CN107037826A - Unmanned plane detection mission distribution method and device - Google Patents
Unmanned plane detection mission distribution method and device Download PDFInfo
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Abstract
The present invention relates to a kind of unmanned plane detection mission distribution method and device, perform the situation of several work task in this method to polylith region to be detected for a frame multi-rotor unmanned aerial vehicle, the area information to be detected and multi-rotor unmanned aerial vehicle information for performing this subtask are obtained first, default UAV O OP models and genetic algorithm are based on then according to this information, obtain the optimal solution for enabling to the model to obtain maximum total revenue, and the task distribution as this subjob and trajectory planning result using the optimal solution.The method that the present invention is provided can cause unmanned plane to perform job task automatically according to the result planned automatically, it is to avoid be influenceed by manual operation.In addition, because the method that the present invention is provided is to regard the optimal solution of default maximum gain model as trajectory planning result, therefore the unmanned plane that job task is performed based on the result also results in maximum total revenue while the task of execution, the most short time is spent, so as to effectively improve the efficiency of operation.
Description
Technical field
The present embodiments relate to unmanned air vehicle technique field, and in particular to a kind of unmanned plane detection mission distribution method and dress
Put.
Background technology
With continuing to develop for aeronautical technology, increasing high technology equipment is had been applied in aviation field.And
Among numerous high technology equipments, unmanned plane is so that its operating efficiency is high, labor intensity is small, the low aspect of integrated cost advantage, fast
Short-term training is a kind of more important high technology equipment in aviation operation process.Taken photo by plane or scanning imagery etc. is made for example, can perform
Industry task.Current unmanned plane can substantially be roughly divided into many rotors (such as four rotors, six rotors or eight rotor wing unmanned aerial vehicles)
And the major class of fixed-wing two.Wherein fixed-wing unmanned plane with flying distance length, area is big for cruise, flying speed is fast, highly high
Advantage is applied in aviation operation by relatively broad.
However, inventor has found in the practice of the invention, because current multi-rotor unmanned aerial vehicle operation is mainly people
Based on remote control, the effect of actual job is influenceed larger by the operation level of operator, and by way of artificially regarding
The course line of planning and theoretical deviated route are serious, cause the operation missing rate and repetitive rate of unmanned plane often higher.
In addition, when a frame multi-rotor unmanned aerial vehicle completes a kind of detection mission to polylith region to be detected, in the process
Because the flight duration of unmanned plane is limited, region to be detected and path planning how is selected to cause the income after completion task most
(the total revenue maximum i.e. as much as possible for completing regionally detecting task and completing all regions after regionally detecting), and in income greatly
The shortest scheme is selected on the basis of maximum and also becomes a urgent problem to be solved.
The content of the invention
It is existing for overcoming An embodiment provides a kind of unmanned plane detection mission distribution method and device
The navigation of unmanned plane is influenceed larger by manual operation in technology, and is utilizing a frame multi-rotor unmanned aerial vehicle to polylith area to be detected
Domain can not make rational planning for the flight path of unmanned plane during operation obtaining the defect that maximum total revenue spends the shortest time.
In a first aspect, An embodiment provides a kind of unmanned plane detection mission distribution method, when a frame is more
Rotor wing unmanned aerial vehicle performs a variety of detection missions to polylith rectangle region to be detected, and methods described includes：
Obtain area information to be detected and multi-rotor unmanned aerial vehicle information；
The initial solution for meeting default UAV-O-OP models constraints is obtained, wherein, the UAV-O-OP models are many
Rotor wing unmanned aerial vehicle obtains the maximum object function of total revenue in this detection mission；The constraints include many rotors nobody
The flight duration constraint of machine institute；
Optimal solution is obtained to the UAV-O-OP model solutions based on the initial solution using default genetic algorithm, and will
The optimal solution is used as task allocation result of the frame multi-rotor unmanned aerial vehicle to polylith region to be detected.
Second aspect, a kind of unmanned plane detection mission distributor of another embodiment of the invention, when many rotors of a frame
Unmanned plane performs a variety of detection missions to polylith rectangle region to be detected, and described device includes：
Information acquisition unit, for obtaining area information to be detected and multi-rotor unmanned aerial vehicle information；
Initial solution acquiring unit, the initial solution of default UAV-O-OP models constraints is met for obtaining, wherein, institute
State UAV-O-OP models and the maximum object function of total revenue is obtained in this detection mission for multi-rotor unmanned aerial vehicle；The constraint
Condition includes the flight duration constraint of multi-rotor unmanned aerial vehicle institute；
Optimal solution computing unit, for being based on the initial solution to the UAV-O-OP models using default genetic algorithm
Solution obtains optimal solution, and distributes knot to the task in polylith region to be detected as a frame multi-rotor unmanned aerial vehicle using the optimal solution
Really.
It is many that a frame is directed to An embodiment provides a kind of unmanned plane detection mission distribution method, in this method
Rotor wing unmanned aerial vehicle performs the situation of several work task to polylith region to be detected, obtains perform the to be detected of this subtask first
Area information and multi-rotor unmanned aerial vehicle information, are based on default UAV-O-OP models then according to this information and heredity are calculated
Method, obtains the optimal solution for enabling to the model to obtain maximum total revenue, and divide the optimal solution as the task of this subjob
With with trajectory planning result.Compared to the mode of existing manual remote control, the method that the present invention is provided can be according to default mould
Type and algorithm automatically obtain the task and trajectory planning of unmanned plane in this subjob so that unmanned plane can according to the task with
And trajectory planning performs job task automatically, it is to avoid influenceed by manual operation.Further, since the method that the present invention is provided is
Using the optimal solution of default maximum gain model as trajectory planning result, therefore based on the nothing of result execution job task
It is man-machine also to result in maximum total revenue while the task of execution, the most short time is spent, so as to effectively improve work
The efficiency of industry so that unmanned plane operation form can be applied in wider detection mission.
Brief description of the drawings
By reading the detailed description of hereafter preferred embodiment, various other advantages and benefit is common for this area
Technical staff will be clear understanding.Accompanying drawing is only used for showing the purpose of preferred embodiment, and is not considered as to the present invention
Limitation.And in whole accompanying drawing, identical part is denoted by the same reference numerals.In the accompanying drawings：
Fig. 1 is a kind of embodiment of the method flow chart for unmanned plane detection mission distribution that the present invention is provided；
Fig. 2 (a) -2 (c) is the regionally detecting schematic diagram to be detected that the present invention is provided；
Fig. 3 is the inlet point position view that the present invention is provided；
Fig. 4 is the chiasma process schematic that the present invention is provided；
Fig. 5 is the regular schematic diagram of chromosomal variation that the present invention is provided；
Fig. 6 is 5 area schematics to be detected that the present invention is provided；
Fig. 7 is the optimal solution convergence schematic diagram that the present invention is provided；
Fig. 8 is a kind of unmanned plane detection mission assigned unit example structure schematic diagram that the present invention is provided.
Embodiment
Below in conjunction with the accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is carried out clear, complete
Site preparation is described, it is clear that described embodiment is only a part of embodiment of the invention, rather than whole embodiments.It is based on
Embodiment in the present invention, it is every other that those of ordinary skill in the art are obtained under the premise of creative work is not made
Embodiment, belongs to the scope of protection of the invention.
In a first aspect, the embodiments of the invention provide a kind of unmanned plane detection mission distribution method, when many rotors of a frame without
During the man-machine a variety of detection missions of execution to polylith rectangle region to be detected, as shown in figure 1, methods described includes：
S101, acquisition area information to be detected and multi-rotor unmanned aerial vehicle information；
S102, acquisition meet the initial solution of default UAV-O-OP models constraints, wherein, the UAV-O-OP models
The maximum object function of total revenue is obtained in this detection mission for multi-rotor unmanned aerial vehicle；The constraints includes many rotors
The flight duration constraint of unmanned plane institute；
S103, using default genetic algorithm based on the initial solution optimal is obtained to the UAV-O-OP model solutions
Solution, and it regard the optimal solution as task allocation result of the multi-rotor unmanned aerial vehicle to polylith region to be detected.
It is many that a frame is directed to An embodiment provides a kind of unmanned plane detection mission distribution method, in this method
Rotor wing unmanned aerial vehicle performs the situation of several work task to polylith region to be detected, obtains perform the to be detected of this subtask first
Area information and multi-rotor unmanned aerial vehicle information, are based on default UAV-O-OP models then according to this information and heredity are calculated
Method, obtains the optimal solution for enabling to the model to obtain maximum total revenue, and divide the optimal solution as the task of this subjob
With with trajectory planning result.Compared to the mode of existing manual remote control, the method that the present invention is provided can be according to default mould
Type and algorithm automatically obtain the task and trajectory planning of unmanned plane in this subjob so that unmanned plane can according to the task with
And trajectory planning performs job task automatically, it is to avoid influenceed by manual operation.Further, since the method that the present invention is provided is
Using the optimal solution of default maximum gain model as trajectory planning result, therefore based on the nothing of result execution job task
It is man-machine also to result in maximum total revenue while the task of execution, the most short time is spent, so as to effectively improve work
The efficiency of industry so that unmanned plane operation form can be applied in wider detection mission.
In the specific implementation, it is to be understood that object function that the UAV-O-OP models in the above method are included and
Constraints is the important evidence that the present invention results in optimum programming result, and it can be set in several ways, below
The optional set-up mode of one of which is described in detail.
The UAV-O-OP models are an orientation problems (OP).Multi-rotor unmanned aerial vehicle self performance, region to be detected
Influence also is produced on the result that task is distributed in terms of path when size performs task with multi-rotor unmanned aerial vehicle.In concrete model
Design parameter and set it is as follows：
(1) multi-rotor unmanned aerial vehicle
Represent to perform a frame multi-rotor unmanned aerial vehicle of task to be detected with U；This unmanned plane is from same starting point, and most
The starting point is returned to eventually.In flight course, the flying speed of multi-rotor unmanned aerial vehicle is V, and it is R to carry radius of investigationD's
Sensor.
With reference to multi-rotor unmanned aerial vehicle perform detection mission the characteristics of, make herein it is assumed hereinafter that：
(1) multi-rotor unmanned aerial vehicle is respectively provided with the ability of automatic obstacle-avoiding, can be in the case of collision is faced, using independently evading
Control strategy, the Path error produced therefrom relative to total flight path length also very little, can be neglected；
(2) multi-rotor unmanned aerial vehicle is flown with identical cruising speed and cruising altitude, so as to not consider other factors
Optimal Effect on Detecting is reached during influence；
(3) influence of the external environment to multi-rotor unmanned aerial vehicle flight path is not considered in multi-rotor unmanned aerial vehicle flight course；
(4) fuel is limited in multi-rotor unmanned aerial vehicle flight course；
(2) region to be detected
IfThe respectively beginning and end of multi-rotor unmanned aerial vehicle, beginning and end herein is identical；For NABlock region to be detected, and region A to be detectediIt is that apex coordinate is (Ai1,Ai2,Ai3,Ai4) area be Si
Rectangle；Starting point, terminal and the collection in region to be detected of multi-rotor unmanned aerial vehicle are combined intoWhen many rotations
Wing unmanned plane treats search coverage AiWhen cover type is scanned, the inlet point that multi-rotor unmanned aerial vehicle flies into region to be detected is Ini, fly
It is a little Out from leaving for region to be detectedi, and assume that the multi-rotor unmanned aerial vehicle must be detected behind monoblock region to be detected just completely
It can leave.At the same time, each region to be detected can only be at most detected once.
(3) flight path
During multi-rotor unmanned aerial vehicle performs detection mission, not only need inside region to be detected by cover type
Scanning completes job task, and also needs in difference interregional flight to be detected to realize the switching between task, therefrom
Generate the flight path in two kinds of flight path, i.e., to be detected interregional and to be detected region.
(1) multi-rotor unmanned aerial vehicle is in interregional flight path to be detected：
In two pieces of region A to be detectedi,AjBetween, the path length of multi-rotor unmanned aerial vehicle flight is Euclidean distance length.And
Multi-rotor unmanned aerial vehicle is in two pieces of region A to be detectedi,AjBetween cost time be tij。
(2) flight path multi-rotor unmanned aerial vehicle operator of the multi-rotor unmanned aerial vehicle in region to be detected during cover type scanning
Formula：
In region A to be detectediInside, multi-rotor unmanned aerial vehicle carries out path planning using parallel sweep strategy.In cover type
Multi-rotor unmanned aerial vehicle is from region A to be detected during scanningiIniPoint enters, into the path behind region to be detected parallel to be detected
Region side, then from OutiPoint leaves, now, and the cost time in multi-rotor unmanned aerial vehicle detection scanning region to be detected is ti。
The detection scanning time of multi-rotor unmanned aerial vehicle depends on the number of times turned under given speed, for Fig. 2 (c) region to be detected
Just there are two different radius of turn number of times, shown in such as Fig. 2 (a) and Fig. 2 (b), the wherein turning in flight path shown in Fig. 2 (b)
Number of times is fewer than flight path shown in Fig. 2 (a), and total path length is also fewer than Fig. 2 (a).
Multi-rotor unmanned aerial vehicle is used during parallel sweep strategy execution regionally detecting task, it is necessary to first determine to enter area to be detected
The point and approach axis in domain.The point that multi-rotor unmanned aerial vehicle enters region to be detected can be arbitrfary point, but when inlet point distance
Apex distance in region to be detected is RDWhen multi-rotor unmanned aerial vehicle number of turns it is minimum, total path is most short.Due to treating for this paper
Search coverage is rectangle, then is R apart from summitDPoint have eight (as shown in Figure 3), be respectively { RD1,RD2...RD8, so
Multi-rotor unmanned aerial vehicle enters the inlet point In in region to be detectediThere are eight kinds of possibility, and approach axis is where the point
Side.And can be uniquely determined and left a little by inlet point, because when the radius of turn of multi-rotor unmanned aerial vehicle is determined, sweep radius is true
It is fixed, determined into region direction to be detected, when the region length of side to be detected is determined, then number of turns is determined, multi-rotor unmanned aerial vehicle from
Open the direction in region to be detected and leave point OutiIt is to determine.
But not only need to consider the cost time inside region to be detected treating when search coverage carries out detection scanning
Also need to consider cost time between region to be detected, it is necessary to the balanced time between the two, so no longer to be spent
Time is criterion, but using the income in region to be detected that detected as standard.
Therefore, for a kind of described unmanned plane detection mission assignment problem, complete to appoint herein with multi-rotor unmanned aerial vehicle
Total revenue after business maximizes the object function as optimization problem, sets up following mathematical modeling.
The object function of the UAV-O-OP models is：
The constraints of the UAV-O-OP models is：
In UAV-O-OP models, NARepresent region A to be detectediNumber；Represent rising for multi-rotor unmanned aerial vehicle
Initial point and terminal, the starting point are same point with terminal；SiRepresent region A to be detectediArea；PiRepresent to complete to be detected
Region AiThe income that is obtained of task；tiRepresent multi-rotor unmanned aerial vehicle to search coverage AiAppoint according to parallel sweep strategy execution
The time of business；tijRepresent multi-rotor unmanned aerial vehicle in region A to be detectedi,AjBetween time for flying；E represents multi-rotor unmanned aerial vehicle
Maximum flight duration limitation；uiRepresent region A to be detectediPosition in route；xiRepresent that multi-rotor unmanned aerial vehicle treats detecting area
Domain AiThe situation of completion task, if xi=1, then it represents that complete detection mission, otherwise multi-rotor unmanned aerial vehicle does not treat search coverage
AiExecution task；yijRepresent whether multi-rotor unmanned aerial vehicle passes through region A to be detectedi,AjIf, yij=1 represents multi-rotor unmanned aerial vehicle
By region A to be detectedi,Aj, otherwise the multi-rotor unmanned aerial vehicle is not by region A to be detectedi,Aj。
Wherein, target function type (1) is maximum for the total revenue obtained after multi-rotor unmanned aerial vehicle execution task；Constraint formula (2)
Be in order to ensure the path starting point of multi-rotor unmanned aerial vehicle be A0, terminal isConstraint formula (3) is to ensure that each area to be detected
Domain is at most accessed once；Constraint formula (4) is to ensure that the maximum flight duration of multi-rotor unmanned aerial vehicle no more than E；Constraint formula (5)
It is to prevent the formation sub-loop of unmanned plane route with formula (6)；Constraint formula (7) is the definition of target, path variable.
Understandable to be, after UAV-O-OP models are obtained, method provided in an embodiment of the present invention can basis
The optimal solution of default genetic algorithm for solving UAV-O-OP models.Wherein this default genetic algorithm for asking for optimal solution can lead to
Accomplished in many ways is crossed, the optional mode of one of which is described in detail below.
The general thought of method provided in an embodiment of the present invention is：Task distribution to be solved for the embodiment of the present invention
For problem, each feasible solution (namely meeting the solution of preset model constraint) can be expressed as item chromosome.Feasible solution kind
Group (namely initial parent population) can be by a plurality of genome into its scale is according to actual conditions self-defining.Obtaining this
After the initial parent population of sample, so can by initial parent population by the intersection of chromosome, making a variation is updated population,
Form new progeny population.Wherein, intersection here refers to that two parent chromosomes form two new strips according to crossover probability
For chromosome, variation here refers to that item chromosome forms a new chromosome according to mutation probability.This cross and variation
The continuous iteration of cyclic process of renewal, finally selects current optimal child chromosome when iterations reaches preset value, should
Child chromosome is the optimal solution for enabling to object function to obtain maximum gain for meeting model constraint, and the optimal solution is
For task allocation result of the present invention needed for final.
And in this course, it is related to the function for the coding in genetic algorithm, intersection, variation and fitness
The genetic algorithm being configured so that after setting of rule can be applied to the solution to preset model and obtain in optimal solution.Can be with
Understand, the setting of each function in genetic algorithm can there are ways to realize, below to a kind of optional function
Set-up mode is specifically described.
(1) encode
Coding in the present invention includes region to be detected, whether performs region task to be detected, region inlet point to be detected,
Wherein, region to be detected belong to set 1,2 ... NA, the inlet point in region to be detected belongs to set { RD1,RD2,...RD8}。
For example, table 1 give one coding after chromosome per a line content.Wherein, chromosome the first row is to treat
The information of search coverage namely the identification information in region to be detected, the second row are to represent whether unmanned plane performs region to be detected and appoint
Business, 1 indicates, 0 indicates without the third line is that the inlet point identification information that unmanned plane is treated when search coverage performs task (enters
Piont mark corresponds to the region R to be detected shown in Fig. 2D1-RD8).Whole chromosome represents multi-rotor unmanned aerial vehicle first from RD7Inlet point
Into region A to be detected3Completion task, then from RD5Point enters region A to be detected5Completion task, then from RD8Point, which enters, to be waited to visit
Survey region A4Completion task, finally returns to starting point, and target A1,A2It is not accessed.
The chromosome of table 1:NA=5
| Region to be detected | 3 | 1 | 5 | 4 | 2 |
| Whether task is performed | 1 | 0 | 1 | 1 | 0 |
| Inlet point | 7 | 1 | 5 | 8 | 6 |
(2) intersect
The interleaved mode of selection of the embodiment of the present invention is two crossover locations for first randomly choosing the first chromosome, is then sought
Look for the first row identical gene with the first chromosome crossover location in the second chromosome；By the first chromosome and the second chromosome
Crossover location gene be replaced, so as to obtain trisome and tetrasome；
For example in Fig. 4, two parent chromosomes first randomly choose 2 positions intersected in parent A, then look for
Swapped to parent B same target regional locations, so as to obtain two new child chromosome A, B.
(3) make a variation
Variation is probably that a gene is also likely to be multiple genes in the present invention, and this paper chromosomal variations mainly have following several
The situation of kind：Zone sequence variation to be detected, if having multi-rotor unmanned aerial vehicle to perform task variation, region inlet point to be detected becomes
It is different.Wherein, if the first row morphs, random fully intermeshing is carried out to the first row, if the second row morphs, definitive variation position
Put, and become have multi-rotor unmanned aerial vehicle execution task without multi-rotor unmanned aerial vehicle execution task by original, if the or the on the contrary, the 3rd
Row morphs, and determines the position of its variation, and the inlet point generated at random is replaced into the inlet point at former variable position；
For example, chromosome A has carried out three kinds of variations in Fig. 5, region access order to be detected is 4 by 3,1,5,4,2 variations,
2,1,3,5, the second row the 3rd row are changed into the region task to be detected that 0 expression unmanned plane performed originally from 1 and not performed, the third line the
The inlet point of one row is by RD7Become RD2。
(4) fitness function and selection
The fitness of the chromosome is：
Wherein, NARepresent region A to be detectediNumber；SiRepresent region A to be detectediArea；PiRepresent to complete to be detected
Region AiThe income that is obtained of task；If xi=1, then it represents that complete detection mission, otherwise the multi-rotor unmanned aerial vehicle is not treated
Search coverage AiExecution task；
Wherein, 0 ... NA+1Represent starting point, region to be detected and terminating point；tiRepresent multi-rotor unmanned aerial vehicle to detecting area
Domain AiAccording to the time of parallel sweep strategy execution task；tijRepresent multi-rotor unmanned aerial vehicle in region A to be detectedi,AjBetween fly
Time；xiRepresent that multi-rotor unmanned aerial vehicle completes region A to be detectediThe situation of completion task, if xi=1, then it represents that complete detection
Task, otherwise the multi-rotor unmanned aerial vehicle do not treat search coverage AiExecution task；yijRepresent whether multi-rotor unmanned aerial vehicle passes through
Region A to be detectedi,AjIf, yij=1 represents that multi-rotor unmanned aerial vehicle passes through region A to be detectedi,Aj, the otherwise multi-rotor unmanned aerial vehicle
Do not pass through region A to be detectedi,Aj。
There are two fitness functions in the embodiment of the present invention, the first fitness is formula (8), is the bigger the better, and represents all quilts
Access the total revenue in region to be detected, i.e., it is relevant with the area in accessed region and region.And when the first of a plurality of chromosome
When fitness is identical, i.e. the different total revenue of allocative decision is identical, but the cost time of each allocative decision be it is different, because
This needs the calculating (as shown in formula (9)) for carrying out the second fitness, carries out postsearch screening, so that total revenue maximum is selected, and
The shortest allocative decision on the basis of total revenue is maximum.
Because the constantly loop iteration of above-mentioned cross and variation process is carried out so that parent population is thus continually updated, so that
The more new populations of generation.It is understood that the process of this iterative cycles can infinitely go on, but so
A final result can not be obtained.Therefore whether the iterations of the invention that can judge currently to add up after each iteration terminates
Iterations threshold value is reached, wherein this threshold value can voluntarily be set according to actual conditions.If judging current be not up to
Iterations threshold value, then need to proceed iterative process；If judgement has currently reached iterations threshold value, then it is assumed that now
Iterations is enough, and current optimal solution may act as the result of the task distribution of this subjob.And then can also be by
The result is distributed to corresponding frame multi-rotor unmanned aerial vehicle, to allow this unmanned plane to perform this work according to this result
Industry task, reaches the purpose of this subjob and obtains the maximum gain in region to be detected.
To embody the superiority of method provided in an embodiment of the present invention, several specific examples are named, are described in detail such as
What utilizes solution of the genetic algorithm to UAV-O-OP models according to above-mentioned function setup, so as to obtain final task distribution knot
Really.
Specifically, solution of the genetic algorithm to UAV-O-OP models is realized in MATLAB 2013 environment,
And tested.
Assuming that there is 1 frame unmanned plane to perform task to five pieces of regions to be detected, and distribution side is obtained using the genetic algorithm
Case, wherein the crossover probability for taking the genetic algorithm is 0.9, mutation probability is 0.5, and population scale is 500, and iterations is
100.The design parameter being related in experimentation is described as follows：
(1) unmanned plane
As shown in table 2, unmanned plane speed is 4m/s, maximum probe radius to the concrete configuration of unmanned plane in this paper experiment
For 5m, max-endurance is 1800s.
The unmanned plane basic parameter allocation list of table 2
| Unmanned plane parameter | A0\AN+1 | V | RD | E |
| Unmanned machine information | (0,0) | 4m/s | 5m | 1800s |
(2) region to be detected
There are five pieces of regions to be detected, it is specific as shown in Figure 6.Specific coordinate and income are as shown in table 3.
The area coordinate information to be detected of table 3
| Coordinate | Bottom left vertex | Left upper apex | Right vertices | Bottom right vertex | Income |
| Region 1 | (100,100) | (100,200) | (200,200) | (200,100) | 0.9 |
| Region 2 | (10,410) | (10,560) | (110,560) | (110,410) | 0.94 |
| Region 3 | (350,10) | (350,110) | (540,110) | (540,10) | 0.87 |
| Region 4 | (150,300) | (150,400) | (260,400) | (260,300) | 0.89 |
| Region 5 | (350,350) | (350,480) | (450,480) | (450,350) | 0.97 |
The income of the optimal solution obtained using the genetic algorithm to above-mentioned scene is 4.9420, and has been restrained in the 4th generation,
Convergence rate is very fast, specific as shown in Figure 7.The optimal distributing scheme such as table 4 of shortest time is spent in the case of Income Maximum
It is shown, and the most short cost time is 1735.5s.And in all regions to be detected, region 5 is not detected, other regions
It is detected.
The optimal distributing scheme of table 4
| Region | 3 | 4 | 5 | 2 | 1 |
| Tasks carrying | 1 | 1 | 0 | 1 | 1 |
| Inlet point | 2 | 8 | 7 | 8 | 3 |
Second aspect, one embodiment of the present of invention additionally provides a kind of unmanned plane detection mission distributor, when a frame
Multi-rotor unmanned aerial vehicle performs a variety of detection missions to polylith rectangle region to be detected, as shown in figure 8, described device includes：
Information acquisition unit 201, for obtaining area information to be detected and multi-rotor unmanned aerial vehicle information；
Initial solution acquiring unit 202, the initial solution of default UAV-O-OP models constraints is met for obtaining, its
In, the UAV-O-OP models are that multi-rotor unmanned aerial vehicle obtains the maximum object function of total revenue in this detection mission；Institute
Stating constraints includes the flight duration constraint of multi-rotor unmanned aerial vehicle institute；
Optimal solution computing unit 203, for being based on the initial solution to the UAV-O-OP using default genetic algorithm
Model solution obtains optimal solution, and distributes knot to the task in polylith region to be detected as the multi-rotor unmanned aerial vehicle using the optimal solution
Really.
In the specific implementation, it is characterised in that：
The object function of the UAV-O-OP models is：
The constraints of the UAV-O-OP models is：
In UAV-O-OP models, NARepresent region A to be detectediNumber；Represent rising for multi-rotor unmanned aerial vehicle
Initial point and terminal, the starting point are same point with terminal；SiRepresent region A to be detectediArea；PiRepresent to complete to be detected
Region AiThe income that is obtained of task；tiRepresent multi-rotor unmanned aerial vehicle to search coverage AiHeld according to parallel sweep flying method
The time of row task；tijRepresent multi-rotor unmanned aerial vehicle in region A to be detectedi,AjBetween time for flying；E represent many rotors without
Man-machine maximum flight duration limitation；uiRepresent region A to be detectediPosition in route；xiRepresent that multi-rotor unmanned aerial vehicle treats spy
Survey region AiThe situation of completion task, if xi=1, then it represents that complete detection mission, otherwise multi-rotor unmanned aerial vehicle is not to be detected
Region AiExecution task；yijRepresent whether multi-rotor unmanned aerial vehicle passes through region A to be detectedi,AjIf, yij=1 represent many rotors without
It is man-machine to pass through region A to be detectedi,Aj, otherwise the multi-rotor unmanned aerial vehicle is not by region A to be detectedi,Aj；
Wherein, the parallel sweep flying method is：With perpendicular to region first to be detected while direction from first while on
The first inlet point enter region to be detected, the distance on first inlet point and nearest region summit to be detected is many rotors
Unmanned plane radius of investigation, first side is any one side in region to be detected；Used inside region to be detected parallel to treating
The mode on one side of search coverage is flown.
In the specific implementation, the initial solution acquiring unit 202 is further used for：
The area information to be detected, multi-rotor unmanned aerial vehicle information are encoded, a plurality of chromosome is generated at random；
Wherein, the random fully intermeshing of the identification information in region to be detected described in the first behavior of a plurality of chromosome, institute
The second row for stating a plurality of chromosome represents whether multi-rotor unmanned aerial vehicle performs region task to be detected, the of a plurality of chromosome
Three behavior multi-rotor unmanned aerial vehicles enter the random combine of the inlet point in region to be detected.
In the specific implementation, the optimal solution computing unit, is further used for performing following steps：
Step 1: generating the initial parent population of default scale according to the initial solution, and calculate every dyeing in population
The fitness of body；
Step 2: carrying out crossover operation to chromosome in parent population obtains first generation progeny population, the step of the intersection
Suddenly specifically include：
Randomly choose the first chromosome in two crossover locations, then look in the second chromosome with the first chromosome hand over
The first row identical gene that vent is put；The crossover location gene of the first chromosome and the second chromosome is replaced, so that
Obtain trisome and tetrasome；Judge whether the trisome and tetrasome meet described default
Constraints；If meeting, the first chromosome and the second chromosome in the parent population are replaced；If it is not satisfied, then tying
Beam current operation；
Step 3: carrying out mutation operation to chromosome in first generation progeny population obtains second generation progeny population, the change
Different step is specifically included：
Randomly choose the 5th chromosome and carry out mutation operation, if the first row morphs, the first row is carried out random complete
Arrangement, if the second row morphs, definitive variation position, and become have many rotations without multi-rotor unmanned aerial vehicle execution task by original
Wing unmanned plane performs task, if or on the contrary, the third line morphs, determine the position of its variation, and by entering for generating at random
Access point replaces the inlet point at former variable position；
Step 4: obtain the optimal solution in the second filial generation population according to the fitness function, and by described second
Progeny population combines to form new parent population with the parent population according to preset ratio；
Judge whether the number of times of the overall loop iteration of current procedures two, three, four reaches preset value；If it is not, then return to step
Two, and perform step 2 using the new parent population as current parent population；If so, then performing step 5；
Step 5：Terminate iteration, and using the optimal solution finally obtained as this subtask allocation result.
In the specific implementation, the fitness of the chromosome is：
Wherein, NARepresent region A to be detectediNumber；SiRepresent region A to be detectediArea；PiRepresent to complete to be detected
Region AiThe income that is obtained of task；If xi=1, then it represents that complete detection mission, otherwise the multi-rotor unmanned aerial vehicle is not treated
Search coverage AiExecution task；
Wherein, 0 ... NA+1Represent starting point, region to be detected and terminating point；tiRepresent multi-rotor unmanned aerial vehicle to detecting area
Domain AiAccording to the time of parallel sweep strategy execution task；tijRepresent multi-rotor unmanned aerial vehicle in region A to be detectedi,AjBetween fly
Time；xiRepresent that multi-rotor unmanned aerial vehicle completes region A to be detectediThe situation of completion task, if xi=1, then it represents that complete detection
Task, otherwise the multi-rotor unmanned aerial vehicle do not treat search coverage AiExecution task；yijRepresent whether multi-rotor unmanned aerial vehicle passes through
Region A to be detectedi,AjIf, yij=1 represents that multi-rotor unmanned aerial vehicle passes through region A to be detectedi,Aj, the otherwise multi-rotor unmanned aerial vehicle
Do not pass through region A to be detectedi,Aj。
By the unmanned plane detection mission assigned unit that the present embodiment is introduced is to perform in the embodiment of the present invention
The distribution of unmanned plane detection mission method device, so based on the unmanned plane detection mission described in the embodiment of the present invention
The method of distribution, those skilled in the art can understand the specific of the unmanned plane detection mission assigned unit of the present embodiment
Embodiment and its various change form, so how to realize this hair for the unmanned plane detection mission assigned unit herein
The method of unmanned plane detection mission distribution in bright embodiment is no longer discussed in detail.As long as those skilled in the art implement this
The device that the method that unmanned plane detection mission is distributed in inventive embodiments is used, belongs to the scope to be protected of the application.
In the specification that this place is provided, numerous specific details are set forth.It is to be appreciated, however, that the implementation of the present invention
Example can be put into practice in the case of these no details.In some instances, known method, structure is not been shown in detail
And technology, so as not to obscure the understanding of this description.
Similarly, it will be appreciated that in order to simplify the disclosure and help to understand one or more of each inventive aspect, exist
Above in the description of the exemplary embodiment of the present invention, each feature of the invention is grouped together into single implementation sometimes
In example, figure or descriptions thereof.However, the method for the disclosure should be construed to reflect following intention：It is i.e. required to protect
The application claims of shield features more more than the feature being expressly recited in each claim.More precisely, such as following
Claims reflect as, inventive aspect is all features less than single embodiment disclosed above.Therefore,
Thus the claims for following embodiment are expressly incorporated in the embodiment, wherein each claim is in itself
All as the separate embodiments of the present invention.
Those skilled in the art, which are appreciated that, to be carried out adaptively to the module in the equipment in embodiment
Change and they are arranged in one or more equipment different from the embodiment.Can be the module or list in embodiment
Member or component be combined into a module or unit or component, and can be divided into addition multiple submodule or subelement or
Sub-component.In addition at least some in such feature and/or process or unit exclude each other, it can use any
Combination is disclosed to all features disclosed in this specification (including adjoint claim, summary and accompanying drawing) and so to appoint
Where all processes or unit of method or equipment are combined.Unless expressly stated otherwise, this specification (including adjoint power
Profit is required, summary and accompanying drawing) disclosed in each feature can or similar purpose alternative features identical, equivalent by offer come generation
Replace.
Although in addition, it will be appreciated by those of skill in the art that some embodiments in this include institute in other embodiments
Including some features rather than further feature, but not the combination of the feature of be the same as Example mean be in the scope of the present invention
Within and form different embodiments.For example, in the following claims, embodiment claimed it is any it
One mode can use in any combination.
The present invention some unit embodiments can be realized with hardware, or with one or more processor run
Software module realize, or realized with combinations thereof.It will be understood by those of skill in the art that can use in practice
Microprocessor or digital signal processor (DSP) are realized in gateway according to embodiments of the present invention, proxy server, system
Some or all parts some or all functions.The present invention is also implemented as being used to perform side as described herein
The some or all equipment or program of device (for example, computer program and computer program product) of method.It is such
Realizing the program of the present invention can store on a computer-readable medium, or can have the shape of one or more signal
Formula.Such signal can be downloaded from internet website and obtained, and either be provided or with any other shape on carrier signal
Formula is provided.
It should be noted that the present invention will be described rather than limits the invention for above-described embodiment, and ability
Field technique personnel can design alternative embodiment without departing from the scope of the appended claims.In the claims,
Any reference symbol between bracket should not be configured to limitations on claims.Word "comprising" is not excluded the presence of not
Element or step listed in the claims.Word "a" or "an" before element does not exclude the presence of multiple such
Element.The present invention can be by means of including the hardware of some different elements and coming real by means of properly programmed computer
It is existing.In if the unit claim of equipment for drying is listed, several in these devices can be by same hardware branch
To embody.The use of word first, second, and third does not indicate that any order.These words can be explained and run after fame
Claim.
Claims (10)
1. a kind of unmanned plane detection mission distribution method, it is characterised in that when a frame multi-rotor unmanned aerial vehicle is waited to visit to polylith rectangle
Survey region and perform a variety of detection missions, methods described includes：
Obtain area information to be detected and multi-rotor unmanned aerial vehicle information；
The initial solution for meeting default UAV-O-OP models constraints is obtained, wherein, the UAV-O-OP models are many rotors
Unmanned plane obtains the maximum object function of total revenue in this detection mission；The constraints includes multi-rotor unmanned aerial vehicle institute
Flight duration is constrained；
Optimal solution is obtained to the UAV-O-OP model solutions based on the initial solution using default genetic algorithm, and by this most
Excellent solution is used as task allocation result of the multi-rotor unmanned aerial vehicle to polylith region to be detected.
2. according to the method described in claim 1, it is characterised in that：
The object function of the UAV-O-OP models is：
The constraints of the UAV-O-OP models is：
In UAV-O-OP models, NARepresent region A to be detectediNumber；A0,Represent multi-rotor unmanned aerial vehicle starting point and
Terminal, the starting point is same point with terminal；SiRepresent region A to be detectediArea；PiRepresent to complete region A to be detectedi
The income that is obtained of task；tiRepresent multi-rotor unmanned aerial vehicle to search coverage AiTask is performed according to parallel sweep flying method
Time；tijRepresent multi-rotor unmanned aerial vehicle in region A to be detectedi,AjBetween time for flying；E represents multi-rotor unmanned aerial vehicle most
Big flight duration limitation；uiRepresent region A to be detectediPosition in route；xiRepresent that multi-rotor unmanned aerial vehicle treats search coverage
AiThe situation of completion task, if xi=1, then it represents that complete detection mission, otherwise multi-rotor unmanned aerial vehicle does not treat search coverage Ai
Execution task；yijRepresent whether multi-rotor unmanned aerial vehicle passes through region A to be detectedi,AjIf, yij=1 represents multi-rotor unmanned aerial vehicle warp
Cross region A to be detectedi,Aj, otherwise the multi-rotor unmanned aerial vehicle is not by region A to be detectedi,Aj。
Wherein, the parallel sweep flying method is：With perpendicular to region first to be detected while direction from first while on
One inlet point enters region to be detected, the distance on first inlet point and nearest region summit to be detected for many rotors nobody
Machine radius of investigation, first side is any one side in region to be detected；Used inside region to be detected parallel to be detected
The mode on the one side in region is flown.
3. according to the method described in claim 1, it is characterised in that the acquisition meets default UAV-O-OP models constraint bar
The initial solution of part, including：
The area information to be detected, multi-rotor unmanned aerial vehicle information are encoded, a plurality of chromosome is generated at random；
Wherein, the random fully intermeshing of the identification information in region to be detected described in the first behavior of a plurality of chromosome, described more
Second row of bar chromosome represents whether multi-rotor unmanned aerial vehicle performs region task to be detected, the third line of a plurality of chromosome
Enter the random combine of the inlet point in region to be detected for multi-rotor unmanned aerial vehicle.
4. according to the method described in claim 1, it is characterised in that described that the initial solution is based on using default genetic algorithm
Obtain optimal solution to the UAV-O-OP model solutions, and using the optimal solution as a frame multi-rotor unmanned aerial vehicle to polylith detecting area
The task allocation result in domain, including：
Step 1: generate the initial parent population of default scale according to the initial solution, and calculate in population every chromosome
Fitness；
Have Step 2: carrying out the step of crossover operation obtains first generation progeny population, the intersection to chromosome in parent population
Body includes：
Two crossover locations in the first chromosome are randomly choosed, then look in the second chromosome intersecting position with the first chromosome
The first row identical gene put；The crossover location gene of the first chromosome and the second chromosome is replaced, so as to obtain
Trisome and tetrasome；Judge whether the trisome and tetrasome meet the default constraint
Condition；If meeting, the first chromosome and the second chromosome in the parent population are replaced；If it is not satisfied, then terminating to work as
Preceding operation；
Step 3: carrying out mutation operation to chromosome in first generation progeny population obtains second generation progeny population, the variation
Step is specifically included：
Randomly choose the 5th chromosome and carry out mutation operation, if the first row morphs, random fully intermeshing is carried out to the first row,
If the second row morphs, definitive variation position, and by it is original without multi-rotor unmanned aerial vehicle perform task become to have many rotors without
Man-machine execution task, if or on the contrary, the third line morphs, determine the position of its variation, and by the inlet point generated at random
Replace the inlet point at former variable position；
Step 4: obtain the optimal solution in the second filial generation population according to the fitness function, and by the second filial generation
Population combines to form new parent population with the parent population according to preset ratio；
Judge whether the number of times of the overall loop iteration of current procedures two, three, four reaches preset value；If it is not, then return to step two, and
Step 2 is performed using the new parent population as current parent population；If so, then performing step 5；
Step 5：Terminate iteration, and using the optimal solution finally obtained as this subtask allocation result.
5. method according to claim 4, it is characterised in that the fitness of the chromosome is：
Wherein, NARepresent region A to be detectediNumber；SiRepresent region A to be detectediArea；PiRepresent to complete region to be detected
AiThe income that is obtained of task；If xi=1, then it represents that complete detection mission, otherwise the multi-rotor unmanned aerial vehicle is not to be detected
Region AiExecution task；
Wherein, 0 ... NA+1Represent starting point, region to be detected and terminating point；tiRepresent multi-rotor unmanned aerial vehicle to search coverage Ai
According to the time of parallel sweep strategy execution task；tijRepresent multi-rotor unmanned aerial vehicle in region A to be detectedi,AjBetween fly
Time；xiRepresent that multi-rotor unmanned aerial vehicle completes region A to be detectediThe situation of completion task, if xi=1, then it represents that complete detection and appoint
Business, otherwise the multi-rotor unmanned aerial vehicle does not treat search coverage AiExecution task；yijRepresent multi-rotor unmanned aerial vehicle whether by treating
Search coverage Ai,AjIf, yij=1 represents that multi-rotor unmanned aerial vehicle passes through region A to be detectedi,Aj, otherwise the multi-rotor unmanned aerial vehicle do not have
Have by region A to be detectedi,Aj。
6. a kind of unmanned plane detection mission distributor, it is characterised in that when a frame multi-rotor unmanned aerial vehicle is waited to visit to polylith rectangle
Survey region and perform a variety of detection missions, described device includes：
Information acquisition unit, for obtaining area information to be detected and multi-rotor unmanned aerial vehicle information；
Initial solution acquiring unit, the initial solution of default UAV-O-OP models constraints is met for obtaining, wherein, it is described
UAV-O-OP models are that multi-rotor unmanned aerial vehicle obtains the maximum object function of total revenue in this detection mission；The constraint bar
Part includes the flight duration constraint of multi-rotor unmanned aerial vehicle institute；
Optimal solution computing unit, for being based on the initial solution to the UAV-O-OP model solutions using default genetic algorithm
Optimal solution is obtained, and regard the optimal solution as task allocation result of the multi-rotor unmanned aerial vehicle to polylith region to be detected.
7. device according to claim 6, it is characterised in that：
The object function of the UAV-O-OP models is：
The constraints of the UAV-O-OP models is：
In UAV-O-OP models, NARepresent region A to be detectediNumber；A0,Represent multi-rotor unmanned aerial vehicle starting point and
Terminal, the starting point is same point with terminal；SiRepresent region A to be detectediArea；PiRepresent to complete region A to be detectedi
The income that is obtained of task；tiRepresent multi-rotor unmanned aerial vehicle to search coverage AiTask is performed according to parallel sweep flying method
Time；tijRepresent multi-rotor unmanned aerial vehicle in region A to be detectedi,AjBetween time for flying；E represents multi-rotor unmanned aerial vehicle most
Big flight duration limitation；uiRepresent region A to be detectediPosition in route；xiRepresent that multi-rotor unmanned aerial vehicle treats search coverage
AiThe situation of completion task, if xi=1, then it represents that complete detection mission, otherwise multi-rotor unmanned aerial vehicle does not treat search coverage Ai
Execution task；yijRepresent whether multi-rotor unmanned aerial vehicle passes through region A to be detectedi,AjIf, yij=1 represents multi-rotor unmanned aerial vehicle warp
Cross region A to be detectedi,Aj, otherwise the multi-rotor unmanned aerial vehicle is not by region A to be detectedi,Aj；
Wherein, the parallel sweep flying method is：With perpendicular to region first to be detected while direction from first while on
One inlet point enters region to be detected, the distance on first inlet point and nearest region summit to be detected for many rotors nobody
Machine radius of investigation, first side is any one side in region to be detected；Used inside region to be detected parallel to be detected
The mode on the one side in region is flown.
8. device according to claim 6, it is characterised in that the initial solution acquiring unit is further used for：
The area information to be detected, multi-rotor unmanned aerial vehicle information are encoded, a plurality of chromosome is generated at random；
Wherein, the random fully intermeshing of the identification information in region to be detected described in the first behavior of a plurality of chromosome, described more
Second row of bar chromosome represents whether multi-rotor unmanned aerial vehicle performs region task to be detected, the third line of a plurality of chromosome
Enter the random combine of the inlet point in region to be detected for multi-rotor unmanned aerial vehicle.
9. device according to claim 6, it is characterised in that the optimal solution computing unit, be further used for performing with
Lower step：
Step 1: generate the initial parent population of default scale according to the initial solution, and calculate in population every chromosome
Fitness；
Have Step 2: carrying out the step of crossover operation obtains first generation progeny population, the intersection to chromosome in parent population
Body includes：
Two crossover locations in the first chromosome are randomly choosed, then look in the second chromosome intersecting position with the first chromosome
The first row identical gene put；The crossover location gene of the first chromosome and the second chromosome is replaced, so as to obtain
Trisome and tetrasome；Judge whether the trisome and tetrasome meet the default constraint
Condition；If meeting, the first chromosome and the second chromosome in the parent population are replaced；If it is not satisfied, then terminating to work as
Preceding operation；
Step 3: carrying out mutation operation to chromosome in first generation progeny population obtains second generation progeny population, the variation
Step is specifically included：
Randomly choose the 5th chromosome and carry out mutation operation, if the first row morphs, random fully intermeshing is carried out to the first row,
If the second row morphs, definitive variation position, and by it is original without multi-rotor unmanned aerial vehicle perform task become to have many rotors without
Man-machine execution task, if or on the contrary, the third line morphs, determine the position of its variation, and by the inlet point generated at random
Replace the inlet point at former variable position；
Step 4: obtain the optimal solution in the second filial generation population according to the fitness function, and by the second filial generation
Population combines to form new parent population with the parent population according to preset ratio；
Judge whether the number of times of the overall loop iteration of current procedures two, three, four reaches preset value；If it is not, then return to step two, and
Step 2 is performed using the new parent population as current parent population；If so, then performing step 5；
Step 5：Terminate iteration, and using the optimal solution finally obtained as this subtask allocation result.
10. device according to claim 9, it is characterised in that the fitness of the chromosome is：
Wherein, NARepresent region A to be detectediNumber；SiRepresent region A to be detectediArea；PiRepresent to complete region to be detected
AiThe income that is obtained of task；If xi=1, then it represents that complete detection mission, otherwise the multi-rotor unmanned aerial vehicle is not to be detected
Region AiExecution task；
Wherein, 0 ... NA+1Represent starting point, region to be detected and terminating point；tiRepresent multi-rotor unmanned aerial vehicle to search coverage Ai
According to the time of parallel sweep strategy execution task；tijRepresent multi-rotor unmanned aerial vehicle in region A to be detectedi,AjBetween fly
Time；xiRepresent that multi-rotor unmanned aerial vehicle completes region A to be detectediThe situation of completion task, if xi=1, then it represents that complete detection and appoint
Business, otherwise the multi-rotor unmanned aerial vehicle does not treat search coverage AiExecution task；yijRepresent multi-rotor unmanned aerial vehicle whether by treating
Search coverage Ai,AjIf, yij=1 represents that multi-rotor unmanned aerial vehicle passes through region A to be detectedi,Aj, otherwise the multi-rotor unmanned aerial vehicle do not have
Have by region A to be detectedi,Aj。
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