CN108459616A - Unmanned aerial vehicle group collaboration covering Route planner based on artificial bee colony algorithm - Google Patents
Unmanned aerial vehicle group collaboration covering Route planner based on artificial bee colony algorithm Download PDFInfo
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Abstract
The invention belongs to unmanned early warning plane routeing fields, disclose a kind of unmanned aerial vehicle group collaboration covering Route planner based on artificial bee colony algorithm, including：Set unmanned aerial vehicle group warning region and can flight range and unmanned aerial vehicle group parameter, set the parameter of artificial bee colony algorithm and initial nectar source group, the state of unmanned aerial vehicle group subsequent time determined by artificial bee colony algorithm, update the speed deflection angle of unmanned aerial vehicle group；Monitor area can be effectively covered in real time, to use unmanned aerial vehicle group early warning to lay a good foundation.
Description
Technical field
The invention belongs to unmanned early warning plane routeing field more particularly to a kind of unmanned planes based on artificial bee colony algorithm
Group's collaboration covering Route planner, the collaboration for being suitable for a unmanned early warning group of planes cover routeing problem.
Background technology
Application advantage of the unmanned plane in modern war is increasingly being applied to execute a variety of dangerous and complicated
Task.For the unmanned early warning plane of early warning detection, it is mainly used for carrying out automation covering to specified region, and to latent
Dangerous carry out early warning.
Under Information Battle Environment complicated and changeable, single unmanned plane faces detection angle and model when executing covering task
The limitation for enclosing equal aspects, constrains the performance of fighting efficiency, the difficulty that single unmanned plane is accomplished the task satisfactorily is increasing.It is more
Unmanned plane collaboration detection refers to that two framves or the above unmanned plane of two framves cooperate, execute covering task with cooperating.Relative to
The unmanned early warning plane of single rack, multi rack unmanned early warning plane can overcome the angle of radar detedtor to limit when being performed in unison with task, from more
A different direction is observed target area, and when facing extensive area search mission, multiple UAVs can be realized pair
The entire effective covering for scouting region, therefore with preferably scouting efficiency and stronger task fault-tolerance ability.
Path Planning for Unmanned Aircraft Vehicle is the core of multiple no-manned plane Collaborative Control, is a key for realizing unmanned plane autonomous flight
Technology can be directed to the air route that different mission requirements provide one or more safe and feasible for unmanned plane, smoothly complete to unmanned plane
Key effect is played at task.In order to smoothly complete collaboration covering task, it to be used for the Path Planning for Unmanned Aircraft Vehicle of early warning detection
Main purpose is to establish the space path that can fly, and in given warning region, finds multiple UAVs and is pointed out from respective
Hair, the optimal or feasible boat that specifying constraint and performance indicator are completely covered and met to region is reached with fast speed
Road, unmanned aerial vehicle group needs continue to move and keep this state after being completely covered.
For such collaboration covering problem, research report both domestic and external is less.2007, the Peng Hui of the National University of Defense technology,
Shen Lincheng etc. artificially reduces the complexity of problem solving, is broken down into the distribution of multiple no-manned plane mission area and path is completely covered
Plan two sub-problems, it is proposed that the Area Coverage Searching Path Method based on scan line mode achieves certain effect.But
Being this method seems more inflexible, not smart enoughization.Described mobile base station maximum region covering problem in Baidu and Google,
There is difference substantially with problem to be solved by this invention.Mobile base station maximum region covering problem is last the result is that quiet
State, and unmanned early warning plane flown in the air motion state after a period of time be very unlikely to become it is static, that is to say, that unmanned plane exists
It is completely covered after specified region, it is also necessary to continue the trend for moving and keeping being completely covered.
Invention content
In view of the above-mentioned problems, the purpose of the present invention is to provide a kind of, the unmanned aerial vehicle group collaboration based on artificial bee colony algorithm is covered
Lid Route planner, this method ask unmanned aerial vehicle group collaboration covering on the basis of angle searching, using artificial bee colony algorithm
Topic carries out trajectory planning, monitor area can be effectively covered in real time, to use unmanned aerial vehicle group early warning to lay a good foundation.
In order to achieve the above objectives, the present invention is realised by adopting the following technical scheme.
A kind of unmanned aerial vehicle group collaboration covering Route planner based on artificial bee colony algorithm, the method includes walking as follows
Suddenly：
Step 1, set unmanned aerial vehicle group warning region and can flight range, the unmanned aerial vehicle group include Dim frame unmanned planes,
Set the maximum speed deflection angle of every frame unmanned plane, the maximum operating range of each airborne radar；Dim is just whole more than zero
Number；
Step 2, the initial velocity of every frame unmanned plane is set, form unmanned aerial vehicle group initial velocity vector, be arranged every frame without
Man-machine initial position forms the initial position matrix of unmanned aerial vehicle group；
Step 3, the initial nectar source group of artificial bee colony algorithm is set, includes CS nectar source in the initial nectar source group, each
The dimension in nectar source is tieed up for Dim, and each nectar source represents a kind of speed deflection angle scheme of unmanned aerial vehicle group, the one dimensional numerical in each nectar source
Correspond to the speed deflection angle of a frame unmanned plane in unmanned aerial vehicle group；And the maximum iteration of an artificial bee colony algorithm is set
MaxCycles, the maximum times Limit for allowing nectar source not to be updated；
Step 4, speed deflection angle vector is determined at random to each nectar source in initial nectar source group, it is current according to unmanned aerial vehicle group
The velocity vector at moment, the corresponding speed deflection angle vector of the location matrix at current time and each nectar source obtain each nectar source
The feasible location coordinates matrix of subsequent time；It is calculated according to the feasible location coordinates matrix of each nectar source subsequent time each
The corresponding fitness value in nectar source chooses the corresponding nectar source of maximum adaptation angle value as most from the corresponding fitness value in CS nectar source
Excellent nectar source, and record the optimal nectar source Best, the feasible location matrix B estLoc of the corresponding unmanned aerial vehicle group in the optimal nectar source, with
And the fitness value BestFit in the optimal nectar source；
Step 5, setting record matrix, it is full null matrix to initialize the record matrix, and the dimension of the record matrix is
CS, the number not being updated for recording CS nectar source；
Step 6, it employs bee to carry out neighborhood search to each nectar source in currently more excellent nectar source group respectively using CS to be searched
CS that rope arrives new nectar sources calculate the fitness value in the new nectar source of CS searched, according to each of searching the suitable of new nectar source
The fitness value that nectar source is corresponded in angle value and current more excellent nectar source group is answered, the nectar source for selecting fitness value big forms more excellent nectar source group
Better, and the location matrix BetterLoc and fitness matrix B etterfit in each nectar source in more excellent nectar source group are recorded, and
More new record matrix；The original state of the current more excellent nectar source group is initial nectar source group；
Step 7, it is observed by the way of roulette is respectively adopted in bee and is selected in current more excellent nectar source group Better using CS
Nectar source carries out the CS new nectar sources that neighborhood search is searched, and calculates the fitness value in the new nectar source of CS searched, according to
The fitness value in each nectar source in the fitness value in new nectar source and current more excellent nectar source group Better each of is searched, selection is suitable
The nectar source for answering angle value big updates more excellent nectar source group Better, location matrix BetterLoc and fitness matrix B etterfit, and
More new record matrix；The maximum nectar source of fitness value is chosen in more excellent nectar source group Better in the updated, by its maximum adaptation
Angle value if more than the fitness value of optimal nectar source Best, then updates optimal nectar source compared with the fitness value of optimal nectar source Best
Best and the feasible location matrix B estLoc and fitness value BestFit in optimal nectar source；
Non- update times in updated more excellent nectar source group Better are more than or equal to the maximum for allowing nectar source not to be updated
The nectar source of number Limit is rejected, and is randomly generated new nectar source and replaced the nectar source rejected；
Step 8, more excellent nectar source group Better step 7 obtained as current more excellent nectar source group, repeat step 6 and
Step 7, it until reaching the maximum iteration MaxCycles of an artificial bee colony algorithm, and is obtained according to last time iteration
Optimal nectar source determine unmanned aerial vehicle group from current time flight to subsequent time speed deflection angle；
Step 9, the speed deflection angle according to unmanned aerial vehicle group from current time flight to subsequent time is under unmanned aerial vehicle group one
The location matrix and velocity vector at moment are updated；
Step 10, the search step number Step for setting unmanned aerial vehicle group, repeats step 4 and step 9 is Step times total, completes nothing
Man-machine group cooperates with covering routeing process.
It is proposed by the present invention to carry out unmanned aerial vehicle group collaboration covering using artificial bee colony algorithm on the basis of angle searching
Trajectory planning has achieved the purpose that the real-time area coverage of unmanned aerial vehicle group is optimal.It is unmanned aerial vehicle group by using artificial bee colony algorithm
Next track points guided.Since when area coverage declines, the flight attitude of unmanned aerial vehicle group can make fast velocity modulation
It is whole, therefore can accomplish the optimal effect of real-time area coverage.Under emulation experiment institute of the present invention setting parameter, real-time area coverage is not
Less than 99.5%, lay a good foundation for unmanned aerial vehicle group early warning.
Description of the drawings
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below
There is attached drawing needed in technology description to be briefly described, it should be apparent that, the accompanying drawings in the following description is only this
Some embodiments of invention for those of ordinary skill in the art without creative efforts, can be with
Obtain other attached drawings according to these attached drawings.
Fig. 1 is the unmanned aerial vehicle group collaboration covering Route planner provided in an embodiment of the present invention based on artificial bee colony algorithm
Flow diagram；
Fig. 2 is unmanned plane during flying model schematic provided in an embodiment of the present invention；
Fig. 3 is that the real-time overlay area carried out corresponding to the unmanned aerial vehicle group trajectory planning a certain moment using the method for the present invention is shown
It is intended to；
Fig. 4 is to carry out unmanned aerial vehicle flight path using the method for the present invention to plan obtained final flight path route schematic diagram；
Fig. 5 is to carry out unmanned aerial vehicle flight path using the method for the present invention to plan that obtained final coverage rate percentage in real time is illustrated
Figure.
Specific implementation mode
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 describes, 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.
A kind of unmanned aerial vehicle group collaboration covering Route planner based on artificial bee colony algorithm, as shown in Figure 1, the method
Include the following steps：
Step 1, set unmanned aerial vehicle group warning region and can flight range, the unmanned aerial vehicle group include Dim frame unmanned planes,
Set the maximum speed deflection angle of every frame unmanned plane, the maximum operating range of each airborne radar；Dim is just whole more than zero
Number.
Specifically, first, setting the warning region S and feasible flight range A of unmanned plane, unmanned aerial vehicle group needs to maximize real
Shi Xietong covers warning region, while ensureing the feasible flight range that do not fly out.Then, poor using aileron when being turned according to unmanned plane
The principle that the dynamic component by lift provides centripetal force is turned, and maximum speed is obtained when unmanned plane overload is maximum during this
Deflection angle thetam.Finally, onboard radar system running parameter is set, single machine airborne radar is determined by the specific systematic parameter of radar
Maximum operating range Rmax。
Step 1 can be divided into following sub-step：
The warning region S and feasible flight range A of 1.1 setting unmanned planes.Warning region refers to the airborne distribution of unmanned aerial vehicle group
Radar system region to be covered.Feasible flight range includes all warning regions, and unmanned plane must fly in feasible flight range,
If flying out the region, effective survey mission region reduces and is vulnerable to the threats such as potential ground air defense missile weapon.Flight path is advised
The final goal drawn is that real-time maximize covers monitor area so that radar system can get maximum terrestrial information.It is alert
Region S is guarded against to be expressed as：
The specific systematic parameter of 1.2 setting unmanned planes, refers mainly to the maximum speed deflection angle theta of unmanned plane herem.Unmanned plane
It is differential by aileron progress when turning so that fuselage tilts, and is turned using the banks of lift.To unmanned plane carry out by
Power is analyzed：
Lcos γ=mg
mVγ 2/ R=Lsin γ
L indicates that lift, γ indicate that roll angle, i.e. fuselage turning inclination angle, m indicate unmanned aerial vehicle body dead weight, R tables in above formula
Show turning radius, VpIt indicates unmanned plane cruising speed, then has：
R=Vp 2/(g·tanγ)
Tan γ are known as overloading in some documents.Obviously overload is bigger, and turning radius is smaller, suffered by unmanned plane turning about
Shu Yue little.However, there are the upper limits for unmanned plane overload, when overload is maximum, roll angle reaches maximum, and minimum turning half can be obtained at this time
Diameter Rmin.By geometrical relationship, by min. turning radius Rmin, carrier aircraft flying speed VpIt can be obtained with flight time interval delta T
To maximum turning angle (i.e. maximum speed deflection angle) θm.Maximum turning angle θmRefer to the two neighboring moment due to unmanned plane directional velocity
Change the maximum angle generated.
The final purpose of 1.3 setting onboard radar system parameters, unmanned aerial vehicle group trajectory planning is that real-time maximize covers prison
Viewed area, therefore it needs to be determined that single unmanned aerial vehicle onboard radar system sphere of action.Here search coverage is reduced to a circle, if thunder
Maximum operating range up to system is Rmax, maximum radar range can be calculated according to radar equation：
In above formula, PtIndicate that radar system peak power, G indicate that antenna gain, λ indicate that electromagnetic wavelength, σ indicate target
Scattering resonance state, k indicate Boltzmann constant, T0Indicate that normal room temperature, B indicate that receiver bandwidth, F indicate noise coefficient, L tables
Show radar own loss, (S/N)ρminIndicate minimum detectable thresholding.
Step 2, the initial velocity of every frame unmanned plane is set, form unmanned aerial vehicle group initial velocity vector, be arranged every frame without
Man-machine initial position forms the initial position matrix of unmanned aerial vehicle group.
Set unmanned plane initial velocity and position, i.e., speed when unmanned aerial vehicle group is into feasible flight range and residing position
It sets：Remember the initial velocity vector of unmanned aerial vehicle groupThe initial position matrix of unmanned aerial vehicle groupWherein,Indicate the initial velocity of the i-th frame unmanned plane,Indicate the first of the i-th frame unmanned plane
Beginning position, andWithThe x coordinate and y-coordinate of initial time the i-th frame unmanned plane are indicated respectively；I=1,
2 ..., Dim, Dim indicate the sum for including unmanned plane in unmanned aerial vehicle group.
Step 3, the initial nectar source group of artificial bee colony algorithm is set, includes CS nectar source in the initial nectar source group, each
The dimension in nectar source is tieed up for Dim, and each nectar source represents a kind of speed deflection angle scheme of unmanned aerial vehicle group, the one dimensional numerical in each nectar source
Correspond to the speed deflection angle of a frame unmanned plane in unmanned aerial vehicle group；And the maximum iteration of an artificial bee colony algorithm is set
MaxCycles, the maximum times Limit for allowing nectar source not to be updated.
Step 3 can be divided into following sub-step：
3.1 artificial bee colony algorithms encode.The setting initial nectar source number of artificial bee colony algorithm is CS, and each nectar source dimension is
Dim.It is encoded using unmanned plane during flying deflection angle theta as parameter, then every one-dimensional representation in nectar source is that single rack unmanned plane generates
Speed deflection angle, what nectar source indicated is a kind of programme.The characteristics of being planned in conjunction with a step, i.e. artificial bee colony algorithm are only
Next unmanned plane during flying target point can be obtained, then whole process can be regarded as unmanned aerial vehicle group and produce many kinds side in advance
Case has selected the maximum scheme of current area coverage.
3.2 setup parameter value ranges.Boundary condition includes unmanned plane maximum speed deflection angle thetam, allow nectar source not by more
New maximum times by the maximum iteration of Limit, algorithm are MaxCycles, the algorithm end condition (step that unmanned plane is walked
Number Step)) and the feasible flight range A of unmanned plane.When the unmanned plane speed deflection angle that algorithm generates exceeds unmanned plane maximum deflection
When angle, it is modified using following formula：
You need to add is that flight track models.First, the location deflection angle of unmanned plane is determinedUnmanned plane during flying model
Schematic diagram is as shown in Fig. 2, can obtain the location deflection angle of unmanned plane according to simple geometric knowledgeIt is speed deflection angle theta
Half.It is then determined feasible location, feasible location is by maximum speed deflection angle thetamIt determines, feasible location is a circular arc line.
Specially：
(1) location deflection angle is determined.When being encoded as information using unmanned plane speed deflection angle, if it is considered to unmanned plane is even
Fast circumference turning can obtain the location deflection angle of adjacent moment position generation according to simple geometric knowledgeIt is to generate speed
The half of deflection angle theta is spent, i.e.,
(2) feasible location is determined.When unmanned plane speed deflection angle theta is without departing from maximum speed deflection angle thetamWhen, then it generates
Flight path is feasible.When unmanned plane flies at a constant speed, according to the deflection angle of unmanned plane, it may be determined that a camber line, on this camber line
Each point is construed as feasible flight path.To simplify the process, it by this camber line can be approximately a circular arc processing.This approximation
Be reasonable because distance that unmanned plane is flown over along circular arc and along chord length fly over apart from approximately equal.It simultaneously can
To determine that the central angle corresponding to the circular arc is speed deflection angle.
Step 4, speed deflection angle vector is determined at random to each nectar source in initial nectar source group, it is current according to unmanned aerial vehicle group
The velocity vector at moment, the corresponding speed deflection angle vector of the location matrix at current time and each nectar source obtain each nectar source
The feasible location coordinates matrix of subsequent time；It is calculated according to the feasible location coordinates matrix of each nectar source subsequent time each
The corresponding fitness value in nectar source chooses the corresponding nectar source of maximum adaptation angle value as most from the corresponding fitness value in CS nectar source
Excellent nectar source, and record the optimal nectar source Best, the feasible location matrix B estLoc of the corresponding unmanned aerial vehicle group in the optimal nectar source, with
And the fitness value BestFit in the optimal nectar source.
The present invention is based on angle searchings, i.e. solution space is the deflection angle of Dim dimension unmanned planes, that is to say, that the solution of the problem is empty
Between be equivalent to the nectar source of artificial bee colony algorithm.It is described to copy basic artificial bee colony algorithm, algorithm is initialized first, initially
The population number for changing nectar source is CS, that is, initializes CS groups solution.Initialization procedure is equivalent to first determine a kind of feasible flight at random
Scheme is not covered with the maximum constraint of area at this time.
Step 4 specifically includes：
(4a) determines speed deflection angle vector θ at random to k-th of nectar source in initial nectar source groupk：
θk=(θk1,θk2…,θki,…θkDim)
θki=(θmax-θmin)ξ+θmin
Wherein, θkiIndicate the i-th dimension speed deflection angle in k-th of nectar source in initial nectar source group, and k=1 ..., CS, i=
1 ..., Dim, Dim indicate that total dimension in each nectar source, CS indicate the nectar source total number for including in initial nectar source group, θmaxIt indicates
The maximum speed deflection angle of unmanned plane, θminIndicate that the minimum speed deflection angle of unmanned plane, ξ indicate that one of [0,1] section is random
Number；
(4b) is according to the speed of the i-th frame unmanned plane current time t in unmanned aerial vehicle groupThe position of current time tAnd the i-th dimension speed deflection angle theta in k-th of nectar sourcekiObtain k-th of nectar source subsequent time t+1 corresponding i-th
The feasible location coordinate of frame unmanned plane
Wherein, t indicates current time, and the initial value of t is zero, VpIndicate the velocity magnitude that unmanned plane flies at a constant speed,It indicates
The i-th dimension speed deflection angle theta in k-th of nectar sourcekiLocation deflection angle of the corresponding unmanned plane from current time to subsequent time；
It enables i take 1 ..., Dim successively, obtains the corresponding feasible location coordinates matrix of k-th of nectar source subsequent time；
It enables k take 1 ..., CS successively, respectively obtains the corresponding feasible location coordinates matrix of CS nectar source subsequent time;
(4c) fitness value can be calculated by fitness function, with area coverage SkWith the punishment Lim for the warning region that flies outk
Weighting is used as fitness function.If unmanned plane covers one piece of region simultaneously, only note is primary.Calculate the adaptation in k-th of nectar source
Angle value fk:
fk=Sk-ω*Limk
Sk=Sk1∪Sk2∪...Ski...∪SkDim
Wherein, SkIndicate the corresponding unmanned plane during flying scheme in k-th of nectar source to total area coverage of warning region, SkiIt indicates
For the i-th frame unmanned plane in k-th of nectar source to the area coverage of warning region, i=1 ..., Dim, ω indicate the weight of boundary constraint,
LimkIndicate that the corresponding unmanned plane during flying scheme in k-th of nectar source flies out the punishment of warning region, union is sought in ∪ expressions;
It enables k take 1 ..., CS successively, respectively obtains the corresponding fitness value f in CS nectar source1,f2…,fk,…fCS;
The punishment Lim of warning region specifically, the corresponding unmanned plane during flying scheme in k-th of the nectar source sub-step (4c) flies outk,
Specially:
Wherein, limkiIndicate that the corresponding unmanned plane during flying scheme of the i-th dimension numerical value in k-th of nectar source flies out the punishing of warning region
It penalizes, xedgeAnd yedgeWithAnd become, whenWhen between minimum value and maximum value, punish to be 0, xminAnd xmaxIt indicates respectively
The abscissa minimum value and maximum value of warning region, yminAnd ymaxThe ordinate minimum value and maximum of warning region are indicated respectively
Value.
(4d) chooses the corresponding nectar source of maximum adaptation angle value as optimal nectar source from the corresponding fitness value in CS nectar source,
And the optimal nectar source Best is recorded, the feasible location BestLoc of the corresponding unmanned aerial vehicle group in the optimal nectar source and the optimal nectar source
Fitness value BestFit=max (f1,f2…,fk,…fCS)。
Step 5, setting record matrix, it is full null matrix to initialize the record matrix, and the dimension of the record matrix is
CS, the number not being updated for recording CS nectar source.
Step 5 specifically includes：Setting record matrix Record=[r1r2…rk…rCS], rk=0, k=1,2 ..., CS；CS
Indicate the nectar source total number for including in the group of nectar source.
Step 6, it employs bee to carry out neighborhood search to each nectar source in currently more excellent nectar source group respectively using CS to be searched
CS that rope arrives new nectar sources calculate the fitness value in the new nectar source of CS searched, according to each of searching the suitable of new nectar source
The fitness value that nectar source is corresponded in angle value and current more excellent nectar source group is answered, the nectar source for selecting fitness value big forms more excellent nectar source group
Better, and the location matrix BetterLoc and fitness matrix B etterfit in each nectar source in more excellent nectar source group are recorded, and
More new record matrix；The original state of the current more excellent nectar source group is initial nectar source group.
Step 6 specifically includes：
(6a) employ for k-th bee generate at random the first parameter Param ∈ [1, Dim] and the second parameter Neighbour ∈ [1,
CS], Neighbour ≠ k；
(6b) employs for k-th bee to carry out neighborhood search to k-th of nectar source in currently more excellent nectar source group, obtains k-th of new honey
The speed deflection angle vector θ ' in sourcek, the speed deflection angle vector θ ' in k-th of new nectar sourcekMiddle i-th dimension speed deflection angle theta 'kiFor：
Wherein, φ is a random number, and φ ∈ (- 1,1), θkiIt is corresponding for k-th of nectar source in currently more excellent nectar source group
I-th dimension speed deflection angle；
It enables k take 1 ..., CS successively, respectively obtains CS new nectar source corresponding speed deflection angle vector θ '1,θ′2,…,θ
′k,…θ′CS；
In sub-step (6b), the speed deflection angle vector θ ' in k-th of new nectar sourcekMiddle i-th dimension speed deflection angle theta 'kiBeyond nothing
Man-machine maximum speed deflection angle thetamaxWith minimum speed deflection angle thetaminWhen, it is modified using following formula：
(6c) obtains the corresponding feasible location coordinates matrix of CS new nectar source subsequent times and fitness value, by the CS
The fitness value in a new nectar source, which carries out corresponding respectively with the fitness value in CS nectar source in currently more excellent nectar source group, to be compared
Compared with the larger nectar source of reservation fitness value forms more excellent nectar source group Better；
(6d) more new record matrix Record=[r '1r′2…r′k…r′CS]；
Wherein, k=1,2 ..., CS, CS indicate the nectar source total number for including in the group of nectar source, r 'kIndicate kth in record matrix
The updated value of dimension data, f (θk) indicate the fitness value in k-th of nectar source in current more excellent nectar source group, f (θ 'k) indicate currently more
The fitness value in k-th of new nectar source in excellent nectar source group.
Step 7, it is observed by the way of roulette is respectively adopted in bee and is selected in current more excellent nectar source group Better using CS
Nectar source carries out the CS new nectar sources that neighborhood search is searched, and calculates the fitness value in the new nectar source of CS searched, according to
The fitness value in each nectar source in the fitness value in new nectar source and current more excellent nectar source group Better each of is searched, selection is suitable
The nectar source for answering angle value big updates more excellent nectar source group Better, location matrix BetterLoc and fitness matrix B etterfit, and
More new record matrix；The maximum nectar source of fitness value is chosen in more excellent nectar source group Better in the updated, by its maximum adaptation
Angle value if more than the fitness value of optimal nectar source Best, then updates optimal nectar source compared with the fitness value of optimal nectar source Best
Best and the feasible location matrix B estLoc and fitness value BestFit in optimal nectar source；
Non- update times in updated more excellent nectar source group Better are more than or equal to the maximum for allowing nectar source not to be updated
The nectar source of number Limit is rejected, and is randomly generated new nectar source and replaced the nectar source rejected.
Step 7 specifically includes：
The fitness value in CS nectar source in currently more excellent nectar source group Better is normalized in (7a), is normalized
Fitness value (f afterwardss1,fs2,…,fsk,…,fsCS)；fskIndicate the fitness value after the normalization of k-th nectar source, k=1 ...,
CS；
(7b) k-th of observation bee selects nectar source to carry out neighborhood search according to the mode of roulette, random to generate third parameter
rand∈(0,1)；Then haveWherein, a is integer, and a ∈ [1, CS], fsnIndicate n-th of nectar source
Fitness value after normalization, n=1 ..., CS；I.e. k-th observation bee selects a-th of nectar source to carry out neighborhood search；
(7c) k-th of observation bee generates the 4th parameter Param ' ∈ [1, Dim] and the 5th parameter Neighbour ' ∈ at random
[1, CS], Neighbour ' ≠ k；K-th of observation bee carries out neighborhood to a-th of nectar source in currently more excellent nectar source group Better and searches
Rope obtains the speed deflection angle vector θ " in k-th of new nectar sourcek, the speed deflection angle vector θ " in k-th of new nectar sourcekMiddle i-th dimension speed
Deflection angle theta "kiFor：
Wherein, φ is a random number, and φ ∈ (- 1,1), θ 'kiIt is corresponding for k-th of nectar source in currently more excellent nectar source group
I-th dimension speed deflection angle；
It enables k take 1 ..., CS successively, respectively obtains CS new nectar source corresponding speed deflection angle vector θ "1,θ″2,…,θ
″k,…θ″CS；
(7d) obtain the corresponding feasible location coordinates matrix of sub-step (7c) obtained CS new nectar source subsequent times and
Fitness value, by the fitness value adaptation with CS nectar source in currently more excellent nectar source group Better respectively in the CS new nectar sources
Angle value correspond and is compared, and the nectar source for selecting fitness value big corresponds to the honey updated in more excellent nectar source group Better
Source, location matrix BetterLoc and fitness matrix B etterfit；
(7e) more new record matrix Record=[r "1r″2…r″k…r″CS]；
Wherein, k=1,2 ..., CS, CS indicate the nectar source total number for including in updated more excellent nectar source group, r "kIt indicates
Record kth dimension data updated value again, f (θ " in matrixk) indicate in updated more excellent nectar source group k-th new nectar source
Fitness value, f (θ 'k) indicate the fitness value in k-th of nectar source in current more excellent nectar source group.
The maximum nectar source of fitness value is chosen in the more excellent nectar source group Better of (7f) in the updated, by its maximum adaptation degree
Value if more than the fitness value of optimal nectar source Best, then updates optimal nectar source compared with the fitness value of optimal nectar source Best
Best and the feasible location coordinate BestLoc and fitness value BestFit in optimal nectar source；
(7g) traversal record matrix Record=[r "1r″2…r″k…r″CS], find out r "indexCorresponding when >=Limit
Index nectar source, index ∈ (1,2 ..., CS) reject i-th ndex nectar source in updated more excellent nectar source group, and random
It generates new nectar source and replaces the nectar source rejected.
Step 8, more excellent nectar source group Better step 7 obtained as current more excellent nectar source group, repeat step 6 and
Step 7, it until reaching the maximum iteration MaxCycles of an artificial bee colony algorithm, and is obtained according to last time iteration
Optimal nectar source determine unmanned aerial vehicle group from current time flight to subsequent time speed deflection angle.
Step 9, the speed deflection angle according to unmanned aerial vehicle group from current time flight to subsequent time is under unmanned aerial vehicle group one
The location matrix and velocity vector at moment are updated.
First, pass through location deflection angleWith the relationship of speed deflection angle theta, by location deflection angleObtain speed deflection angle
θ.Then, according to speed deflection angle theta and current time speed voldObtain subsequent time speed v after algorithm iterationnew.Finally, complete
The update of pairs of velocity information.
It particularly may be divided into following sub-step：
9.1 calculating speed deflection angles.Location deflection angle when unmanned plane steady circular turnsIt is the two of speed deflection angle theta
Times, actually just obtain location deflection angle after carrying out a step optimizingAnd then speed deflection angle theta can be obtained.
9.2 speed amendments.According to speed deflection angle theta and current time speed voldIt is descended for the moment after can obtaining algorithm iteration
Carve speed vnew, can be represented by the formula：vnew=mod ((vold+ θ), 2 π)
Using the directional velocity as subsequent time directional velocity, so that it may using obtain being turned into as symmetry axis using the directional velocity
One feasible location circular arc carries out the processing of artificial bee colony algorithm optimizing next time.
Step 10, the search step number Step for setting unmanned aerial vehicle group, repeats step 4 and step 9 is Step times total, completes nothing
Man-machine group cooperates with covering routeing process.
Calculate the flight path of next section of unmanned aerial vehicle group, the serial place in usage time after for a period of time again after unmanned plane during flying
Reason completes the planning of unmanned aerial vehicle group flight path.
The effect of the present invention can be described further by following emulation experiment：
1. simulated conditions：
1) setting unmanned plane waits for the environmental information and own system parameter of flight range.Set the monitor area S of unmanned plane
For the rectangular area of 200km × 200km, the feasible flight range A of unmanned plane is the rectangular area of 340km × 340km.Utilize 6
Frame unmanned plane the region fly, each frame unmanned plane during flying initial position be rectangular area frame on any point, each frame without
Man-machine initial velocity is arbitrary.The cruising speed that unmanned plane is arranged is 150m/s, sets carrier aircraft maximum roll angle as 30 °, flies
Interval delta T is 20s, and the maximum speed deflection angle theta of unmanned plane during flying can be calculatedmIt is 45 °.Single rack can be obtained by radar equation
The region overlay area R of unmanned planemax(maximum operating range range), here setting regions be covered as radius be 70km circle.
It is assumed that the unmanned aerial vehicle group flight path of 100 steps of prediction, unmanned aerial vehicle group trajectory planning is carried out using artificial bee colony algorithm：
2) specific algorithm parameter is as shown in the table：
2. emulation content and interpretation of result
Unmanned aerial vehicle group trajectory planning, at a time covering of the unmanned plane to whole region are carried out using artificial bee colony algorithm
Situation is as shown in Figure 3.Rectangular area indicates that monitor area S, " ♂ " indicate that unmanned plane present position and directional velocity, circle indicate
Single rack unmanned plane overlay area；
Fig. 4 gives this trajectory planning finally obtained unmanned aerial vehicle group flight path, and dotted line frame indicates the security area of unmanned plane
Domain S, each curve indicate the planning flight path of a frame unmanned plane.Can be obtained by figure, when a frame unmanned plane close to another frame without
When man-machine, algorithm can automatically force unmanned plane to leave oneself original position and fly to other directions.Due to flight be it is dynamic,
After it is 100% to reach coverage rate, inevitable coverage rate can decline, and algorithm can attempt to make up coverage rate when coverage rate declines.If certain
The monitor area of frame unmanned plane enters without other unmanned planes, then this unmanned plane can turn the covering for making up and losing quickly
Rate, the phenomenon that causing circuit.It can be seen that unmanned aerial vehicle group trajectory planning requires harshness to primary condition.
Fig. 5 gives the real-time coverage rate figure using the method for the present invention, wherein abscissa is search step number, and ordinate is
Coverage rate.It can be obtained by the figure, the real-time coverage rate of unmanned aerial vehicle group is effectively improved using artificial bee colony algorithm.Contact figure
3 can obtain, when the position distribution of each frame unmanned plane it is more uniform when, coverage rate is larger；And when the position of a certain moment unmanned plane
It sets when flying away from monitor area S, the real-time area coverage of unmanned aerial vehicle group will decline.
One of ordinary skill in the art will appreciate that：Realize that all or part of step of above method embodiment can pass through
The relevant hardware of program instruction is completed, and program above-mentioned can be stored in computer read/write memory medium, which exists
When execution, step including the steps of the foregoing method embodiments is executed；And storage medium above-mentioned includes：ROM, RAM, magnetic disc or CD
Etc. the various media that can store program code.
The above description is merely a specific embodiment, but scope of protection of the present invention is not limited thereto, any
Those familiar with the art in the technical scope disclosed by the present invention, can easily think of the change or the replacement, and should all contain
Lid is within protection scope of the present invention.Therefore, protection scope of the present invention should be based on the protection scope of the described claims.
Claims (7)
1. a kind of unmanned aerial vehicle group collaboration covering Route planner based on artificial bee colony algorithm, which is characterized in that the method
Include the following steps：
Step 1, set unmanned aerial vehicle group warning region and can flight range, the unmanned aerial vehicle group include Dim frame unmanned planes, setting
The maximum speed deflection angle of every frame unmanned plane, the maximum operating range of each airborne radar；Dim is the positive integer more than zero；
Step 2, the initial velocity of every frame unmanned plane is set, forms the initial velocity vector of unmanned aerial vehicle group, every frame unmanned plane is set
Initial position, form the initial position matrix of unmanned aerial vehicle group；
Step 3, the initial nectar source group of artificial bee colony algorithm is set, includes CS nectar source, each nectar source in the initial nectar source group
Dimension be Dim dimensions, each nectar source represents a kind of speed deflection angle scheme of unmanned aerial vehicle group, and the one dimensional numerical in each nectar source corresponds to
For the speed deflection angle of a frame unmanned plane in unmanned aerial vehicle group；And the maximum iteration of an artificial bee colony algorithm is set
MaxCycles, the maximum times Limit for allowing nectar source not to be updated；
Step 4, speed deflection angle vector is determined at random to each nectar source in initial nectar source group, according to unmanned aerial vehicle group current time
Velocity vector, the corresponding speed deflection angle vector of the location matrix at current time and each nectar source obtains one under each nectar source
The feasible location coordinates matrix at moment；Each nectar source is calculated according to the feasible location coordinates matrix of each nectar source subsequent time
Corresponding fitness value chooses the corresponding nectar source of maximum adaptation angle value as optimal honey from the corresponding fitness value in CS nectar source
Source, and record the optimal nectar source Best, the feasible location matrix B estLoc of the corresponding unmanned aerial vehicle group in the optimal nectar source, and should
The fitness value BestFit in optimal nectar source；
Step 5, setting record matrix, it is full null matrix to initialize the record matrix, and the dimension of the record matrix is CS, is used
In the number that CS nectar source of record is not updated；
Step 6, it employs bee to carry out neighborhood search to each nectar source in currently more excellent nectar source group respectively using CS to be searched
The new nectar sources CS, the fitness value in the new nectar source of CS searched is calculated, according to the fitness for each of searching new nectar source
The fitness value in nectar source is corresponded in value and current more excellent nectar source group, the nectar source for selecting fitness value big forms more excellent nectar source group
Better, and the location matrix BetterLoc and fitness matrix B etterfit in each nectar source in more excellent nectar source group are recorded, and
More new record matrix；The original state of the current more excellent nectar source group is initial nectar source group；
Step 7, the nectar source selected by the way of roulette is respectively adopted in bee in current more excellent nectar source group Better is observed using CS
The CS new nectar sources that neighborhood search is searched are carried out, the fitness value in the new nectar source of CS searched are calculated, according to search
To each of the fitness value in new nectar source and the fitness value in each nectar source in current more excellent nectar source group Better, select fitness
It is worth big nectar source and updates more excellent nectar source group Better, location matrix BetterLoc and fitness matrix B etterfit, and updates
Record matrix；The maximum nectar source of fitness value is chosen in more excellent nectar source group Better in the updated, by its maximum adaptation angle value
Compared with the fitness value of optimal nectar source Best, if more than the fitness value of optimal nectar source Best, then optimal nectar source Best is updated,
And the feasible location matrix B estLoc and fitness value BestFit in optimal nectar source；
Non- update times in updated more excellent nectar source group Better are more than or equal to the maximum times for allowing nectar source not to be updated
The nectar source of Limit is rejected, and is randomly generated new nectar source and replaced the nectar source rejected；
Step 8, more excellent nectar source group Better step 7 obtained repeats step 6 and step as current more excellent nectar source group
7, the maximum iteration MaxCycles until reaching an artificial bee colony algorithm, and obtained most according to last time iteration
Excellent nectar source determines speed deflection angle of the unmanned aerial vehicle group from current time flight to subsequent time；
Step 9, the speed deflection angle according to unmanned aerial vehicle group from current time flight to subsequent time is to unmanned aerial vehicle group subsequent time
Location matrix and velocity vector be updated；
Step 10, the search step number Step for setting unmanned aerial vehicle group, repeats step 4 and step 9 is Step times total, completes unmanned plane
Group's collaboration covering routeing process.
2. a kind of unmanned aerial vehicle group collaboration covering Route planner based on artificial bee colony algorithm according to claim 1,
It is characterized in that, step 2 specifically includes：
Remember the initial velocity vector of unmanned aerial vehicle groupThe initial position matrix of unmanned aerial vehicle groupWherein,Indicate the initial velocity of the i-th frame unmanned plane,Indicate the first of the i-th frame unmanned plane
Beginning position, and WithThe x coordinate and y-coordinate of initial time the i-th frame unmanned plane are indicated respectively；I=1,
2 ..., Dim, Dim indicate the sum for including unmanned plane in unmanned aerial vehicle group.
3. a kind of unmanned aerial vehicle group collaboration covering Route planner based on artificial bee colony algorithm according to claim 1,
It is characterized in that, step 4 specifically includes：
(4a) determines speed deflection angle vector θ at random to k-th of nectar source in initial nectar source groupk：
θk=(θk1, θk2..., θki... θkDim)
θki=(θmax-θmin)ξ+θmin
Wherein, θkiIndicate the i-th dimension speed deflection angle in k-th of nectar source in initial nectar source group, and k=1 ..., CS, i=1 ...,
Dim, Dim indicate that total dimension in each nectar source, CS indicate the nectar source total number for including in initial nectar source group, θmaxIndicate unmanned plane
Maximum speed deflection angle, θminIndicate that the minimum speed deflection angle of unmanned plane, ξ indicate a random number in [0,1] section；
(4b) is according to the speed of the i-th frame unmanned plane current time t in unmanned aerial vehicle groupThe position of current time t
And the i-th dimension speed deflection angle theta in k-th of nectar sourcekiObtain the corresponding i-th frame unmanned planes of k-th of nectar source subsequent time t+1 can
Row position coordinates
Wherein, t indicates current time, and the initial value of t is zero, VpIndicate the velocity magnitude that unmanned plane flies at a constant speed,It indicates k-th
The i-th dimension speed deflection angle theta in nectar sourcekiLocation deflection angle of the corresponding unmanned plane from current time to subsequent time；
It enables i take 1 ..., Dim successively, obtains the corresponding feasible location coordinates matrix of k-th of nectar source subsequent time；
It enables k take 1 ..., CS successively, respectively obtains the corresponding feasible location coordinates matrix of CS nectar source subsequent time；
(4c) calculates the fitness value f in k-th of nectar sourcek：
fk=Sk-ω*Limk
Sk=Sk1USk2U...Ski...∪SkDim
Wherein, SkIndicate the corresponding unmanned plane during flying scheme in k-th of nectar source to total area coverage of warning region, SkiIndicate kth
For the i-th frame unmanned plane in a nectar source to the area coverage of warning region, i=1 ..., Dim, ω indicate the weight of boundary constraint,
LimkIndicate that the corresponding unmanned plane during flying scheme in k-th of nectar source flies out the punishment of warning region, union is sought in U expressions；
It enables k take 1 .., CS successively, respectively obtains the corresponding fitness value f in CS nectar source1, f2..., fk... fCS；
(4d) chooses the corresponding nectar source of maximum adaptation angle value as optimal nectar source from the corresponding fitness value in CS nectar source, and remembers
Record the optimal nectar source Best, the feasible location BestLoc of the corresponding unmanned aerial vehicle group in the optimal nectar source and the optimal nectar source it is suitable
Answer angle value BestFit=max (f1, f2..., fk... fCS)。
4. a kind of unmanned aerial vehicle group collaboration covering Route planner based on artificial bee colony algorithm according to claim 1,
It is characterized in that, step 5 specifically includes：
Setting record matrix Record=[r1 r2 … rk … rCS], rk=0, k=1,2 ..., CS；rkIndicate k-th of nectar source
The number not being updated, CS indicate the nectar source total number for including in the group of nectar source.
5. a kind of unmanned aerial vehicle group collaboration covering Route planner based on artificial bee colony algorithm according to claim 1,
It is characterized in that, step 6 specifically includes：
(6a) employs bee to generate the first parameter Param ∈ [1, Dim] and the second parameter Neighbour ∈ [1, CS] at random k-th,
Neighbour≠k；
(6b) employs for k-th bee to carry out neighborhood search to k-th of nectar source in currently more excellent nectar source group, obtains k-th new nectar source
Speed deflection angle vector θ 'k, the speed deflection angle vector θ ' in k-th of new nectar sourcekMiddle i-th dimension speed deflection angle theta 'kiFor：
Wherein, φ is a random number, and φ ∈ (- 1,1), θkiFor k-th of nectar source in currently more excellent nectar source group it is corresponding i-th
Tie up speed deflection angle；
It enables k take 1 ..., CS successively, respectively obtains CS new nectar source corresponding speed deflection angle vector θ '1, θ '2..., θ 'k... θ
′CS；
(6c) obtains the corresponding feasible location coordinates matrix of CS new nectar source subsequent times and fitness value, new by the CS
The fitness value in nectar source is carried out corresponding respectively with the fitness value in CS nectar source in currently more excellent nectar source group and is compared, and is protected
The nectar source for staying fitness value larger forms more excellent nectar source group Better；
(6d) more new record matrix Record=[r '1 r′2 … r′k … r′CS]；
Wherein, k=1,2 ..., CS, CS indicate the nectar source total number for including in the group of nectar source, r 'kIndicate kth dimension in record matrix
According to updated value, f (θk) indicate the fitness value in k-th of nectar source in current more excellent nectar source group, f (θ 'k) indicate current more excellent honey
The fitness value in k-th of new nectar source in the group of source.
6. a kind of unmanned aerial vehicle group collaboration covering Route planner based on artificial bee colony algorithm according to claim 5,
It is characterized in that, in sub-step (6b),
The speed deflection angle vector θ ＇ in k-th of new nectar sourcekMiddle i-th dimension speed deflection angle theta 'kiMaximum speed beyond unmanned plane is inclined
Rotational angle thetamaxWith minimum speed deflection angle thetaminWhen, it is modified using following formula：
7. a kind of unmanned aerial vehicle group collaboration covering Route planner based on artificial bee colony algorithm according to claim 1,
It is characterized in that, step 7 specifically includes：
The fitness value in CS nectar source in currently more excellent nectar source group Better is normalized in (7a), after being normalized
Fitness value (fs1, fs2..., fsk..., fsCS)；fskIndicate the fitness value after k-th of nectar source normalization, k=1 ..., CS；
(7b) k-th of observation bee selects nectar source to carry out neighborhood search according to the mode of roulette, random to generate third parameter rand
∈ (0,1)；Then haveWherein, a is integer, and a ∈ [1, CS], fsnIndicate n-th of nectar source normalization
Fitness value afterwards, n=1 ..., CS；
(7c) k-th observation bee the 4th parameter Param ' ∈ [1, Dim] of random generation and the 5th parameter Neighbour ' ∈ [1,
CS], Neighbour ' ≠ k；K-th of observation bee carries out neighborhood search to a-th of nectar source in currently more excellent nectar source group Better
Obtain the speed deflection angle vector θ " in k-th of new nectar sourcek, the speed deflection angle vector θ ＂ in k-th of new nectar sourcekMiddle i-th dimension speed is inclined
Rotational angle theta "kiFor：
Wherein, φ is a random number, and φ ∈ (- 1,1), θ 'kiFor k-th of nectar source in currently more excellent nectar source group it is corresponding
I ties up speed deflection angle；
It enables k take 1 ..., CS successively, respectively obtains CS new nectar source corresponding speed deflection angle vector θ "1, θ ＂2..., θ ＂k... θ
″CS；
(7d) obtains the corresponding feasible location coordinates matrix of CS new nectar source subsequent times and adaptation that sub-step (7c) obtains
Angle value, by the fitness value in the new nectar source the CS fitness value with CS nectar source in currently more excellent nectar source group Better respectively
Correspond and be compared, the nectar source for selecting fitness value big corresponds to nectar source, the position updated in more excellent nectar source group Better
Set matrix B etterLoc and fitness matrix B etterfit；
(7e) more new record matrix Record=[r "1 r″2 … r″k … r″CS]；
Wherein, k=1,2 ..., CS, CS indicate the nectar source total number for including in updated more excellent nectar source group, r "kIndicate record square
Kth dimension data updated value again, f (θ " in battle arrayk) indicate the fitness in k-th of new nectar source in updated more excellent nectar source group
Value, f (θ ＇k) indicate the fitness value in k-th of nectar source in current more excellent nectar source group.
Fitness value maximum nectar source is chosen in the more excellent nectar source group Better of (7f) in the updated, by its maximum adaptation angle value with
The fitness value of optimal nectar source Best compares, and if more than the fitness value of optimal nectar source Best, then updates optimal nectar source Best, with
And the feasible location coordinate BestLoc and fitness value BestFit in optimal nectar source；
(7g) traversal record matrix Record=[r "1 r″2 … r″k … r″CS], find out r "indexCorresponding when >=Limit
Index nectar source, index ∈ (1,2 ..., CS) reject i-th ndex nectar source in updated more excellent nectar source group, and random
It generates new nectar source and replaces the nectar source rejected.
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