain unmanned plane U_iBy target point T_jSet out and fly to target point T_kDuring the navigation of flight path section Between：

Wherein, U_iThe unmanned plane of the above-mentioned task of execution is represented, U represents unmanned plane set, T_jFor starting point,_TkFor terminating point, T represents the set of target point, V_g ⁱFor unmanned plane U_iGround velocity between above-mentioned two target point；

Calculated using below equation and obtain unmanned plane U_iGround velocity：

Wherein, V_a ⁱRepresent air speed size, β_a ⁱRepresent air speed course angle, V_g ⁱRepresent the size of ground velocity, β_g ⁱRepresent ground velocity course Angle,Wind speed size is represented,Represent wind direction；

Calculated using below equation and obtain unmanned plane U_iIn T_jAnd T_kThe Euclidean distance of point-to-point transmission：

Wherein, X, Y represent correspondence target point horizontal stroke, ordinate respectively.

Optionally, chromosome in initial population is obtained according to the hours underway and MUAV-VS-EVRP models of flight path section Corresponding all unmanned plane completion task times include：

Hours underway is obtained according to MUAV-VS-EVRP models：

Its constraints is：

WhereinRepresent unmanned plane by target point T_jSet out and fly to target point T_kHours underway,It is a binary Decision variable, andWork as UAVU_iThrough T_jFly to T_kWhen, thenValue be 1, otherwiseValue be 0, N_TRepresent The quantity of target point, N_URepresent the quantity of unmanned plane.

Optionally, based on genetic algorithm, chromosome in initial population is intersected, variation is handled, and it is predetermined reaching After iterations, choose all unmanned planes and complete task times most short chromosome as the optimal task assignment of the unmanned plane Scheme includes：

Step 1, using the coding method initial solution is generated, and generate the initial population of pre-determined size and according in population Each chromosome corresponding all unmanned planes completion task times calculate its fitness；

Step 2, intersected using two individuals (A, B) in roulette method choice parent population, crossover rule is First crossover location in random selection individual A, then in lookup individual B with individual A crossover locations the first row identical gene, will contaminate Crossover location gene, which is replaced, in colour solid A and B obtains new chromosome C and D, judges whether chromosome C and D meet MUAV- The constraints of VS-EVRP models, if replacing chromosome A and B in population using chromosome C and D if meeting, otherwise to being unsatisfactory for The chromosome of constraints carries out constraint checking, i.e. when unmanned plane quantity is unsatisfactory for constraints in inspection chromosome A and B, pin Chromosome to being unsatisfactory for condition, randomly selects a gene position and judges that the coding of the unmanned plane in the gene position whether there is two Individual and two or more, if the unmanned plane of missing then is encoded into the gene position, otherwise chooses gene position, generation is met again The chromosome of constraints replaces chromosome A and B in population, and then continuous iteration updates step 1 population, obtains new filial generation kind Group；

Step 3, using item chromosome in roulette method choice step 2 population enter row variation, the chromosome is entered The mode of row variation is at least one of following variation modes, including：Target point variation is carried out to chromosome the first row；To dye The row of colour solid second carries out unmanned plane variation；

The step of whole chromosome makes a variation includes：First, if the first row order of chromosome makes a variation, randomly select current Two gene positions of chromosome and the target point coding for exchanging correspondence gene position；Whether the row of reselection second makes a variation and becomes dystopy Put, random generation variation has the value encoded different from current location unmanned plane to replace initial value if variation, and sentences after variation Whether disconnected chromosome meets the constraints of MUAV-VS-EVRP models, if replacing chromosome in population if meeting, otherwise to not The chromosome for meeting constraints carries out constraint checking, i.e., when unmanned plane quantity is unsatisfactory for constraints in inspection chromosome, pin Chromosome to being unsatisfactory for condition, randomly selects a gene position and judges that the coding of the unmanned plane in the gene position whether there is two Individual and two or more, if the unmanned plane of missing then is encoded into the gene position, otherwise chooses gene position, generation is met again The chromosome of constraints replaces the chromosome in population and constantly iteration updates step 2 population, obtains new progeny population；

Step 4, calculating progeny population fitness simultaneously choose the optimal solution in current iteration in all solutions；

Step 5, judge whether current iterations reaches preset value, if judgement is no, to the progeny population in step 3 Combine to form new parent population return to step 2 according to a certain percentage with parent population；If being judged as YES, terminate iteration, will The optimal solution finally obtained as unmanned plane task allocation result.

The embodiment of the present invention proposes the distributor that a kind of multiple no-manned plane performs multitask, including：

Acquisition module, the positional information for obtaining multiple unmanned planes and multiple target points, and the multiple unmanned plane With the kinematic parameter of wind field；

First processing module, for the positional information according to the multiple unmanned plane and the multiple target point and default something lost Propagation algorithm, each chromosome built in initial population, the initial population includes the European flight path of unmanned plane quantity Each European flight path of bar is completed by different unmanned planes；

Second processing module, for determining that unmanned plane flies according to the kinematic parameter of the initial population, unmanned plane and wind field Row state and unmanned plane complete the hours underway of the flight path section of European flight path, according to the hours underway of flight path section and MUAV-VS-EVRP models obtain the corresponding all unmanned planes of chromosome in initial population and complete task time；

3rd processing module, for based on genetic algorithm, being intersected to chromosome in initial population, variation processing, and After predetermined iterations is reached, choose all unmanned planes and complete task times most short chromosome as the unmanned plane most Excellent task allocative decision.

Optionally, the first processing module, for carrying out chromosome coding according to the coded system of default genetic algorithm Generate the initial population of pre-determined size；The chromosome is made up of target point information and unmanned machine information；Wherein described target point Belong to setT₀Represent UAVs starting point, N_TTarget point quantity is represented, unmanned plane belongs to setN_URepresent unmanned plane quantity；The random fully intermeshing of target point, the second row described in the behavior of chromosome first Corresponding unmanned plane is randomly selected to be combined into each target point according to unmanned plane collection, and the unmanned plane in unmanned plane set need to be ensured All at least it is chosen once.

Optionally, the Second processing module, for the corresponding European flight path of each chromosome according to its target point The flight path is divided into multiple flight path sections by accessed order；According to the coordinate of the corresponding starting point of each flight path section and end The coordinate of stop, determines unmanned plane during flying state, and then obtain the unmanned plane completion flight path section with reference to Wind parameters in wind Hours underway；When being completed according to the corresponding all unmanned plane tasks of each flight path section corresponding hours underway acquisition chromosome Between.

Optionally, the Second processing module, for the coordinate according to the corresponding starting point of each flight path section and termination The coordinate of point, determines unmanned plane during flying state, and then obtain the boat that the unmanned plane completes the flight path section with reference to Wind parameters in wind The row time includes：

Calculated using below equation and obtain unmanned plane U_iGround velocity：

As shown from the above technical solution, a kind of multiple no-manned plane that the embodiment of the present invention is proposed accesses the flight path rule of multiple target point Draw method and device to analyze by the kinematic parameter to wind field and unmanned plane first, obtain reality of the unmanned plane in wind field State of flight, is then based on the planning that actual flight state carries out flight path, permanent with setting unmanned plane speed in the prior art Fixed scheme is compared, and can accurately calculate unmanned plane on be possible to flight path according to the state of uncertain environment Wind Field Hours underway, and then select optimal flight path.

Brief description of the drawings

The features and advantages of the present invention can be more clearly understood from by reference to accompanying drawing, accompanying drawing is schematical without that should manage Solve to carry out any limitation to the present invention, in the accompanying drawings：

Fig. 1 shows that the flow for the distribution method that a kind of multiple no-manned plane that one embodiment of the invention is provided performs multitask is shown It is intended to；

Fig. 2 shows the flow signal of the hours underway for the calculating Dubins flight paths that one embodiment of the invention is provided Figure；

Fig. 3 shows the schematic flow sheet for the genetic algorithm that one embodiment of the invention is provided；

Fig. 4 a- Fig. 4 c show that one embodiment of the invention provides the schematic diagram of the operator in genetic algorithm；

Fig. 5 shows the wind direction schematic diagram that one embodiment of the invention is provided；

Fig. 6 shows the velocity relation schematic diagram that one embodiment of the invention is provided；

Fig. 7 shows that UAV that one embodiment of the invention provides is flown to the analysis schematic diagram that C points are influenceed by wind field by A；

Fig. 8 shows the schematic diagram being segmented to flight path that one embodiment of the invention is provided；

Fig. 9 shows that the structure for the distributor that a kind of multiple no-manned plane that one embodiment of the invention is provided performs multitask is shown It is intended to.

Embodiment

To make the purpose, technical scheme and advantage of the embodiment of the present invention clearer, below in conjunction with the embodiment of the present invention In accompanying drawing, the technical scheme in the embodiment of the present invention is clearly and completely described, it is clear that described embodiment is A part of embodiment of the present invention, rather than whole embodiments.Based on the embodiment in the present invention, ordinary skill people The every other embodiment that member is obtained on the premise of creative work is not made, belongs to the scope of protection of the invention.

Fig. 1 shows that a kind of multiple no-manned plane that one embodiment of the invention is provided accesses the flow of the trajectory planning of multiple target point Schematic diagram, referring to Fig. 1, this method can be realized by processor, specifically include following steps：

110th, obtain the positional information of multiple unmanned planes and multiple target points, and the multiple unmanned plane and wind field fortune Dynamic parameter；

It should be noted that before the distribution of the task of progress and trajectory planning, technical staff can set or according to reality Situation measures the positional information of unmanned plane and multiple target points, is then input in processor.

Needed to set according to practical flight in addition, the kinematic parameter of unmanned plane can be technical staff, the motion of wind field Parameter can technical staff's measurement draw or set according to actual conditions.

120th, according to the positional information and default genetic algorithm of the multiple unmanned plane and the multiple target point, build just Each chromosome European flight path including unmanned plane quantity and the European flight of each bar in beginning population, the initial population Path is completed by different unmanned planes；

130th, unmanned plane during flying state and unmanned plane are determined according to the kinematic parameter of the initial population, unmanned plane and wind field The hours underway of the flight path section of European flight path is completed, according to the hours underway and MUAV-VS-EVRP models of flight path section Obtain the corresponding all unmanned planes of chromosome in initial population and complete task time；

140th, based on genetic algorithm, chromosome in initial population is intersected, variation handles, and reaching predetermined iteration After number of times, choose all unmanned planes and complete task times most short chromosome as the optimal task assignment side of the unmanned plane Case.

It is understandable to be, intersect every time, the iteration of variation there may be new individual appearance, be then based on step The calculating for the hours underway that 130 pairs of new chromosomes are carried out, therefore, one hours underway of each European flight path correspondence.

It can be seen that, the present embodiment is analyzed by the kinematic parameter to wind field and unmanned plane first, obtains unmanned plane in wind Actual flight state in, is then based on the planning that actual flight state carries out flight path, compared with prior art, this reality Apply example to be combined unmanned aerial vehicle flight path planning problem with unmanned plane practical flight environment, the optimal flight paths side for obtaining planning Case is better than the scheme of unmanned plane constant airspeed, and then reaches and can accurately calculate navigation of the unmanned plane on be possible to flight path Time, and then select optimal flight path.

Each step in the embodiment of the present invention is described in detail below：

First, step 120 is described in detail：

Then, referring to Fig. 2, step 130 is described in detail below：

210th, the corresponding European flight path of each chromosome is accessed sequentially by the flight path according to its target point It is divided into multiple flight path sections；

220th, according to the coordinate of the corresponding starting point of each flight path section and the coordinate of terminating point, determined with reference to Wind parameters in wind Unmanned plane during flying state, and then obtain the hours underway that the unmanned plane completes the flight path section；

230th, the corresponding all unmanned planes of the chromosome are obtained according to the corresponding hours underway of each flight path section to complete to appoint The business time.

Wherein, step 220 includes：

Calculated using below equation and obtain unmanned plane U_iGround velocity：

In addition, calculating the step of corresponding unmanned plane of chromosome completes task time in initial population includes：

Unmanned plane is obtained according to MUAV-VS-EVRP models and completes task time：

Its constraints is：

Step 140 is described in detail below：

Step 1, using the coding method initial solution is generated, and generate the initial population of pre-determined size and according in population Each chromosome corresponding unmanned plane completion task time calculates its fitness；

Step 2, intersected using two individuals (A, B) in roulette method choice parent population, crossover rule is First crossover location in random selection individual A, then in lookup individual B with individual A crossover locations the first row identical gene, will contaminate Crossover location gene, which is replaced, in colour solid A and B obtains new chromosome C and D, judges whether chromosome C and D meet MUAV- The constraints of VS-EVRP models, if replacing chromosome A and B in population using chromosome C and D if meeting, otherwise to being unsatisfactory for The chromosome of constraints carries out constraint checking, and the chromosome that generation meets constraints replaces chromosome A and B in population, so Continuous iteration updates step 1 population afterwards, obtains new progeny population；

The step of whole chromosome makes a variation includes：First, if the first row order of chromosome makes a variation, randomly select current Two gene positions of chromosome and the target point coding for exchanging correspondence gene position；Whether the row of reselection second makes a variation and becomes dystopy Put, random generation variation has the value encoded different from current location unmanned plane to replace initial value if variation, and sentences after variation Whether disconnected chromosome meets the constraints of MUAV-VS-EVRP models, if replacing chromosome in population if meeting, otherwise to not Meet constraints chromosome carry out constraint checking, generation meet constraints chromosome replace population in chromosome simultaneously Continuous iteration updates step 2 population, obtains new progeny population；

The principle of the genetic algorithm of the use of the present invention is described in detail referring to Fig. 3：

1st, open；

2nd, the setting based on technical staff, generation includes the population of specified quantity chromosome, and specified quantity can be specially 100 It is individual；

As shown in fig. 4 a, chromosome A represent constant wind off field two unmanned plane UAV access one kind of three target points can Row scheme, i.e., No. one UAV set out from starting point S (0,0), are returned successively after access target point 3 and target point 1, No. two UAV are from Initial point S (0,0) sets out, and is returned after access target point 2.The second row represents the coding that UAV accesses correspondence target point in coding.

Wherein, chromosome A includes two European flight paths and the European flight path of each bar is by different unmanned planes one With No. two completions.

3rd, the fitness of each chromosome is calculated；

It should be noted that using the computational methods of the step 140 in Fig. 1 correspondence embodiments, calculating unmanned plane and completing every The hours underway of individual European flight path, and based on unmanned plane complete task time calculate chromosome fitness, for example：Nobody Machine completes task time and fitness inversely.

It is understandable to be, carry out fitness after generating the population of specified quantity according to the coded system in above-mentioned steps 2 Calculating, the calculating of fitness is using object function as foundation in invention herein, and its calculating process is as follows：

4th, selection operation

Selection operation is carried out by the method for roulette according to J '.

5th, crossover operation

By intersecting to parent chromosome, it can inherit and compare excellent gene in parent, obtain more excellent filial generation. The method that single-point maps is used herein for current coded system for MUAV-VS-EVRP problems, that is, randomly generates parent dye The gene position that colour solid A intersects, finds the corresponding gene position of same target point in parent chromosome B, intersects and produces daughter chromosome A, B, and constraints verification is carried out to daughter chromosome A, B.

Referring to Fig. 4 b, there are parent Parent A and Parent B, the gene intersected is randomly generated on Parent A Position is 3, finds the gene position of correspondence same target point on Parent B, daughter chromosome OffSpring A are produced after intersection And OffSpringB, constraint checking is carried out to OffSpring A and OffSpringB, it is found that OffSpring A unmanned plane is compiled Code represents same unmanned plane, does not meet constraints, thus randomly generates base to OffSpring A unmanned plane coding again Intersect because the 3rd gene position of position and Parent A carries out mapping, OffSpring A meet constraints after intersection.

6th, mutation operation

Variation is to prevent genetic algorithm to be absorbed in local optimum.Calculated for the heredity for solving sUav-DVS-VRP models There are two kinds of situations in method, chromosomal variation：The variation of target point coding and course angle encode variation.According to mutation probability, chromosome In can occur repeatedly variation also do not morph.Wherein, the variation of target point coding is using the variation of dual-gene position, i.e., in chromosome The first row randomly generate two gene positions for entering row variation, and the value in two gene positions is exchanged, this method meets mould The constraint that each target point is only accessed once in type, it is ensured that the feasibility of daughter chromosome, course angle coding is using uniform change It is different.

Mutation operation is illustrated：

As illustrated in fig. 4 c, there are parent Parent A, carry out target point variation and unmanned plane variation respectively on Parent A, First determine whether whether two kinds of variations occur before row variation is entered, when judging to obtain target point variation generation, randomly select progress The gene position chosen in the gene position of compiling, this example is 1 and 3, then swaps the desired value in the gene position being selected, Obtain new target point access order；When judging to obtain unmanned plane variation generation, the gene position into row variation is randomly selected, this The gene position chosen in example is 3, and the random generation unmanned plane coding different from current unmanned plane replaces currency, obtains new Parent A.Constraints verification is carried out to Parent A, it is found that Parent A unmanned plane coding represents same unmanned plane, Constraints is not met, thus mutation operation is carried out to Parent A unmanned plane coding again, it is 2 to choose mutant gene position, The random generation unmanned plane coding different from current unmanned plane replaces currency, obtains OffSpring A and meets constraints.

7th, operation is updated

8th, optimal distributing scheme is chosen

9th, judge whether to terminate

10th, optimal distributing scheme is obtained

11st, terminate

It should be noted that above-mentioned steps are corresponding with the part steps in the corresponding embodiments of Fig. 1, therefore, similarity this Place is repeated no more, the related content that please specifically check in the corresponding embodiments of Fig. 1.

The design principle of the present invention is described in detail with reference to above-mentioned genetic algorithm：

Step one, to avoid problem excessively complicated, the present invention fixes wind field using region and carries out wind field modeling, i.e., in regulation In region, the wind speed and direction of its wind field is constant.

The wind field state of known region is represented by：

Wherein, V_wRepresent the wind speed in wind field, β_wRepresent wind direction.

Wind speed V_wRefer to wind facies for the distance moved in the unit interval of ground, unit is m/s；Wind direction β_wRefer to that wind comes Direction, the units of measurement of wind direction typically represents with orientation, such as land, typically uses 16 orientation references, marine to use 36 Individual orientation references, and then represented in high-altitude with angle, i.e., circumference is divided into 360 degree, provides that west wind (W) is 0 degree (i.e. 360 herein Degree), south wind (S) is 90 degree, and east wind (E) is 180 degree, and north wind (N) is 270 degree, as shown in Figure 5.

Step 2, configures UAV

With

Represent that the four skyborne configuration definitions of rotor mUAV, mUAV are：

Q=(x, y, β_g) (4)

Wherein,

Wherein,WithWhat is represented is coordinates of the frame UAV in Descartes's inertial reference system；V_gRepresent UAV ground velocity β_g Refer to mUAV course angle.

To make problem reduction, set forth herein the vacation below in relation to the UAV kinematic constraints for needing to meet in task process is performed If：

(1) mUAV is many rotor UAV of isomorphism；

(2) mUAV collision is not considered, it is believed that mUAV has barrier avoiding function；

(3) consider mUAV in fixed altitude；

(4) according to mUAV flight envelope, there is bound in flying speeds of the mUAV under specified altitude assignment constantly acting load, i.e.,V_{a_min}And V_{a_max}It is illustrated respectively in the minimum value and maximum of mUAV air speeds under certain height；

(5) multi rack mUAV has starting point to set out and returns to starting point after completion task is performed.

Step 3, calculates UAV actual flight state

Consider that the UAV actual speeds of wind effect are defined as UAV ground velocity size for V_g, now UAV course angle is β_g, UAV Ground vectorThe air speed size that the UAV theoretical velocities for not considering wind effect are defined as into UAV is V_a, now UAV Course angle is β_a, UAV air velocity vectorsUAV air speedsGround velocityWith wind speed in wind fieldVector correlation such as Fig. 6 institutes Show.

Above-mentioned speed is with angular relationship：

When calm,That is UAV air speeds are equal with ground velocity.

It is illustrated with reference to Fig. 7：

UAV is flown to A (50,300) by S (0,0), and air speed is 8m/s, and the environment residing for the UAV is that wind speed is 5m/s, wind direction For south wind (w=90 ° of Vw=5m/s, β), Uav air speeds in this process and ground velocity are can obtain according to formula (7) as shown in table 4-1.

The rotor Uav of table 4-1 tetra- air speed, ground velocity contrast table under calm and south wind environment

	Air speed	Ground velocity
			South wind environment	28.80km/h,35.5°	23.49km/h,80.5°
No-wind environment	28.80km/h,80.5°	28.80km/h,80.5°

Step 4, target point configuration

N_TThe set of individual target point is represented by：

Wherein, the position of target point all in set and task amount are known.In the present invention, on each target point May all there is different types of task to need to be performed by UAV, and every frame UAV can only be performed on a target point in the process One task, i.e., each target point will be accessed by different UAV, and every frame UAV can only access some target point once.

Step 5, calculates hours underway

In the distribution of UAV tasks and trajectory planning problem using the flight time as target, UAV task allocative decision is determined Determine the order of UAV access targets point, trajectory planning is carried out according to UAV target points access order, calculated by the result of trajectory planning The UAV flight time and then determine whether the distribution of current UAV tasks is better than known formula with trajectory planning scheme by the UAV flight time Case.

Because Uav in the ship trajectory of point-to-point transmission is Euclidean distance, thus Uav is fixed in the navigation direction of point-to-point transmission, is entered And it is constant in the ground velocity of point-to-point transmission to fix Uav under wind field, but under fixed wind field Uav by target point in multiple target points Ground velocity is change in scene, and its flight time is calculated as follows：

Wherein,

Represent Uav in T_jAnd T_kThe Euclidean distance of point-to-point transmission, ground velocity of the Uav in point-to-point transmissionTried to achieve by formula (7).

So as to which mUAV task completion time can be calculated according to formula (8).

Wherein,Represent UAVU_iIn T_j、T_k2 points of hours underway；

It is a binary decision variable, andWork as UAVU_YThrough T_jFly to T_kWhen, thenValue be 1, OtherwiseValue be 0；

J, k value take 0 expression UAV by starting point in J or path ends point to starting point.

In solution procedure, also need to meet following constraints：

Above-mentioned condition ensures that all target points can be accessed to and can only be accessed once.

Above-mentioned condition ensures the UAV routes of the UAV quantity by starting point, and has the UAV paths sensing of UAV quantity same Point.

Above-mentioned condition ensures that the path for the route and each frame UAV for having UAV quantity is on the basis of other constraintss The ring of one closure, i.e., UAV ship trajectory is an orderly route, and is eventually returned to starting point.

It can be seen that, the corresponding task completion time of each chromosome can obtain based on above formula, and then therefrom selection has been gone out on missions Into time most short task allocative decision.

The detailed description of instantiation is carried out to the present invention below：

First, all emulation experiments are on 4G internal memories, 3.4GHzCPU hardware, in MatlabR2014a ring Run in border.It is described as follows：

Mathematical modeling of the UAV models based on UAV, its air speed is 8 meter per seconds, and two frame UAV take off from starting point S (0,0), The reentry point S (0,0) after access task is completed；Wind field environment is fixed wind field, i.e., the wind speed and direction in an experimentation All be constant, and in order to ensure UAV can safe flight, wind speed size is 5 meter per seconds, and wind direction takes east, i.e., 180 °, and UAV is needed Three coordinate of ground point to be accessed 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).

The model and algorithm proposed according to the invention described above, is carried out real under east wind wind field environment and experiment scene herein Test, and obtain the most short task of unmanned plane task completion time under each wind field environment and distribute as shown in table 3-1 (referring to Fig. 8).

3-1

For method embodiment, in order to be briefly described, therefore it is all expressed as to a series of combination of actions, but ability Field technique personnel should know that embodiment of the present invention is not limited by described sequence of movement, because according to the present invention Embodiment, some steps can be carried out sequentially or simultaneously using other.Secondly, those skilled in the art should also know, Embodiment described in this description belongs to preferred embodiment, involved action embodiment party not necessarily of the present invention Necessary to formula.

Fig. 9 shows that a kind of unmanned plane that one embodiment of the invention is provided accesses the knot of the trajectory planning device of multiple target point Structure schematic diagram, referring to Fig. 9, the device includes：Acquisition module 101, first processing module 102, Second processing module 103 and Three processing modules 104, wherein：

Acquisition module 101, the positional information for obtaining multiple unmanned planes and multiple target points, and it is the multiple nobody The kinematic parameter of machine and wind field；

First processing module 102, for the positional information according to the multiple unmanned plane and the multiple target point and in advance If genetic algorithm, each chromosome built in initial population, the initial population includes the European path of nobody and quantity And each European flight path of bar is completed by different unmanned planes；

Second processing module 103, for determining unmanned plane according to the kinematic parameter of the initial population, unmanned plane and wind field State of flight and unmanned plane complete the hours underway of the flight path section of European flight path, according to the hours underway of flight path section and MUAV-VS-EVRP models obtain the corresponding task completion time of chromosome in initial population；

3rd processing module 104, for based on genetic algorithm, being intersected to chromosome in initial population, variation processing, And after predetermined iterations is reached, task completion time most short chromosome is chosen as the OPTIMAL TASK point of the unmanned plane With scheme.

Each functional module to the present apparatus is described in detail below：

First processing module 102, it is predetermined for carrying out chromosome coding generation according to the coded system of default genetic algorithm The initial population of scale；The chromosome is made up of target point information and unmanned machine information；Wherein described target point belongs to setT₀Represent UAVs starting point, N_TTarget point quantity is represented, unmanned plane belongs to setN_U Represent unmanned plane quantity；The random fully intermeshing of target point described in the behavior of chromosome first, the second behavior is according to unmanned plane collection It is combined into each target point and randomly selects corresponding unmanned plane, and need to ensures that the unmanned plane in unmanned plane set is all at least chosen Once.

Second processing module 103, is accessed suitable for the corresponding European flight path of each chromosome according to its target point The flight path is divided into multiple flight path sections by sequence；According to the coordinate of the corresponding starting point of each flight path section and the seat of terminating point Mark, determines unmanned plane during flying state, and then obtain the hours underway that the unmanned plane completes the flight path section with reference to Wind parameters in wind； The corresponding task completion time of the chromosome is obtained according to the corresponding hours underway of each flight path section.

Further, Second processing module 103, for the coordinate according to the corresponding starting point of each flight path section and termination The coordinate of point, determines unmanned plane during flying state, and then obtain the boat that the unmanned plane completes the flight path section with reference to Wind parameters in wind The row time includes：

Calculated using below equation and obtain unmanned plane U_iGround velocity：

Its constraints is：

3rd processing module 104, for performing following steps：Step 1, using the coding method generate initial solution, and Generate the initial population of pre-determined size and its fitness is calculated according to the corresponding completion task time of each chromosome in population；Step Rapid 2, intersected using two individuals (A, B) in roulette method choice parent population, crossover rule is first to randomly choose Crossover location in individual A, then search in individual B with individual A crossover locations the first row identical gene, by chromosome A and B Crossover location gene, which is replaced, obtains new chromosome C and D, judges whether chromosome C and D meet MUAV-VS-EVRP models Constraints, if replacing chromosome A and B in population using chromosome C and D if meeting, otherwise to being unsatisfactory for constraints Chromosome carries out constraint checking, i.e. when unmanned plane quantity is unsatisfactory for constraints in inspection chromosome A and B, for being unsatisfactory for bar The chromosome of part, randomly select a gene position and judge the unmanned plane in the gene position coding with the presence or absence of two and two with On, if the unmanned plane of missing then is encoded into the gene position, gene position is otherwise chosen again, and generation meets constraints Chromosome replaces chromosome A and B in population, and then continuous iteration updates step 1 population, obtains new progeny population；Step 3, Enter row variation using item chromosome in roulette method choice step 2 population, be to the mode that the chromosome enters row variation At least one of following variation modes, including：Target point variation is carried out to chromosome the first row；The row of chromosome second is carried out Unmanned plane makes a variation；The step of whole chromosome makes a variation includes：First, if the first row order of chromosome makes a variation, randomly select Two gene positions of current chromosome and the target point coding for exchanging correspondence gene position；Whether the row of reselection second makes a variation and makes a variation Position, random generation variation has the value encoded different from current location unmanned plane to replace initial value if variation, and after variation Judge whether chromosome meets the constraints of MUAV-VS-EVRP models, it is otherwise right if replacing chromosome in population if meeting The chromosome for being unsatisfactory for constraints carries out constraint checking, that is, examines unmanned plane quantity in chromosome A and B to be unsatisfactory for constraints When, the chromosome for being unsatisfactory for condition randomly selects a gene position and whether judges the coding of the unmanned plane in the gene position In the presence of two and two or more, if the unmanned plane of missing then is encoded into the gene position, gene position is otherwise chosen again, it is raw The chromosome in population is replaced into the chromosome for meeting constraints and constantly iteration updates step 2 population, obtains new filial generation Population；Step 4, calculating progeny population fitness simultaneously choose the optimal solution in current iteration in all solutions；Step 5, judgement are current Iterations whether reach preset value, if judgement is no, to the progeny population in step 3 and parent population according to a certain percentage Combination forms new parent population return to step 2；If being judged as YES, terminate iteration, regard the optimal solution finally obtained as nothing Man-machine task allocation result.

For device embodiments, because it is substantially similar to method embodiment, so description is fairly simple, Related part illustrates referring to the part of method embodiment.

It should be noted that in all parts of the device of the present invention, according to the function that it to be realized to therein Part has carried out logical partitioning, still, and the present invention is not only restricted to this, all parts can be repartitioned as needed or Person combines.

The present invention all parts embodiment can be realized with hardware, or with one or more processor transport Capable software module is realized, or is realized with combinations thereof.In the present apparatus, PC is by realizing internet to equipment or device Remote control, the step of accurately control device or device are each operated.The present invention is also implemented as being used to perform here The some or all equipment or program of device of described method are (for example, computer program and computer program production Product).Being achieved in that the program of the present invention can store on a computer-readable medium, and the file or document tool that program is produced Having can be statistical, produces data report and cpk reports etc., and batch testing can be carried out to power amplifier and is counted.It should be noted that on Stating embodiment, the present invention will be described rather than limits the invention, and those skilled in the art are not departing from Replacement embodiment can be designed in the case of the scope of attached claim.In the claims, it will should not be located between bracket Any reference symbol be configured to limitations on claims.Word "comprising" does not exclude the presence of member not listed in the claims Part or step.Word "a" or "an" before element does not exclude the presence of multiple such elements.The present invention can be borrowed Help include the hardware of some different elements and realized by means of properly programmed computer.If listing equipment for drying Unit claim in, several in these devices can be embodied by same hardware branch.Word first, Second and third use do not indicate that any order.These words can be construed to title.

Finally it should be noted that：The above embodiments are merely illustrative of the technical solutions of the present invention, rather than its limitations；Although The present invention is described in detail with reference to the foregoing embodiments, it will be understood by those within the art that：It still may be used To be modified to the technical scheme described in foregoing embodiments, or equivalent substitution is carried out to which part technical characteristic； And these modification or replace, do not make appropriate technical solution essence depart from various embodiments of the present invention technical scheme spirit and Scope.

Claims

1. a kind of multiple no-manned plane performs the distribution method of multitask, it is characterised in that including：

S1, the positional information for obtaining multiple unmanned planes and multiple target points, and the motion of the multiple unmanned plane and wind field are joined Number；

S2, positional information and default genetic algorithm according to the multiple unmanned plane and the multiple target point, build initial plant European flight path and each bar European flight path of each chromosome including unmanned plane quantity in group, the initial population Completed by different unmanned planes；

S3, determine that according to the kinematic parameter of the initial population, unmanned plane and wind field unmanned plane during flying state and unmanned plane are completed The hours underway of the flight path section of European flight path, is obtained according to the hours underway and MUAV-VS-EVRP models of flight path section The corresponding all unmanned planes of chromosome complete task time in initial population；

S4, based on genetic algorithm, chromosome in initial population is intersected, variation processing, and is reaching predetermined iterations Afterwards, all unmanned planes are chosen and complete task times most short chromosome as the optimal scheduling scheme of the unmanned plane.

2. according to the method described in claim 1, it is characterised in that according to the multiple unmanned plane and the multiple target point Positional information and default genetic algorithm, building initial population includes：

The initial population that chromosome coding generates pre-determined size is carried out according to the coded system of default genetic algorithm；The chromosome It is made up of target point information and unmanned machine information；Wherein described target point belongs to setT₀Represent UAVs's Starting point, N_TTarget point quantity is represented, unmanned plane belongs to setN_URepresent unmanned plane quantity；

The random fully intermeshing of target point described in the behavior of chromosome first, the second behavior is combined into each target according to unmanned plane collection Point randomly selects corresponding unmanned plane, and need to ensure that the unmanned plane in unmanned plane set is all at least chosen once.

3. method according to claim 1 or 2, it is characterised in that according to the fortune of the initial population, unmanned plane and wind field Dynamic parameter determines that unmanned plane during flying state and unmanned plane complete the hours underway of the flight path section of European flight path, according to the boat The hours underway and MUAV-VS-EVRP models of mark section obtain the corresponding all unmanned planes of chromosome in initial population and complete task Time includes：

The flight path is divided into multiple by each corresponding European flight path of chromosome according to the accessed order of its target point Flight path section；

According to the coordinate of the corresponding starting point of each flight path section and the coordinate of terminating point, determine that unmanned plane flies with reference to Wind parameters in wind Row state, and then obtain the hours underway that the unmanned plane completes the flight path section；

The corresponding all unmanned planes of the chromosome are obtained according to the corresponding hours underway of each flight path section and complete task time.

4. method according to claim 3, it is characterised in that according to the coordinate of the corresponding starting point of each flight path section and end The coordinate of stop, determines unmanned plane during flying state, and then obtain the unmanned plane completion flight path section with reference to Wind parameters in wind Hours underway includes：

Calculated using below equation and obtain unmanned plane U_iBy target point T_jSet out and fly to target point T_kThe hours underway of flight path section：

<mrow> <msubsup> <mi>t</mi> <mrow> <mo>(</mo> <msub> <mi>T</mi> <mi>j</mi> </msub> <mo>,</mo> <msub> <mi>T</mi> <mi>k</mi> </msub> <mo>)</mo> </mrow> <mi>i</mi> </msubsup> <mo>=</mo> <mfrac> <mrow> <msub> <mi>Length</mi> <mrow> <mo>(</mo> <msub> <mi>T</mi> <mi>j</mi> </msub> <mo>,</mo> <msub> <mi>T</mi> <mi>k</mi> </msub> <mo>)</mo> </mrow> </msub> </mrow> <mrow> <msup> <msub> <mi>V</mi> <mi>g</mi> </msub> <mi>i</mi> </msup> </mrow> </mfrac> <mo>,</mo> <mo>&ForAll;</mo> <msub> <mi>U</mi> <mi>i</mi> </msub> <mo>&Element;</mo> <mi>U</mi> <mo>,</mo> <mo>&ForAll;</mo> <msub> <mi>T</mi> <mi>j</mi> </msub> <mo>,</mo> <msub> <mi>T</mi> <mi>k</mi> </msub> <mo>&Element;</mo> <mi>T</mi> </mrow>

Wherein, U_iThe unmanned plane of the above-mentioned task of execution is represented, U represents unmanned plane set,^T _jFor starting point,_TkFor terminating point, T is represented The set of target point, V_g ⁱFor unmanned plane U_iGround velocity between above-mentioned two target point；

Calculated using below equation and obtain unmanned plane U_iGround velocity：

<mrow> <mfenced open = "(" close = ")"> <mtable> <mtr> <mtd> <mrow> <msup> <msub> <mi>cos&beta;</mi> <mi>g</mi> </msub> <mi>i</mi> </msup> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msup> <msub> <mi>sin&beta;</mi> <mi>g</mi> </msub> <mi>i</mi> </msup> </mrow> </mtd> </mtr> </mtable> </mfenced> <mo>&CenterDot;</mo> <msup> <msub> <mi>V</mi> <mi>g</mi> </msub> <mi>i</mi> </msup> <mo>=</mo> <mfenced open = "(" close = ")"> <mtable> <mtr> <mtd> <mrow> <msup> <msub> <mi>cos&beta;</mi> <mi>a</mi> </msub> <mi>i</mi> </msup> </mrow> </mtd> <mtd> <mrow> <msup> <msub> <mi>cos&beta;</mi> <mi>w</mi> </msub> <mi>i</mi> </msup> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msup> <msub> <mi>sin&beta;</mi> <mi>a</mi> </msub> <mi>i</mi> </msup> </mrow> </mtd> <mtd> <mrow> <msup> <msub> <mi>sin&beta;</mi> <mi>w</mi> </msub> <mi>i</mi> </msup> </mrow> </mtd> </mtr> </mtable> </mfenced> <mfenced open = "(" close = ")"> <mtable> <mtr> <mtd> <mrow> <msup> <msub> <mi>V</mi> <mi>a</mi> </msub> <mi>i</mi> </msup> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msup> <msub> <mi>V</mi> <mi>w</mi> </msub> <mi>i</mi> </msup> </mrow> </mtd> </mtr> </mtable> </mfenced> </mrow>

Wherein, V_a ⁱRepresent air speed size, β_a ⁱRepresent air speed course angle, V_g ⁱRepresent the size of ground velocity, β_g ⁱGround velocity course angle is represented,Wind speed size is represented,Represent wind direction；

<mrow> <msub> <mi>Length</mi> <mrow> <mo>(</mo> <msub> <mi>T</mi> <mi>j</mi> </msub> <mo>,</mo> <msub> <mi>T</mi> <mi>k</mi> </msub> <mo>)</mo> </mrow> </msub> <mo>=</mo> <mi>s</mi> <mi>q</mi> <mi>r</mi> <mi>t</mi> <mrow> <mo>(</mo> <msup> <mrow> <mo>(</mo> <mrow> <msub> <mi>X</mi> <msub> <mi>T</mi> <mi>j</mi> </msub> </msub> <mo>-</mo> <msub> <mi>X</mi> <msub> <mi>T</mi> <mi>k</mi> </msub> </msub> </mrow> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mo>+</mo> <msup> <mrow> <mo>(</mo> <mrow> <msub> <mi>Y</mi> <msub> <mi>T</mi> <mi>j</mi> </msub> </msub> <mo>-</mo> <msub> <mi>Y</mi> <msub> <mi>T</mi> <mi>k</mi> </msub> </msub> </mrow> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mo>)</mo> </mrow> <mo>,</mo> <mo>&ForAll;</mo> <msub> <mi>T</mi> <mi>j</mi> </msub> <mo>,</mo> <msub> <mi>T</mi> <mi>k</mi> </msub> <mo>&Element;</mo> <mi>T</mi> </mrow>

5. method according to claim 3, it is characterised in that according to the hours underway and MUAV-VS-EVRP of flight path section Chromosome corresponding all unmanned planes completion task times include in model acquisition initial population：

All unmanned planes are obtained according to MUAV-VS-EVRP models and complete task time：

<mrow> <mi>J</mi> <mo>=</mo> <mi>m</mi> <mi>a</mi> <mi>x</mi> <mo>{</mo> <munderover> <mo>&Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <msub> <mi>N</mi> <mi>U</mi> </msub> </munderover> <munderover> <mo>&Sigma;</mo> <mrow> <mi>j</mi> <mo>=</mo> <mn>0</mn> </mrow> <msub> <mi>N</mi> <mi>T</mi> </msub> </munderover> <munderover> <mo>&Sigma;</mo> <mrow> <mi>k</mi> <mo>=</mo> <mn>0</mn> </mrow> <msub> <mi>N</mi> <mi>T</mi> </msub> </munderover> <msubsup> <mi>X</mi> <mrow> <mo>(</mo> <msub> <mi>T</mi> <mi>j</mi> </msub> <mo>,</mo> <msub> <mi>T</mi> <mi>k</mi> </msub> <mo>)</mo> </mrow> <mi>i</mi> </msubsup> <msubsup> <mi>t</mi> <mrow> <mo>(</mo> <msub> <mi>T</mi> <mi>j</mi> </msub> <mo>,</mo> <msub> <mi>T</mi> <mi>k</mi> </msub> <mo>)</mo> </mrow> <mi>i</mi> </msubsup> <mo>}</mo> </mrow>

Its constraints is：

<mrow> <munderover> <mo>&Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <msub> <mi>N</mi> <mi>U</mi> </msub> </munderover> <munderover> <mo>&Sigma;</mo> <mrow> <mi>k</mi> <mo>=</mo> <mn>1</mn> </mrow> <msub> <mi>N</mi> <mi>T</mi> </msub> </munderover> <msubsup> <mi>X</mi> <mrow> <mo>(</mo> <msub> <mi>T</mi> <mi>j</mi> </msub> <mo>,</mo> <msub> <mi>T</mi> <mi>k</mi> </msub> <mo>)</mo> </mrow> <mi>i</mi> </msubsup> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mo>&ForAll;</mo> <msub> <mi>U</mi> <mi>i</mi> </msub> <mo>&Element;</mo> <mi>U</mi> <mo>,</mo> <mo>&ForAll;</mo> <msub> <mi>T</mi> <mi>j</mi> </msub> <mo>&Element;</mo> <mi>T</mi> </mrow>

<mrow> <munderover> <mo>&Sigma;</mo> <mrow> <mi>k</mi> <mo>=</mo> <mn>1</mn> </mrow> <msub> <mi>N</mi> <mi>T</mi> </msub> </munderover> <munderover> <mo>&Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <msub> <mi>N</mi> <mi>U</mi> </msub> </munderover> <msubsup> <mi>X</mi> <mrow> <mo>(</mo> <msub> <mi>T</mi> <mn>0</mn> </msub> <mo>,</mo> <msub> <mi>T</mi> <mi>k</mi> </msub> <mo>)</mo> </mrow> <mi>i</mi> </msubsup> <mo>=</mo> <munderover> <mo>&Sigma;</mo> <mrow> <mi>k</mi> <mo>=</mo> <mn>1</mn> </mrow> <msub> <mi>N</mi> <mi>T</mi> </msub> </munderover> <munderover> <mo>&Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <msub> <mi>N</mi> <mi>U</mi> </msub> </munderover> <msubsup> <mi>X</mi> <mrow> <mo>(</mo> <msub> <mi>T</mi> <mi>k</mi> </msub> <mo>,</mo> <msub> <mi>T</mi> <mn>0</mn> </msub> <mo>)</mo> </mrow> <mi>i</mi> </msubsup> <mo>=</mo> <msub> <mi>N</mi> <mi>U</mi> </msub> <mo>,</mo> <mo>&ForAll;</mo> <msub> <mi>U</mi> <mi>i</mi> </msub> <mo>&Element;</mo> <mi>U</mi> <mo>,</mo> <mo>&ForAll;</mo> <msub> <mi>T</mi> <mi>k</mi> </msub> <mo>&Element;</mo> <mi>T</mi> </mrow>

<mrow> <munderover> <mo>&Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <msub> <mi>N</mi> <mi>U</mi> </msub> </munderover> <munderover> <mo>&Sigma;</mo> <mrow> <mi>j</mi> <mo>=</mo> <mn>0</mn> </mrow> <msub> <mi>N</mi> <mi>T</mi> </msub> </munderover> <munderover> <mo>&Sigma;</mo> <mrow> <mi>k</mi> <mo>=</mo> <mn>0</mn> </mrow> <msub> <mi>N</mi> <mi>T</mi> </msub> </munderover> <msubsup> <mi>X</mi> <mrow> <mo>(</mo> <msub> <mi>T</mi> <mi>j</mi> </msub> <mo>,</mo> <msub> <mi>T</mi> <mi>k</mi> </msub> <mo>)</mo> </mrow> <mi>i</mi> </msubsup> <mo>=</mo> <msub> <mi>N</mi> <mi>T</mi> </msub> <mo>+</mo> <msub> <mi>N</mi> <mi>U</mi> </msub> <mo>,</mo> <mo>&ForAll;</mo> <msub> <mi>U</mi> <mi>i</mi> </msub> <mo>&Element;</mo> <mi>U</mi> <mo>,</mo> <mo>&ForAll;</mo> <msub> <mi>T</mi> <mi>j</mi> </msub> <mo>,</mo> <msub> <mi>T</mi> <mi>k</mi> </msub> <mo>&Element;</mo> <mi>T</mi> </mrow>

Wherein,Represent unmanned plane by target point T_jSet out and fly to target point T_kHours underway,It is a binary decision Variable, andWork as UAVU_iThrough T_jFly to T_kWhen, thenValue be 1, otherwiseValue be 0, N_TRepresent target The quantity of point, N_URepresent the quantity of unmanned plane.

6. method according to claim 4, it is characterised in that based on genetic algorithm, hand over chromosome in initial population Fork, variation processing, and after predetermined iterations is reached, choose all unmanned planes and complete task times most short chromosome conduct The optimal scheduling scheme of the unmanned plane includes：

Step 1, generate initial solution using the coding method, and generate the initial population of pre-determined size and according to each in population Chromosome corresponding unmanned plane completion task time calculates its fitness；

Step 2, intersected using two individuals (A, B) in roulette method choice parent population, crossover rule for first with Crossover location in machine selection individual A, then in lookup individual B with individual A crossover locations the first row identical gene, by chromosome Crossover location gene, which is replaced, in A and B obtains new chromosome C and D, judges whether chromosome C and D meet MUAV-VS- The constraints of EVRP models, if replacing chromosome A and B in population using chromosome C and D if meeting, otherwise to being unsatisfactory for about The chromosome of beam condition carries out constraint checking, i.e. when unmanned plane quantity is unsatisfactory for constraints in inspection chromosome A and B, for The chromosome of condition is unsatisfactory for, a gene position is randomly selected and judges that the coding of the unmanned plane in the gene position whether there is two And two or more, if the unmanned plane of missing then is encoded into the gene position, gene position is otherwise chosen again, and generation is met about The chromosome of beam condition replaces chromosome A and B in population, and then continuous iteration updates step 1 population, obtains new filial generation kind Group；

Step 3, using item chromosome in roulette method choice step 2 population enter row variation, the chromosome is become Different mode is at least one of following variation modes, including：Target point variation is carried out to chromosome the first row；To chromosome Second row carries out unmanned plane variation；

The step of whole chromosome makes a variation includes：First, if the first row order of chromosome makes a variation, current dyeing is randomly selected Two gene positions of body and the target point coding for exchanging correspondence gene position；Whether the row of reselection second makes a variation and variable position, if Make a variation the value replacement initial value having different from current location unmanned plane coding that then generation makes a variation at random, and the judgement dyeing after variation Whether body meets the constraints of MUAV-VS-EVRP models, if replacing chromosome in population if meeting, otherwise to being unsatisfactory for about The chromosome of beam condition carries out constraint checking, i.e., when unmanned plane quantity is unsatisfactory for constraints in inspection chromosome, for discontented The chromosome of sufficient condition, randomly selects a gene position and judges that the coding of the unmanned plane in the gene position whether there is two and two More than individual, if the unmanned plane of missing then is encoded into the gene position, gene position is otherwise chosen again, generation meets constraint bar The chromosome of part replaces the chromosome in population and constantly iteration updates step 2 population, obtains new progeny population；

Step 5, judge whether current iterations reaches preset value, if judgement is no, to the progeny population in step 3 and father Combine to form new parent population return to step 2 according to a certain percentage for population；If being judged as YES, terminate iteration, will be final The optimal solution of acquisition as unmanned plane task allocation result.

7. a kind of multiple no-manned plane performs the distributor of multitask, it is characterised in that including：

Acquisition module, the positional information for obtaining multiple unmanned planes and multiple target points, and the multiple unmanned plane and wind The kinematic parameter of field；

First processing module, is calculated for the positional information according to the multiple unmanned plane and the multiple target point and default heredity Each chromosome in method, structure initial population, the initial population includes the European flight path of unmanned plane quantity and each The European flight path of bar is completed by different unmanned planes；

Second processing module, for determining unmanned plane during flying shape according to the kinematic parameter of the initial population, unmanned plane and wind field State and unmanned plane complete the hours underway of the flight path section of European flight path, according to the hours underway and MUAV- of flight path section VS-EVRP models obtain the corresponding all unmanned planes of chromosome in initial population and complete task time；

3rd processing module, for based on genetic algorithm, being intersected to chromosome in initial population, variation processing, and up to To after predetermined iterations, choose all unmanned planes and complete task

Time most short chromosome as the unmanned plane optimal task assignment method.

8. device according to claim 7, it is characterised in that the first processing module, for being calculated according to default heredity The coded system of method carries out the initial population that chromosome coding generates pre-determined size；The chromosome is by target point information and nobody Machine information is constituted；Wherein described target point belongs to setT₀Represent UAVs starting point, N_TRepresent target point Quantity, unmanned plane belongs to setN_URepresent unmanned plane quantity；Target described in the behavior of chromosome first The random fully intermeshing of point, the second behavior is combined into each target point according to unmanned plane collection and randomly selects corresponding unmanned plane, and needs to protect The unmanned plane in unmanned plane set is demonstrate,proved all at least to be chosen once.

9. the device according to claim 7 or 8, it is characterised in that the Second processing module, for each chromosome pair The flight path is divided into multiple flight path sections by the European flight path answered according to the accessed order of its target point；Perform the first step Rapid and second step；

The first step includes：Calculated using below equation and obtain unmanned plane U_iBy target point T_jSet out and fly to target point T_kBoat The hours underway of mark section：

Wherein, U_iThe unmanned plane of the above-mentioned task of execution is represented, U represents unmanned plane set, T_jFor starting point, T_kFor terminating point, T tables Show the set of target point, V_g ⁱFor unmanned plane U_iGround velocity between above-mentioned two target point；

Calculated using below equation and obtain unmanned plane U_iGround velocity：

Wherein, X, Y represent correspondence target point horizontal stroke, ordinate respectively；

The second step includes：

Its constraints is：

10. device according to claim 9, it is characterised in that the 3rd processing module is used to perform following steps：