CN107037826B - Unmanned plane detection mission distribution method and device - Google Patents
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Abstract
The present invention relates to a kind of unmanned plane detection mission distribution method and devices, the case where several work task is executed to muti-piece region to be detected for a frame multi-rotor unmanned aerial vehicle in this method, the area information to be detected and multi-rotor unmanned aerial vehicle information for executing this subtask are obtained first, preset UAV-O-OP model and genetic algorithm are based on then according to this information, the optimal solution for enabling to the model to obtain maximum total revenue is obtained, and using the optimal solution as the distribution of the task of this subjob and trajectory planning result.Method provided by the invention can make unmanned plane execute job task automatically according to the result planned automatically, avoid the influence by manual operation.Furthermore, since method provided by the invention is using the optimal solution of preset maximum gain model as trajectory planning result, therefore the unmanned plane for executing job task based on the result can also obtain maximum total revenue while the task of execution, the shortest time is spent, so as to effectively improve the efficiency of operation.
Description
Technical field
The present invention relates to air vehicle technique fields, and in particular to a kind of unmanned plane detection mission distribution method and device.
Background technique
With the continuous development of aeronautical technology, more and more high technology equipments are had been applied in aviation field.And
In numerous high technology equipments, the advantage for the aspects such as unmanned plane is high with its operating efficiency, labor intensity is small, overall cost is low is fast
Short-term training is a kind of more important high technology equipment in aviation operation process.It takes photo by plane or scanning imagery etc. is made for example, can execute
Industry task.Current unmanned plane can substantially be roughly divided into more rotors (such as quadrotor, six rotors or eight rotor wing unmanned aerial vehicles etc.)
And fixed-wing two major classes.Wherein fixed-wing unmanned plane is long with flying distance, cruise area is big, flying speed is fast, height is high
Advantage is applied in aviation operation by relatively broad.
However, in the practice of the invention inventors have found that since current multi-rotor unmanned aerial vehicle operation is mainly people
Based on remote control, the effect of actual job by operator operation it is horizontal be affected, and by way of artificially regarding
The course line of planning and theoretical deviated route are serious, cause the operation missing rate of unmanned plane and repetitive rate often higher.
In addition, when a frame multi-rotor unmanned aerial vehicle completes a kind of detection mission to muti-piece region to be detected, in the process
Since the flight duration of unmanned plane is limited, the income after how selecting region to be detected and path planning to make completion task is most
(the total revenue maximum i.e. as much as possible for completing regionally detecting task and complete all areas after regionally detecting) greatly, and in income
The shortest scheme is selected on the basis of maximum also becomes a urgent problem to be solved.
Summary 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 affected by manual operation in technology, and in one frame multi-rotor unmanned aerial vehicle of utilization to muti-piece area to be detected
Domain can not be made rational planning for the track of unmanned plane when operation to obtain 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 executes a variety of detection missions to muti-piece rectangle region to be detected, which comprises
Obtain area information to be detected and multi-rotor unmanned aerial vehicle information；
Obtain the initial solution for meeting preset UAV-O-OP model constraint condition, wherein the UAV-O-OP model is more
Rotor wing unmanned aerial vehicle obtains the maximum objective function of total revenue in this detection mission；The constraint condition include more rotors nobody
The constraint of machine institute flight duration；
The initial solution is based on using preset genetic algorithm, optimal solution is obtained to the UAV-O-OP model solution, and will
The optimal solution is as a frame multi-rotor unmanned aerial vehicle to the task allocation result in muti-piece region to be detected.
Second aspect, a kind of unmanned plane detection mission distributor of another embodiment of the invention, when the more rotors of a frame
Unmanned plane executes a variety of detection missions to muti-piece 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, for obtaining the initial solution for meeting preset UAV-O-OP model constraint condition, wherein institute
Stating UAV-O-OP model is that multi-rotor unmanned aerial vehicle obtains the maximum objective function of total revenue in this detection mission；The constraint
Condition includes the constraint of multi-rotor unmanned aerial vehicle institute flight duration；
Optimal solution computing unit, for being based on the initial solution to the UAV-O-OP model using preset genetic algorithm
Solution obtains optimal solution, and distributes knot for the optimal solution as task of the frame multi-rotor unmanned aerial vehicle to muti-piece region to be detected
Fruit.
It is more for a frame in this method An embodiment provides a kind of unmanned plane detection mission distribution method
The case where rotor wing unmanned aerial vehicle executes several work task to muti-piece region to be detected obtains execute the to be detected of this subtask first
Area information and multi-rotor unmanned aerial vehicle information are based on preset UAV-O-OP model 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 using the optimal solution as the task of this subjob point
With with trajectory planning result.Compared to the mode of existing manual remote control, method provided by the invention can be according to preset mould
Type and algorithm automatically obtain the task and trajectory planning of unmanned plane in this subjob, allow unmanned plane according to the task with
And trajectory planning executes job task automatically, avoids the influence by manual operation.Further, since method provided by the invention is
Using the optimal solution of preset maximum gain model as trajectory planning as a result, therefore executing the nothing of job task based on the result
It is man-machine also to obtain maximum total revenue while the task of execution, the shortest time is spent, is made so as to effectively improve
The efficiency of industry, so that unmanned plane operation form can be applied in wider detection mission.
Detailed description of the invention
By reading the following detailed description of the preferred embodiment, various other advantages and benefits are common for this field
Technical staff will become clear.The drawings are only for the purpose of illustrating a preferred embodiment, and is not considered as to the present invention
Limitation.And throughout the drawings, the same reference numbers will be used to refer to the same parts.In the accompanying drawings:
Fig. 1 is a kind of embodiment of the method flow chart of unmanned plane detection mission distribution provided by the invention；
Fig. 2 (a) -2 (c) is regionally detecting schematic diagram to be detected provided by the invention；
Fig. 3 is inlet point position view provided by the invention；
Fig. 4 is chiasma process schematic provided by the invention；
Fig. 5 is chromosomal variation rule schematic diagram provided by the invention；
Fig. 6 is 5 area schematics to be detected provided by the invention；
Fig. 7 is optimal solution convergence schematic diagram provided by the invention；
Fig. 8 is a kind of unmanned plane detection mission assigned unit example structure schematic diagram provided by the invention.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts every other
Embodiment shall fall within the protection scope of the present invention.
In a first aspect, the embodiment of the invention provides a kind of unmanned plane detection mission distribution method, when the more rotors of a frame without
When the man-machine a variety of detection missions of execution to muti-piece rectangle region to be detected, as shown in Figure 1, which comprises
S101, area information to be detected and multi-rotor unmanned aerial vehicle information are obtained；
S102, acquisition meet the initial solution of preset UAV-O-OP model constraint condition, wherein the UAV-O-OP model
The maximum objective function of total revenue is obtained in this detection mission for multi-rotor unmanned aerial vehicle；The constraint condition includes more rotors
The constraint of unmanned plane institute flight duration；
S103, using preset genetic algorithm be based on the initial solution UAV-O-OP model solution is obtained it is optimal
Solution, and using the optimal solution as the multi-rotor unmanned aerial vehicle to the task allocation result in muti-piece region to be detected.
It is more for a frame in this method An embodiment provides a kind of unmanned plane detection mission distribution method
The case where rotor wing unmanned aerial vehicle executes several work task to muti-piece region to be detected obtains execute the to be detected of this subtask first
Area information and multi-rotor unmanned aerial vehicle information are based on preset UAV-O-OP model 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 using the optimal solution as the task of this subjob point
With with trajectory planning result.Compared to the mode of existing manual remote control, method provided by the invention can be according to preset mould
Type and algorithm automatically obtain the task and trajectory planning of unmanned plane in this subjob, allow unmanned plane according to the task with
And trajectory planning executes job task automatically, avoids the influence by manual operation.Further, since method provided by the invention is
Using the optimal solution of preset maximum gain model as trajectory planning as a result, therefore executing the nothing of job task based on the result
It is man-machine also to obtain maximum total revenue while the task of execution, the shortest time is spent, is made so as to effectively improve
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 objective function that the UAV-O-OP model in the above method includes and
Constraint condition is the important evidence that the present invention can obtain optimum programming result, can be arranged in several ways, below
One of optional set-up mode is described in detail.
The UAV-O-OP model is an orientation problem (OP).Multi-rotor unmanned aerial vehicle self performance, region to be detected
Path when size executes task with multi-rotor unmanned aerial vehicle etc. also has an impact the result of task distribution.In concrete model
Design parameter and be provided that
(1) multi-rotor unmanned aerial vehicle
Indicate to execute 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 carrying radius of investigation is RD's
Sensor.
In conjunction with multi-rotor unmanned aerial vehicle execute detection mission the characteristics of, make herein it is assumed hereinafter that:
(1) multi-rotor unmanned aerial vehicle all has the ability of automatic obstacle-avoiding, can be in the case of facing collision, using independently evading
Control strategy, the Path error generated therefrom can be neglected relative to total flight path length also very little；
(2) multi-rotor unmanned aerial vehicle is flown with identical cruising speed and cruising altitude, thus not considering other factors
Reach optimal Effect on Detecting when 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
If A0,The respectively beginning and end of multi-rotor unmanned aerial vehicle, beginning and end herein are identical；For NABlock region to be detected, and region A to be detectediBe apex coordinate be (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 more rotations
Wing unmanned plane treats search coverage AiWhen cover type scans, the inlet point that multi-rotor unmanned aerial vehicle flies into region to be detected is Ini, fly
From leaving a little as Out for region to be detectedi, and assume after the multi-rotor unmanned aerial vehicle must detect monolith region to be detected 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 executes detection mission, not only need to pass through cover type inside region to be detected
Job task is completed in scanning, but also is needed in different interregional flights to be detected with the switching between realization task, therefrom
Produce two kinds of flight path, i.e., the flight path in 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 when cover type scanning
Formula:
In region A to be detectediInside, multi-rotor unmanned aerial vehicle carry out path planning using parallel sweep strategy.In cover type
Multi-rotor unmanned aerial vehicle is from region A to be detected when scanningiIniPoint enters, and is parallel into the path behind region to be detected to be detected
Region side, then from OutiPoint leaves, at this point, 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 turning under given speed, for the region to be detected of Fig. 2 (c)
Just there are two different turning radius numbers, as shown in Fig. 2 (a) and Fig. 2 (b), the wherein turning in track shown in Fig. 2 (b)
Track shown in number ratio Fig. 2 (a) will lack, and total path length also lacks than Fig. 2 (a).
When multi-rotor unmanned aerial vehicle uses parallel sweep strategy execution regionally detecting task, need first to determine into 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 arbitrary point, but work as 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 this paper to
Search coverage is rectangle, then is R apart from vertexDPoint have eight (as shown in Figure 3), 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 perpendicular to where the point
Side.And can be uniquely determined and be left a little by inlet point, because the turning radius when multi-rotor unmanned aerial vehicle determines that sweep radius is true
It is fixed, determined into region direction to be detected, when region side length to be detected determines, then number of turns determines, multi-rotor unmanned aerial vehicle from
It opens the direction in region to be detected and leaves point OutiIt is determining.
However not only need to consider the cost time inside region to be detected when treating search coverage and carrying out detection scanning
It also needs to consider the cost time between region to be detected, needs the balanced time between the two, so no longer to be spent
Time is measurement standard, but using the income in the region to be detected detected as standard.
Therefore, it for a kind of described unmanned plane detection mission assignment problem, completes to appoint with multi-rotor unmanned aerial vehicle herein
Total revenue after business maximizes the objective function as optimization problem, establishes following mathematical model.
The objective function of the UAV-O-OP model are as follows:
The constraint condition of the UAV-O-OP model are as follows:
In UAV-O-OP model, NAIndicate region A to be detectediNumber；A0,Indicate rising for multi-rotor unmanned aerial vehicle
Initial point and terminal, the starting point and terminal are same point；SiIndicate region A to be detectediArea；PiIt indicates to complete to be detected
Region AiTask income obtained；tiIndicate multi-rotor unmanned aerial vehicle to search coverage AiAppoint according to parallel sweep strategy execution
The time of business；tijIndicate multi-rotor unmanned aerial vehicle in region A to be detectedi,AjBetween time for flying；E indicates multi-rotor unmanned aerial vehicle
Maximum flight duration limitation；uiIndicate region A to be detectediPosition in route；xiIndicate that multi-rotor unmanned aerial vehicle treats detecting area
Domain AiThe case where 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；yijIndicate whether multi-rotor unmanned aerial vehicle passes through region A to be detectedi,AjIf yij=1 indicates multi-rotor unmanned aerial vehicle
By region A to be detectedi,Aj, otherwise the multi-rotor unmanned aerial vehicle does not pass through 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)
It is to guarantee that the path starting point of multi-rotor unmanned aerial vehicle is 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 unmanned plane route from forming sub-loop with formula (6)；Constraint formula (7) is the definition of target, path variable.
Understandable to be, after obtaining UAV-O-OP model, method provided in an embodiment of the present invention can basis
Preset genetic algorithm solves the optimal solution of UAV-O-OP model.Wherein this default genetic algorithm for seeking optimal solution can lead to
Accomplished in many ways is crossed, one of optional mode is described in detail below.
The general thought of method provided in an embodiment of the present invention are as follows: task distribution to be solved for the embodiment of the present invention
For problem, each feasible solution (namely the solution for meeting preset model constraint) can be expressed as item chromosome.Feasible solution kind
Group (namely initial parent population) can be by a plurality of genome at, scale self-defining according to the actual situation.Obtaining this
After the initial parent population of sample, and then initial parent population can be updated population by the intersection of chromosome, variation,
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 the cyclic process of update finally selects current optimal child chromosome when the number of iterations reaches preset value, should
Child chromosome is to meet the optimal solution for enabling to objective function to obtain maximum gain of model constraint, which is
For final required task allocation result of the invention.
And in this course, be related in genetic algorithm coding, intersection, variation and fitness function
The genetic algorithm of rule being configured so that after setting can be applied to solution to preset model and obtain in optimal solution.It can be with
Understand, the setting of each function in genetic algorithm can be there are ways to realize, below to a kind of optional function
Set-up mode is specifically described.
(1) it encodes
Coding in the present invention includes region to be detected, whether executes 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 gives the content of every a line of chromosome after a coding.Wherein, chromosome the first row be to
The identification information in the information of search coverage namely region to be detected, the second row are to indicate whether unmanned plane executes 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 executes task (enters
Piont mark corresponds to region R to be detected shown in Fig. 2D1-RD8).Whole chromosome indicates 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 enters wait visit
Survey region A4Completion task, finally returns to starting point, and target A1,A2It is not accessed.
1 chromosome of table: NA=5
| Region to be detected | 3 | 1 | 5 | 4 | 2 |
| Whether task is executed | 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 gene identical with the first row of the first chromosome crossover location in the second chromosome；By the first chromosome and the second chromosome
Crossover location gene be replaced, to obtain third chromosome and tetrasome；
Such as in Fig. 4, two parent chromosomes first randomly choose 2 positions intersected in parent A, then look for
It is swapped to parent B same target regional location, to obtain two new child chromosome A, B.
(3) it makes a variation
It is also likely to be multiple genes that variation, which may be a gene, in the present invention, and this paper chromosomal variation mainly has following several
Kind situation: zone sequence variation to be detected, if there is multi-rotor unmanned aerial vehicle to execute task variation, region inlet point to be detected becomes
It is different.Wherein, if the first row morphs, random fully intermeshing, if the second row morphs, definitive variation position are carried out to the first row
It sets, and task is executed by original no multi-rotor unmanned aerial vehicle and becomes have multi-rotor unmanned aerial vehicle to execute task, or on the contrary, if third
Row morphs, and determines the position of its variation, and the inlet point generated at random is replaced 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 third, which is arranged, to be become 0 expression unmanned plane from 1 and is originally performed region task to be detected not execute, the third line
The inlet point of one column is by RD7Become RD2。
(4) fitness function and selection
The fitness of the chromosome are as follows:
Wherein, NAIndicate region A to be detectediNumber；SiIndicate region A to be detectediArea；PiIt indicates to complete to be detected
Region AiTask income obtained；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+1Indicate starting point, region to be detected and terminating point；tiIndicate multi-rotor unmanned aerial vehicle to detecting area
Domain AiAccording to the time of parallel sweep strategy execution task；tijIndicate multi-rotor unmanned aerial vehicle in region A to be detectedi,AjBetween fly
Time；xiIndicate that multi-rotor unmanned aerial vehicle completes region A to be detectediThe case where completion task, if xi=1, then it represents that complete detection
Task, otherwise the multi-rotor unmanned aerial vehicle does not treat search coverage AiExecution task；yijIndicate whether multi-rotor unmanned aerial vehicle passes through
Region A to be detectedi,AjIf yij=1 indicates 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 function in the embodiment of the present invention, the first fitness is formula (8), is the bigger the better, indicates all quilts
Access the total revenue in region to be detected, i.e., it is related with the area in accessed region and region.And work as the first of a plurality of chromosome
When fitness is identical, i.e., allocation plan difference total revenue is identical, but the cost time of each allocation plan is but different, because
This needs to carry out the calculating (as shown in formula (9)) of the second fitness, carries out postsearch screening, so that total revenue maximum is selected, and
The shortest allocation plan on the basis of total revenue is maximum.
Since the constantly loop iteration of above-mentioned cross and variation process carries out, so that parent population is thus continually updated, thus
Generate more new populations.It is understood that the process of this iterative cycles can infinitely go on, but in this way
A final result can not be obtained.Therefore whether the present invention judges currently accumulative the number of iterations after each iteration
The number of iterations threshold value is had reached, wherein this threshold value can self-setting according to the actual situation.If judgement is current not up to
The number of iterations threshold value then needs to continue iterative process；If judgement has currently reached the number of iterations threshold value, then it is assumed that at this time
The number of iterations is enough, and current optimal solution may act as the result of the task distribution of this subjob.And then it can also incite somebody to action
The result is distributed to corresponding frame multi-rotor unmanned aerial vehicle, so that this unmanned plane can execute this work according to this result
Industry task achievees the purpose that this subjob and obtains the maximum gain in region to be detected.
For the superiority for embodying 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 model according to above-mentioned function setup, to obtain final task distribution knot
Fruit.
Specifically, solution of the genetic algorithm to UAV-O-OP model is realized in the environment of MATLAB 2013,
And it is tested.
Assuming that there is 1 frame unmanned plane to execute task to five pieces of regions to be detected, and distribution side is obtained using the genetic algorithm
Case, wherein taking the crossover probability of the genetic algorithm is 0.9, mutation probability 0.5, population scale 500, the number of iterations is
100.The design parameter being related in experimentation is described as follows:
(1) unmanned plane
The concrete configuration of unmanned plane is as shown in table 2 in the experiment of this paper, and unmanned plane speed is 4m/s, maximum probe radius
For 5m, max-endurance 1800s.
2 unmanned plane basic parameter allocation list of table
| 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 use of income of the genetic algorithm to the optimal solution that above-mentioned scene obtains 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.
4 optimal distributing scheme of table
| Region | 3 | 4 | 5 | 2 | 1 |
| Task execution | 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 executes a variety of detection missions to muti-piece 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, for obtaining the initial solution for meeting preset UAV-O-OP model constraint condition,
In, the UAV-O-OP model is that multi-rotor unmanned aerial vehicle obtains the maximum objective function of total revenue in this detection mission；Institute
Stating constraint condition includes the constraint of multi-rotor unmanned aerial vehicle institute flight duration；
Optimal solution computing unit 203, for being based on the initial solution to the UAV-O-OP using preset genetic algorithm
Model solution obtains optimal solution, and distributes knot for the optimal solution as task of the multi-rotor unmanned aerial vehicle to muti-piece region to be detected
Fruit.
In the specific implementation, it is characterised in that:
The objective function of the UAV-O-OP model are as follows:
The constraint condition of the UAV-O-OP model are as follows:
In UAV-O-OP model, NAIndicate region A to be detectediNumber；A0,Indicate rising for multi-rotor unmanned aerial vehicle
Initial point and terminal, the starting point and terminal are same point；SiIndicate region A to be detectediArea；PiIt indicates to complete to be detected
Region AiTask income obtained；tiIndicate multi-rotor unmanned aerial vehicle to search coverage AiIt is held according to parallel sweep flying method
The time of row task；tijIndicate multi-rotor unmanned aerial vehicle in region A to be detectedi,AjBetween time for flying；E indicate more rotors without
Man-machine maximum flight duration limitation；uiIndicate region A to be detectediPosition in route；xiIndicate that multi-rotor unmanned aerial vehicle treats spy
Survey region AiThe case where 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；yijIndicate whether multi-rotor unmanned aerial vehicle passes through region A to be detectedi,AjIf yij=1 indicate more rotors without
It is man-machine to pass through region A to be detectedi,Aj, otherwise the multi-rotor unmanned aerial vehicle does not pass through region A to be detectedi,Aj；
Wherein, the parallel sweep flying method are as follows: with perpendicular to region first to be detected while direction from first while on
The first inlet point enter region to be detected, be more rotors at a distance from first inlet point and nearest region vertex to be detected
Unmanned plane radius of investigation, first side are any one side in region to be detected；Inside region to be detected using be parallel to
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, generate a plurality of chromosome 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 indicates whether multi-rotor unmanned aerial vehicle executes 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 executing 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 it specifically includes:
Two crossover locations in the first chromosome are randomly choosed, then look for handing in the second chromosome with the first chromosome
The identical gene of the first row that vent is set；The crossover location gene of the first chromosome and the second chromosome is replaced, thus
Obtain third chromosome and tetrasome；It is described default to judge whether the third chromosome and tetrasome meet
Constraint condition；If satisfied, then replacing the first chromosome and the second chromosome in the parent population；If 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 specifically includes:
It randomly chooses the 5th chromosome and carries 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 task is executed by original no multi-rotor unmanned aerial vehicle and becomes have more rotations
Wing unmanned plane executes task, or on the contrary, if the third line morphs, determine the position of its variation, and will generate at random into
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 according to preset ratio with the parent population；
Judge whether the number of the whole loop iteration of current procedures two, three, four reaches preset value；If it is not, then return step
Two, and step 2 is executed using the new parent population as current parent population；If so, executing step 5；
Step 5: terminate iteration, and using the optimal solution finally obtained as the allocation result of this subtask.
In the specific implementation, the fitness of the chromosome are as follows:
Wherein, NAIndicate region A to be detectediNumber；SiIndicate region A to be detectediArea；PiIt indicates to complete to be detected
Region AiTask income obtained；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+1Indicate starting point, region to be detected and terminating point；tiIndicate multi-rotor unmanned aerial vehicle to detecting area
Domain AiAccording to the time of parallel sweep strategy execution task；tijIndicate multi-rotor unmanned aerial vehicle in region A to be detectedi,AjBetween fly
Time；xiIndicate that multi-rotor unmanned aerial vehicle completes region A to be detectediThe case where completion task, if xi=1, then it represents that complete detection
Task, otherwise the multi-rotor unmanned aerial vehicle does not treat search coverage AiExecution task；yijIndicate whether multi-rotor unmanned aerial vehicle passes through
Region A to be detectedi,AjIf yij=1 indicates 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。
Since the unmanned plane detection mission assigned unit that the present embodiment is introduced is that can execute in the embodiment of the present invention
The distribution of unmanned plane detection mission method device, so based on 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
Device used by the method that unmanned plane detection mission is distributed in inventive embodiments, belongs to the range to be protected of the application.
In the instructions provided here, numerous specific details are set forth.It is to be appreciated, however, that implementation of the invention
Example can be practiced without these specific details.In some instances, well known method, structure is not been shown in detail
And technology, so as not to obscure the understanding of this specification.
Similarly, it should be understood that in order to simplify the disclosure and help to understand one or more of the various inventive aspects, In
Above in the description of 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 disclosed method should not be interpreted as reflecting the following intention: i.e. required to protect
Shield the present invention claims features more more than feature expressly recited in each claim.More precisely, as following
Claims reflect as, inventive aspect is all features less than single embodiment disclosed above.Therefore,
Thus the claims for following specific embodiment are expressly incorporated in the specific embodiment, wherein each claim itself
All as a separate embodiment of the present invention.
Those skilled in the art will understand that can be carried out adaptively to the module in the equipment in embodiment
Change and they are arranged in one or more devices different from this embodiment.It can be the module or list in embodiment
Member or component are combined into a module or unit or component, and furthermore they can be divided into multiple submodule or subelement or
Sub-component.Other than such feature and/or at least some of process or unit exclude each other, it can use any
Combination is to all features disclosed in this specification (including adjoint claim, abstract and attached drawing) and so disclosed
All process or units of what method or apparatus are combined.Unless expressly stated otherwise, this specification is (including adjoint power
Benefit requires, abstract and attached drawing) disclosed in each feature can be by providing identical, equivalent, or similar purpose alternative features come generation
It replaces.
In addition, it will be appreciated by those of skill in the art that although some embodiments in this include institute in other embodiments
Including certain features rather than other feature, but the combination of the feature of different embodiment means 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 can in any combination mode come using.
Certain unit embodiments of the invention can be implemented in hardware, or to run on one or more processors
Software module realize, or be implemented in a combination thereof.It will be understood by those of skill in the art that can be used in practice
Microprocessor or digital signal processor (DSP) realize gateway according to an embodiment of the present invention, proxy server, in system
Some or all components some or all functions.The present invention is also implemented as executing side as described herein
Some or all device or device programs (for example, computer program and computer program product) of method.It is such
It realizes that program of the 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 an internet website to obtain, and perhaps be provided on the carrier signal or with any other shape
Formula provides.
It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and ability
Field technique personnel can be designed alternative embodiment without departing from the scope of the appended claims.In the claims,
Any reference symbol between parentheses should not be configured to limitations on claims.Word "comprising" does not exclude the presence of not
Element or step listed in the claims.Word "a" or "an" located in front of the element does not exclude the presence of multiple such
Element.The present invention can be by means of including the hardware of several different elements and being come by means of properly programmed computer real
It is existing.In the unit claims listing several devices, several in these devices can be through the same hardware branch
To embody.The use of word first, second, and third does not indicate any sequence.These words can be explained and be run after fame
Claim.
Claims (8)
1. a kind of unmanned plane detection mission distribution method, which is characterized in that waited for using a frame multi-rotor unmanned aerial vehicle muti-piece rectangle
Search coverage executes a variety of detection missions, which comprises
Obtain area information to be detected and multi-rotor unmanned aerial vehicle information；
Obtain the initial solution for meeting preset UAV-O-OP model constraint condition, wherein the UAV-O-OP model is more rotors
Unmanned plane obtains the maximum objective function of total revenue in this detection mission；The constraint condition includes multi-rotor unmanned aerial vehicle institute
The constraint of flight duration；
The objective function of the UAV-O-OP model are as follows:
The constraint condition of the UAV-O-OP model are as follows:
In UAV-O-OP model, NAIndicate region A to be detectediNumber；A0,Indicate the starting point of multi-rotor unmanned aerial vehicle
And terminal, the starting point and terminal are same point；SiIndicate region A to be detectediArea；PiIt indicates to complete region to be detected
AiTask income obtained；tiIndicate multi-rotor unmanned aerial vehicle to search coverage AiIt executes and appoints according to parallel sweep flying method
The time of business；tijIndicate multi-rotor unmanned aerial vehicle in region A to be detectedi,AjBetween time for flying；E indicates multi-rotor unmanned aerial vehicle
Maximum flight duration limitation；uiIndicate region A to be detectediPosition in route；xiIndicate that multi-rotor unmanned aerial vehicle treats detecting area
Domain AiThe case where 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；yijIndicate whether multi-rotor unmanned aerial vehicle passes through region A to be detectedi,AjIf yij=1 indicates multi-rotor unmanned aerial vehicle
By region A to be detectedi,Aj, otherwise the multi-rotor unmanned aerial vehicle does not pass through region A to be detectedi,Aj；
The initial solution is based on using preset genetic algorithm, optimal solution is obtained to the UAV-O-OP model solution, and most by this
Excellent solution is as the multi-rotor unmanned aerial vehicle to the task allocation result in muti-piece region to be detected, comprising:
Step 1: generate the initial parent population of default scale according to the initial solution, and calculate in population every chromosome
Fitness；
Step 2: carrying out crossover operation to chromosome in parent population obtains first generation progeny population, the step of intersection, has
Body includes:
Two crossover locations in the first chromosome are randomly choosed, then look for intersecting position with the first chromosome in the second chromosome
The identical gene of the first row set；The crossover location gene of the first chromosome and the second chromosome is replaced, to obtain
Third chromosome and tetrasome；Judge whether the third chromosome and tetrasome meet the constraint article
Part；If satisfied, then replacing the first chromosome and the second chromosome in the parent population；If not satisfied, then terminating current
Operation；
Step 3: carrying out mutation operation to chromosome in first generation progeny population obtains second generation progeny population, the variation
Step specifically includes:
It randomly chooses the 5th chromosome and carries 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 original no multi-rotor unmanned aerial vehicle execute task become to have more rotors without
Man-machine execution task, or on the contrary, the position of its variation, and the inlet point that will be generated at random are determined if the third line morphs
Replace the inlet point at former variable position；
Step 4: obtain the optimal solution in the second generation progeny population according to fitness function, and by the second generation filial generation
Population combines to form new parent population according to preset ratio with the parent population；
Judge whether the number of the whole loop iteration of current procedures two, three, four reaches preset value；If it is not, then return step two, and
Step 2 is executed using the new parent population as current parent population；If so, executing step 5；
Step 5: terminate iteration, and using the optimal solution finally obtained as the allocation result of this subtask.
2. according to the method described in claim 1, it is characterized by:
The parallel sweep flying method are as follows: with perpendicular to region first to be detected while direction from first while on first enter
Point enters region to be detected, and first inlet point detects at a distance from nearest region vertex to be detected for multi-rotor unmanned aerial vehicle
Radius, first side are any one side in region to be detected；It is used inside region to be detected and is parallel to region to be detected
The mode on one side is flown.
3. the method according to claim 1, wherein the acquisition meets preset UAV-O-OP model constraint item
The initial solution of part, comprising:
The area information to be detected, multi-rotor unmanned aerial vehicle information are encoded, generate a plurality of chromosome 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 is described more
Second row of chromosome indicates whether multi-rotor unmanned aerial vehicle executes 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. the method according to claim 1, wherein the fitness of the chromosome are as follows:
Wherein, NAIndicate region A to be detectediNumber；SiIndicate region A to be detectediArea；PiIt indicates to complete region to be detected
AiTask income obtained；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+1Indicate starting point and ending point；tiIndicate multi-rotor unmanned aerial vehicle to search coverage AiAccording to parallel sweep plan
Slightly execute the time of task；tijIndicate multi-rotor unmanned aerial vehicle in region A to be detectedi,AjBetween time for flying；xiIndicate more rotations
Wing unmanned plane completes region A to be detectediThe case where completion task, if xi=1, then it represents that complete detection mission, otherwise more rotors
Unmanned plane does not treat search coverage AiExecution task；yijIndicate whether multi-rotor unmanned aerial vehicle passes through region A to be detectedi,AjIf
yij=1 indicates that multi-rotor unmanned aerial vehicle passes through region A to be detectedi,Aj, otherwise the multi-rotor unmanned aerial vehicle does not pass through region to be detected
Ai,Aj。
5. a kind of unmanned plane detection mission distributor, which is characterized in that waited for using a frame multi-rotor unmanned aerial vehicle muti-piece rectangle
Search coverage executes a variety of detection missions, 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, for obtaining the initial solution for meeting preset UAV-O-OP model constraint condition, wherein described
UAV-O-OP model is that multi-rotor unmanned aerial vehicle obtains the maximum objective function of total revenue in this detection mission；The constraint item
Part includes the constraint of multi-rotor unmanned aerial vehicle institute flight duration；
The objective function of the UAV-O-OP model are as follows:
The constraint condition of the UAV-O-OP model are as follows:
In UAV-O-OP model, NAIndicate region A to be detectediNumber；A0,Indicate the starting point of multi-rotor unmanned aerial vehicle
And terminal, the starting point and terminal are same point；SiIndicate region A to be detectediArea；PiIt indicates to complete region to be detected
AiTask income obtained；tiIndicate multi-rotor unmanned aerial vehicle to search coverage AiIt executes and appoints according to parallel sweep flying method
The time of business；tijIndicate multi-rotor unmanned aerial vehicle in region A to be detectedi,AjBetween time for flying；E indicates multi-rotor unmanned aerial vehicle
Maximum flight duration limitation；uiIndicate region A to be detectediPosition in route；xiIndicate that multi-rotor unmanned aerial vehicle treats detecting area
Domain AiThe case where 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；yijIndicate whether multi-rotor unmanned aerial vehicle passes through region A to be detectedi,AjIf yij=1 indicates multi-rotor unmanned aerial vehicle
By region A to be detectedi,Aj, otherwise the multi-rotor unmanned aerial vehicle does not pass through region A to be detectedi,Aj；
Optimal solution computing unit, for being based on the initial solution to the UAV-O-OP model solution using preset genetic algorithm
Optimal solution is obtained, and using the optimal solution as the multi-rotor unmanned aerial vehicle to the task allocation result in muti-piece region to be detected, comprising:
Step 1: generate the initial parent population of default scale according to the initial solution, and calculate in population every chromosome
Fitness；
Step 2: carrying out crossover operation to chromosome in parent population obtains first generation progeny population, the step of intersection, has
Body includes:
Two crossover locations in the first chromosome are randomly choosed, then look for intersecting position with the first chromosome in the second chromosome
The identical gene of the first row set；The crossover location gene of the first chromosome and the second chromosome is replaced, to obtain
Third chromosome and tetrasome；Judge whether the third chromosome and tetrasome meet the constraint article
Part；If satisfied, then replacing the first chromosome and the second chromosome in the parent population；If not satisfied, then terminating current
Operation；
Step 3: carrying out mutation operation to chromosome in first generation progeny population obtains second generation progeny population, the variation
Step specifically includes:
It randomly chooses the 5th chromosome and carries 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 original no multi-rotor unmanned aerial vehicle execute task become to have more rotors without
Man-machine execution task, or on the contrary, the position of its variation, and the inlet point that will be generated at random are determined if the third line morphs
Replace the inlet point at former variable position；
Step 4: obtain the optimal solution in the second generation progeny population according to fitness function, and by the second generation filial generation
Population combines to form new parent population according to preset ratio with the parent population；
Judge whether the number of the whole loop iteration of current procedures two, three, four reaches preset value；If it is not, then return step two, and
Step 2 is executed using the new parent population as current parent population；If so, executing step 5；
Step 5: terminate iteration, and using the optimal solution finally obtained as the allocation result of this subtask.
6. device according to claim 5, it is characterised in that:
The parallel sweep flying method are as follows: with perpendicular to region first to be detected while direction from first while on first enter
Point enters region to be detected, and first inlet point detects at a distance from nearest region vertex to be detected for multi-rotor unmanned aerial vehicle
Radius, first side are any one side in region to be detected；It is used inside region to be detected and is parallel to region to be detected
The mode on one side is flown.
7. device according to claim 5, which is characterized 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, generate a plurality of chromosome 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 is described more
Second row of chromosome indicates whether multi-rotor unmanned aerial vehicle executes 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.
8. device according to claim 5, which is characterized in that the fitness of the chromosome are as follows:
Wherein, NAIndicate region A to be detectediNumber；SiIndicate region A to be detectediArea；PiIt indicates to complete region to be detected
AiTask income obtained；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+1Indicate starting point and ending point；tiIndicate multi-rotor unmanned aerial vehicle to search coverage AiAccording to parallel sweep plan
Slightly execute the time of task；tijIndicate multi-rotor unmanned aerial vehicle in region A to be detectedi,AjBetween time for flying；xiIndicate more rotations
Wing unmanned plane completes region A to be detectediThe case where completion task, if xi=1, then it represents that complete detection mission, otherwise more rotors
Unmanned plane does not treat search coverage AiExecution task；yijIndicate whether multi-rotor unmanned aerial vehicle passes through region A to be detectedi,AjIf
yij=1 indicates that multi-rotor unmanned aerial vehicle passes through region A to be detectedi,Aj, otherwise the multi-rotor unmanned aerial vehicle does not pass through region to be detected
Ai,Aj。
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| CN107807665B (en) * | 2017-11-29 | 2020-11-17 | 合肥工业大学 | Unmanned aerial vehicle formation detection task cooperative allocation method and device |
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