CN110069076A - A kind of unmanned plane cluster air battle method for surrounding and seize behavior based on violent wolf - Google Patents
A kind of unmanned plane cluster air battle method for surrounding and seize behavior based on violent wolf Download PDFInfo
- Publication number
- CN110069076A CN110069076A CN201910327148.9A CN201910327148A CN110069076A CN 110069076 A CN110069076 A CN 110069076A CN 201910327148 A CN201910327148 A CN 201910327148A CN 110069076 A CN110069076 A CN 110069076A
- Authority
- CN
- China
- Prior art keywords
- unmanned plane
- seize
- target
- decision
- violent wolf
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/10—Simultaneous control of position or course in three dimensions
- G05D1/101—Simultaneous control of position or course in three dimensions specially adapted for aircraft
- G05D1/104—Simultaneous control of position or course in three dimensions specially adapted for aircraft involving a plurality of aircrafts, e.g. formation flying
Abstract
The present invention discloses a kind of unmanned plane cluster air battle method that behavior is surrounded and seize based on violent wolf, and steps are as follows: step 1: unmanned plane cluster air battle initialization；Step 2: building cluster air combat situation threatens cost；Step 3: learn to determine that topological structure is surrounded in the air battle of unmanned plane cluster using violent wolf situation；Step 4: surrounding topology map is that violent wolf surrounds and seize formation configuration；Step 5: control is surrounded in the unmanned plane formation for surrounding and seize configuration based on violent wolf；Step 6: output cluster air combat formation control instruction.The method of the present invention can support the Air-to-air Combat Environment of dynamic change, the risk of attacks of unmanned plane cluster is effectively reduced, reach the optimal of overall efficiency, guarantee that the stability of Attack, the consistency of attack and higher target wound probability simultaneously, overcomes the difficult points such as dynamic decision, the consistency control in clustered control.
Description
Technical field
The present invention is a kind of unmanned plane cluster air battle method that behavior is surrounded and seize based on violent wolf, belongs to unmanned plane autonomous control neck
Domain.
Background technique
With the complication of combat duty, the cluster dog fight based on multiple UAVs cooperation is had become currently
One of military important subject.On the one hand the demand in unmanned plane practical application, can in single combat duty
It can need to carry multiple-task and sensor load, need to have long voyage, long endurance and high reliability, this is to single unmanned plane
Mission payload require it is very high.On the other hand since single unmanned air vehicle technique encounters bottleneck, it is therefore foreseen that be difficult to obtain in short-term
Breakthrough, size sensor are difficult to further reduce, and xenogenesis sensor integrationization is difficult, and the energy of battery or fuel is close
Degree can not greatly improve, and the limitation of artificial intelligence level causes monomer unmanned plane mission reliability inadequate.To overcome these difficulties,
Cluster is formed by the way of multi-machine collaborative and participates in dog fight, and ammunition and combat duty are assigned to every frame small drone
On, using the cluster air battle scheme of distributed structure/architecture, every frame unmanned plane only needs to be implemented relatively simple subtask, no matter at
Conspicuousness raising can be obtained in sheet or in reliability.
For multiple no-manned plane air battle, unordered operation form will cause the confusion of dog fight situation, so that fighting
Effect is difficult to assess, and is unfavorable for the design and implementation of air battle method.In general, implementing the strike of encirclement property in a manner of formation is one
The effective collaboration strike mode of kind, for multiple target, as multiple UAVs surround target formation center implementation, and with
Fixed angle transmitting ammunition is to maximumlly shoot down each target.Currently used cluster control method, as Artificial Potential Field Method,
Reynolds model, Vicsek model and behaviorbased control method only focus on a certain or several regulations and form into columns or collect
The stability of group's surrounded shape is constituted.If be used for Air-to-air Combat Environment, need to take corresponding formation morphological transformation method in real time, with gram
The uncertainty of air combat situation is taken, while reducing itself risk of dog fight.
It is a kind of typical nature group fistfight phenomenon that violent wolf, which is surrounded and seize, and violent wolf can force in the form of group surrounds
Large-scale prey surrender.Violent wolf surrounds and seize individual interaction and group decision-making mechanism present in behavior, is to realize it in dynamic environment
The basis that lower success is surrounded embodies violent wolf to the compliance of external environment sensing and study, the cooperation to hunting target and surrounding
Property two aspect outstanding advantage.On the other hand, the advantageous feature that violent wolf is surrounded and seize and the sky with unmanned plane cluster under complex environment
War situation is characterized in being consistent.It therefore, is the air battle of unmanned plane cluster by the way that violent wolf to be surrounded and seize to the Context aware policy mappings of process
The learning strategy of form, by the cooperative motion Feature Mapping that violent wolf surrounds and seize behavior be the formation surrounded of unmanned plane cluster air battle with again
Structure control strategy, and then it is corresponding with unmanned plane cluster air combat decision process that violent wolf surrounded and seize behavior, gives full play to violent wolf intelligence
Natural advantage, be solve Antagonistic Environment under multiple no-manned plane dog fight and cooperation effective ways.
Summary of the invention
It is an object of the invention to propose a kind of unmanned plane cluster air battle method for surrounding and seize behavior based on violent wolf, specially one
Air battle strategy and control method of the kind for multiple no-manned plane collaboration dog fight, point of dog fight is participated in for unmanned plane cluster
Cloth decision, effectively promoted in enemy's fixed area all targets it is lethal, while reduce itself danger, reach whole effect
Can it is optimal, overcome the problems, such as the clustered controls such as environmental dynamics, formation stability, behavior congruence.
The present invention is a kind of unmanned plane cluster air battle method that behavior is surrounded and seize based on violent wolf, is used for more rotors or VTOL
Aircraft cluster dog fight, as shown in Figure 1, its main implementation steps is as follows.
Step 1: unmanned plane cluster air battle initialization
S11, unmanned plane during flying state initialization
For the present invention towards following multi-rotor unmanned aerial vehicle model, state of flight is divided into displacement, velocity and acceleration three classes vector,
Amount to 12 states.
Wherein, xi=[pix,piy,piz,ψi]TIndicate the displacement vector of the i-th frame unmanned plane, pixFor X-axis position coordinates, piy
For Y-axis position coordinates, pizFor Height position co-ordinate, ψiFor yaw angle；vi=[vix,viy,viz,ωi]TIndicate the i-th frame unmanned plane
Velocity vector, vixFor along X-axis velocity component, viyFor along Y-axis velocity component, vizFor vertical velocity component, ωiFor yaw angle
Speed；ui=[uix,uiy,uiz,uiψ]TIndicate the acceleration or control vector of the i-th frame unmanned plane, uixFor along X-axis acceleration
Control amount, uiyFor along Y-axis Acceleration Control amount, uizFor vertical acceleration control amount, uiψFor yaw acceleration control amount；KvIt is 4
× 4 diagonal negative coefficient matrixes.
Every frame unmanned plane initial displacement vector is in vector section (xmin,xmax) in generate at random, xminAnd xmaxRespectively most
Small and maximum initial displacement vector；Initial velocity vector is in vector section (vmin,vmax) in generate at random, vminAnd vmaxRespectively
Minimum and maximum initial velocity vector；Initial acceleration control amount is always 0.
S12, the initialization of unmanned plane mission payload
Unmanned plane mission payload is made of weapon load, wherein the weapon load level of the i-th frame unmanned plane is by it to target
Threat coefficient ηiIt indicates.Therefore, mission payload initialization initializes the target threat coefficient of every frame unmanned plane.
S13, the initialization of UAV Communication topology
Descending sort is carried out to the target threat coefficient of every frame unmanned plane, generates y-bend similar to Figure 2 from top to bottom
Tree-shaped Communication topology.Wherein, head node has highest threat level, the second level of child nodes tool as head wolf unmanned plane
There is secondary high threat level, sequentially forms full binary tree topological structure.In the topological structure, the i-th frame unmanned plane nodal community
It can be described by following structural body.
Wherein, B (i) is the communication node architecture body of the i-th frame unmanned plane,For parent node index,For Zuo Zi
Node index,For right child node index.Head node is without father node, i.e.,Leaf node is without child node, i.e.,
S14, the initialization of situation decision matrix
Situation decision matrix Q is by 4N × (N-3)！A element composition, each element can (s a) indicates that wherein N is nothing by Q
Man-machine quantity, s indicate a decision-making state, choose the displacement vector of a frame unmanned plane, total 4N state, and a indicates adoptable
Surround topology configuration., it is specified that communication topology head node and second layer node topology are constant in the present invention, therefore optional topology knot
Structure is altogether (N-3)！Kind.
Step 2: building cluster air combat situation threatens cost
S21, target's center's relative orientation information is obtained
If total M target, the position of j-th of target is obtainedI-th frame is calculated using following formula
Unmanned plane and target's center's relative distance.
Wherein, ρicFor the i-th frame unmanned plane and target's center's relative distance,For target's center
Position coordinates.
S22, calculating and father node UAV Communication distance
The communication distance ρ of the i-th frame unmanned plane and its father node unmanned plane is calculated using following formulaip, wherein pp=[ppx,ppy,
ppz]TIndicate the father node (of the i-th frame unmanned planeFrame) unmanned plane location information.
S23, target's center's sight deflecting angle is calculated
As shown in figure 3, utilizing following formula according to the sight vector at the i-th frame unmanned plane relative target center and itself yaw angle
Calculate the sight deflecting angle θ at relative target centeric。
S24, air combat situation threat cost is calculated
Air combat situation threatens cost to be threatened by target range, target angle threatens and communication distance threat forms, wherein leading to
Communication distance threatens the rapidity for embodying and surrounding target cooperative strike.Target range threat is expressed from the next
Wherein, d is that radius is surrounded in expectation.
Target angle threat is expressed from the next
Communication distance threat can be expressed from the next
Wherein, r is head node unmanned plane index.Therefore, air combat situation threatens cost to be represented by
S=ωrsr+ωasa+ωTsT (9)
Wherein, ωr、ωaWith ωTRespectively target range threatens coefficient, target angle that coefficient and communication distance is threatened to threaten
Coefficient.
Step 3: learn to determine that topological structure is surrounded in the air battle of unmanned plane cluster using violent wolf situation
S31, topological structure is surrounded according to the selection of violent wolf decision probability
Decision-making period is set as Td, in each decision moment t=kTd(k ∈ N), calculating every kind using following formula may take
Surround the violent wolf decision probability P of topological structure aT(a):
Wherein, γ is that decision chooses accelerator coefficient, and coefficient value is bigger, and the decision probability difference for surrounding topological structure is got over
Greatly, stFor a 4N dimensional vector, the decision-making state that every frame unmanned plane is inscribed when current decision is indicated, A is all possible surrounds
Topological structure set.One kind is randomly choosed according to violent wolf decision probability and surrounds topological structure, is denoted as at。
S32, it calculates and surrounds state value under new topological structure
It executes and new surrounds topological structure at, obtain the decision-making state s at next decision momentt+1, substitute into following formula and newly opened up
It flutters under structure and surrounds state value.
S33, situation decision matrix is updated
State value r will be surrounded and substitute into following formula update situation decision matrix Q
Wherein, ntIt is state-topological structure to (st,at) number that repeats.
S34, determine that topological structure is surrounded in unmanned plane cluster air battle
Enable st=st+1, repeat the total N of above step S31 to S34maxIt is secondary, choose the greastest element in situation decision matrix Q
Plain Q*(s,a*), then its corresponding new topological classification a*Topological structure is surrounded in the unmanned plane cluster air battle as newly used.
Step 4: surrounding topology map is that violent wolf surrounds and seize formation configuration
S41, to surround topological structure carry out preamble traversal
As shown in figure 4, the new topological classification a generated according to step 3*, by its internal node interconnecting relation, update every
The communication node architecture body B (i) of frame unmanned plane.It is indexed using left and right child nodeWithRecurrence is to the new topological class
Type, that is, binary tree a*Preamble traversal is carried out, ergodic sequence L is generated.
S42, formation configuration is surrounded and seize by the generation of preamble traversing result
Ergodic sequence L is decomposed into L according to second layer node sequencelAnd LrTwo subsequences, wherein LlIt is with a second layer left side
Child node is the sequence of starting, LrThe sequence for starting that be with the right child node of the second layer be.Then, it obtains according to the following formula and removes head node
Every frame unmanned plane outside unmanned plane it is expected phase angle with respect to the formation configuration of its father node unmanned plane.
Wherein, i is unmanned plane index, and p is its father node unmanned plane index, and r is head node unmanned plane index, formation configuration
It is expected that phase angleIt is defined as desired course spacing (as shown in Fig. 5) of the i-th frame unmanned plane with respect to its father node unmanned plane, θ*
=2 π/N, niIndicate the i-th frame unmanned plane in sequence LlOr LrIn serial number, npIndicate its father node unmanned plane in sequence LlOr LrIn
Serial number.As shown in figure 5, head node unmanned plane remains that with target's center position, in the same horizontal line, i.e., it is with respect to mesh
The desired configuration phase angle at mark center
Step 5: control is surrounded in the unmanned plane formation for surrounding and seize configuration based on violent wolf
S51, the azimuth information for calculating the relatively each target of unmanned plane
For every frame unmanned plane i, the position of j-th of target is obtainedAnd yaw angleUsing
Following formula calculates relative distance ρ of i-th of unmanned plane with respect to j-th of targetijAnd angle
Wherein, pi=[pix,piy,piz]TFor the three-dimensional coordinate vector of the i-th frame unmanned plane.Then, it calculates following mutually orthogonal
Direction vector
S52, calculating unmanned plane surround formation Acceleration Control vector
Define intermediate variableFor i=r, definitionFor i ≠ r, definitionThen the i-th frame unmanned plane air battle surrounds formation Acceleration Control vector and can be expressed from the next
Wherein, K1And K2For the diagonal gain matrix of positive real number, For the displacement vector, velocity vector and acceleration of j-th of target.
For the expectation yaw angle of the i-th frame unmanned plane, guides unmanned plane to be directed toward target's center always and implement strike.The height coordinate of respectively j-th target is divided along X-axis speed
Amount accelerates along Y-axis velocity component, along Z axis velocity component, along X-axis Acceleration Control amount, along Y-axis Acceleration Control amount, along Z axis
Spend control amount, yaw acceleration control amount.
Step 6: output cluster air combat formation control instruction
The the i-th frame unmanned plane Acceleration Control vector u obtained by step 5i=[uix,uiy,uiz,uiψ]T, then i-th is exported
The cluster air combat formation control instruction of frame unmanned plane is to move along the x-axis control instruction uix, control instruction u is moved along Y-axisiy, along Z
Axis moves control instruction uizWith yaw control instruction uiψ。
The present invention proposes that a kind of unmanned plane cluster air battle method that behavior is surrounded and seize based on violent wolf, this method can support dynamic
The risk of attacks of unmanned plane cluster is effectively reduced in the Air-to-air Combat Environment of variation, reaches the optimal of overall efficiency, while guaranteeing that attack is compiled
The stability of team, the consistency of attack and higher target wound probability, overcome dynamic decision in clustered control, one
The difficult points such as cause property control.
Detailed description of the invention
A kind of unmanned plane cluster air battle method flow diagram that behavior is surrounded and seize based on violent wolf of Fig. 1
Seven node full binary tree type cluster communication topological structure schematic diagram of Fig. 2
The sight deflecting angle schematic diagram at Fig. 3 unmanned plane relative target center
The violent wolf of Fig. 4 surrounds and seize unmanned plane and surrounds formation mapping relations schematic diagram
The violent wolf of Fig. 5 surrounds formation desired configuration schematic diagram
Fig. 6 emulation experiment two dimension cluster flight path
Fig. 7 emulation experiment three-dimensional cluster flight path
Fig. 8 emulation experiment unmanned plane and target's center's degree of contrast curve
Fig. 9 emulation experiment air combat situation threatens cost change curve
Label and symbol description are as follows in Fig. 1 to 5:
N situation learns the number of iterations
NmaxMaximum number of iterations
stThe decision-making state of t moment
θicThe sight deflecting angle at the i-th frame unmanned plane relative target center
Radius is surrounded in d expectation
Expectation formation configuration phase angle of the i-th frame unmanned plane with respect to father node unmanned plane
The expectation formation configuration phase angle at head wolf unmanned plane relative target center
Fig. 6, label and symbol description are as follows in 7:
Open symbols unmanned plane and target initial position
Filled marks unmanned plane and target termination position
Arrow direction unmanned plane yaw angle is directed toward
Assault formation profile is surrounded in circle of dotted line expectation
Specific embodiment
The validity of method proposed by the invention is verified below by a specific example.This example uses N=7 frame
Unmanned plane carries out aerial game with M=3 unfriendly target, and 7 frame unmanned planes are implemented to beat by being formed to surround to form into columns to 3 targets
It hits.In this example, it is expected that surrounding radius is d=50m.Target attempts not contained by unmanned plane formation, and having range is dD=
The radius of investigation of 1.2d can detect all unmanned planes within the scope of this, be drawn using repulsive potential field function shown in formula (17)
It leads its implementation and gets rid of movement.
Wherein, j indicates j-th of target, and G is the unmanned plane quantity within radius of investigation, σr=3 be repulsion coefficient, k13=
3、k23=1.5, k14=3, k24=1.5 be status tracking gain,For the yaw angle and yaw angle speed of j-th of target
Degree,Flying height it is expected for target,It is expected that head turns to for target.Meanwhile
To guarantee target without departing from the theater of war, the spacing for setting each target and target's center maintains daWithin=0.8d, lead to
Formula (18) construction attraction potential field function is crossed to realize.
Wherein, σa=6 be attraction potential field coefficient.Target original state is random generation, and wherein initial position range is
[-da,da]×[-da,da] × [5,15], initial velocity range is [- 3,3] × [- 3,3] × [- 1,1], and elemental height range is
[5,15], initial yaw range are [- π, π].Simulation step length selects h=0.01s in example, when emulation a length of 400s, emulate ring
Border is configured to intel i7-4790 processor, and 3.60Ghz dominant frequency, 4G memory, software is MATLAB R2014a version.
Steps are as follows for the concrete practice of this example:
Step 1: unmanned plane cluster air battle initialization
(1) unmanned plane during flying state initialization
It is respectively x that unmanned plane initial displacement vector Lower and upper bounds are set in this examplemax=[d, d, 15, π]TAnd xmin=
[-d,-d,5,-π]T；Initial velocity vector Lower and upper bounds are respectively vmax=[3,3,1,0]TAnd vmin=[- 3, -3, -1,0]T；
Initial acceleration control amount is always 0；Diagonal coefficient matrix Kv=diag { -0.5, -0.3, -0.7, -0.7 }.
(2) unmanned plane mission payload initializes
Setting 7 frame unmanned plane weapon load in this example and threatening coefficient is respectively η1=50, η2=20, η3=20, η4=
10、η5=10, η6=10, η7=10.
(3) UAV Communication topology initializes
It threatens coefficient to carry out descending sort unmanned plane target, generates Communication topology as shown in Figure 2 from top to bottom.
Wherein, head node is as No. 1 unmanned plane, i.e. r=1, the second node layer is 2, No. 3 machines, 4,5,6, No. 7 machines of third layer.At this
In topological structure, each frame UAV Communication node structure body is expressed as B (1)={ 0,2,3 }, B (2)={ Isosorbide-5-Nitrae, 5 }, B (3)
={ 1,6,7 }, B (4)={ 2,0,0 }, B (5)={ 2,0,0 }, B (6)={ 3,0,0 }, B (7)={ 3,0,0 }.
(4) situation decision matrix initializes
Situation decision matrix Q is initialized as 28 × 24 null matrix, i.e., each element be initialized as Q (s, a)=0.
Step 2: building cluster air combat situation threatens cost
(1) target's center's relative orientation information is obtained
Obtain the current location of 3 targetsEvery frame unmanned plane and target's center's phase are calculated using formula (3)
Adjust the distance ρic。
(2) it calculates and father node UAV Communication distance
In addition to No. 1 unmanned plane, remaining unmanned plane obtains its father node unmanned plane current location, using formula (4) calculate its with
The communication distance ρ of father node unmanned planeip。
(3) target's center's sight deflecting angle is calculated
For every frame unmanned plane, current yaw angle is obtained, the sight deflecting angle at relative target center is calculated using formula (5)
θic。
(4) it calculates air combat situation and threatens cost
Formula (6), formula (7), formula (8) is respectively adopted and calculates target range threat, target angle threat and communication distance threat.
It is given to threaten coefficient ωr=0.3, ωa=0.3, ωT=0.4, air combat situation, which is calculated, using formula (9) threatens cost.
Step 3: learn to determine that topological structure is surrounded in the air battle of unmanned plane cluster using violent wolf situation
(1) topological structure is surrounded according to the selection of violent wolf decision probability
Decision-making period is set as Td=10s sets γ=0.5, in each decision moment t=kTd(k ∈ N) is obtained current
When inscribe every frame unmanned plane decision-making state composition column vector st, 24 kinds of violent wolves for surrounding topological structure, which are calculated, using formula (10) determines
Plan probability, and one kind is randomly selected according to decision probability and surrounds topological structure at。
(2) it calculates under new topological structure and surrounds state value
It executes and new surrounds topological structure at, obtain t=(k+1) TdThe decision-making state s at momentt+1, substitute into formula (11) and obtain
atUnder surround state value r.
(3) situation decision matrix is updated
State value r will be surrounded and substitute into formula (12) update situation decision matrix Q.
(4) determine that topological structure is surrounded in unmanned plane cluster air battle
Enable st=st+1, repeat sub-step (1) to (3) total N in above step threemaxIt=30 times, chooses situation and determines
Greatest member Q in plan matrix Q*(s,a*), and new topological classification a*。
Step 4: surrounding topology map is that violent wolf surrounds and seize formation configuration
(1) to surround topological structure carry out preamble traversal
The new topological classification a generated according to step 3*, by its internal node interconnecting relation, update every frame unmanned plane
Communication node architecture body B.It is indexed using left and right child node, recurrence is to the binary tree a*Preamble traversal is carried out, ergodic sequence is generated
L。
(2) formation configuration is surrounded and seize by the generation of preamble traversing result
By the L that ergodic sequence L sequential breakdown is since No. 2 machineslSubsequence and the L since No. 3 machinesrSubsequence, according to formula
(13) the formation configuration for calculating 2 to No. 7 machines with respect to its father node unmanned plane it is expected phase angle(i=2 ..., 7), while No. 1
The desired configuration phase angle at machine relative target center
Step 5: control is surrounded in the unmanned plane formation for surrounding and seize configuration based on violent wolf
(1) azimuth information of the relatively each target of unmanned plane is calculated
For every frame unmanned plane, the position of 3 targets is obtainedAnd yaw angleIt adopts
The distance ρ of its relatively each target is calculated with formula (14)ijAnd angleIn turn, orthogonal direction vector is calculated using formula (15)
rijAnd bij, i=1 ..., 7, j=1,2,3.
(2) it calculates unmanned plane and surrounds formation Acceleration Control vector
For No. 1 machine, definitionFor 2 to No. 7 machines, definitionI=
2 ..., 7, j=1,2,3, p number for corresponding father node unmanned plane, give the diagonal gain matrix K of positive real number1=diag 2,
2,3,3}、K2=diag { 0,0,1.5,1.5 } surrounds formation Acceleration Control using the air battle that formula (16) calculate every frame unmanned plane
Vector.
Step 6: output cluster air combat formation control instruction
The every frame unmanned plane Acceleration Control vector u obtained by step 5i=[uix,uiy,uiz,uiψ]T, it is exported along X-axis
Mobile control instruction uix, control instruction u is moved along Y-axisiy, control instruction u is moved along Z axisizWith yaw control instruction uiψ, i=
1,...,7。
It should be noted that the weapon load in the embodiment of the present invention threatens coefficient, target range to threaten, target angle prestige
The side of body and communication distance threaten task scene sets itself current according to user；The factor v of selection is bigger, this illustrates to use
Family is more biased towards dependence and use in enhancing cluster air combat process to such mission payload.
Two-dimentional and the three-dimensional flight track and scene that Fig. 6,7 provide unmanned plane cluster in this example respectively are shown, can be obtained
Out, although 3 unfriendly targets have the tendency that escaping surrounding and seize in air combat process, 7 frame unmanned planes are still successfully formed in target
The heart is the center of circle, using 50m as the encirclement of radius formation, and 3 targets are surrounded and seize in the ring of encirclement.In addition, by each unmanned plane in Fig. 6,7
Arrow be directed toward and Fig. 8 result, it can be deduced that, every frame unmanned plane is successfully kept and the altitudes such as 3 targets in this example,
Head is directed toward target's center always, so that surrounding formation can cause to kill with high probability to target.Fig. 9 result then table
Bright, unfriendly target in air combat process can be significantly reduced to the threat cost of our unmanned plane cluster in the mentioned method of the present invention,
The safety of effective protection we unmanned plane cluster under dynamic Antagonistic Environment.
Claims (4)
1. a kind of unmanned plane cluster air battle method for surrounding and seize behavior based on violent wolf, it is characterised in that: the method steps are as follows:
Step 1: unmanned plane cluster air battle initialization specifically includes:
S11, unmanned plane during flying state initialization；
S12, the initialization of unmanned plane mission payload, i.e., initialize the target threat coefficient of every frame unmanned plane；
S13, the initialization of UAV Communication topology, i.e., carry out descending sort to the target threat coefficient of every frame unmanned plane, from upper
Lower generation binary tree structure Communication topology；
S14, the initialization of situation decision matrix；
Step 2: building cluster air combat situation threatens cost, specifically includes:
S21, target's center's relative orientation information is obtained；
S22, calculating and father node UAV Communication distance；
S23, target's center's sight deflecting angle is calculated；
S24, air combat situation threat cost is calculated；
Step 3: learn to determine that topological structure is surrounded in the air battle of unmanned plane cluster using violent wolf situation, specifically include:
S31, topological structure is surrounded according to the selection of violent wolf decision probability；
S32, it calculates and surrounds state value under new topological structure；
S33, situation decision matrix is updated；
S34, determine that topological structure is surrounded in unmanned plane cluster air battle；
Step 4: surrounding topology map is that violent wolf surrounds and seize formation configuration, is specifically included:
S41, to surround topological structure carry out preamble traversal；
S42, formation configuration is surrounded and seize by the generation of preamble traversing result；
Step 5: control is surrounded in the unmanned plane formation for surrounding and seize configuration based on violent wolf, is specifically included:
S51, the azimuth information for calculating the relatively each target of unmanned plane；
S52, calculating unmanned plane surround formation Acceleration Control vector；
Step 6: output cluster air combat formation control instruction.
2. a kind of unmanned plane cluster air battle method for surrounding and seize behavior based on violent wolf according to claim 1, it is characterised in that:
Detailed process is as follows by the step S31:
Decision-making period is set as Td, in each decision moment t=kTd(k ∈ N), using following formula calculate every kind may take surround
The violent wolf decision probability P of topological structure aT(a):
Wherein, γ is that decision chooses accelerator coefficient, and coefficient value is bigger, and the decision probability difference for surrounding topological structure is bigger, stFor
One 4N dimensional vector, indicates the decision-making state that every frame unmanned plane is inscribed when current decision, and A surrounds topological structure to be all possible
Set；One kind is randomly choosed according to violent wolf decision probability and surrounds topological structure, is denoted as at。
3. a kind of unmanned plane cluster air battle method for surrounding and seize behavior based on violent wolf according to claim 1, it is characterised in that:
Detailed process is as follows by the step S42:
Ergodic sequence L is decomposed into L according to second layer node sequencelAnd LrTwo subsequences, wherein LlIt is with second layer Zuo Zijie
Sequence of the point for starting, LrThe sequence for starting that be with the right child node of the second layer be；Then, according to the following formula obtain except head node nobody
Every frame unmanned plane outside machine it is expected phase angle with respect to the formation configuration of its father node unmanned plane；
Wherein, i is unmanned plane index, and p is its father node unmanned plane index, and r is head node unmanned plane index, the expectation of formation configuration
Phase angleIt is defined as desired course spacing of the i-th frame unmanned plane with respect to its father node unmanned plane, θ*=2 π/N, niIndicate the i-th frame
Unmanned plane is in sequence LlOr LrIn serial number, npIndicate its father node unmanned plane in sequence LlOr LrIn serial number.
4. a kind of unmanned plane cluster air battle method for surrounding and seize behavior based on violent wolf according to claim 1, it is characterised in that:
Detailed process is as follows by the step S52:
Define intermediate variableFor i=r, definitionFor i ≠ r, definition
Then the i-th frame unmanned plane air battle surrounds formation Acceleration Control vector and can be expressed from the next
Wherein, K1And K2For the diagonal gain matrix of positive real number, For the displacement vector of j-th of target, velocity vector and add
Velocity vector；For the expectation yaw angle of the i-th frame unmanned plane, unmanned plane is guided to begin
It is directed toward target's center eventually and implements strike；Respectively j-th of target
Height coordinate, along X-axis velocity component, along Y-axis velocity component, along Z axis velocity component, along X-axis Acceleration Control amount, along Y-axis
Acceleration Control amount, along Z axis Acceleration Control amount, yaw acceleration control amount.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910327148.9A CN110069076B (en) | 2019-04-23 | 2019-04-23 | Unmanned aerial vehicle cluster air combat method based on wolf warns trapping behavior |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910327148.9A CN110069076B (en) | 2019-04-23 | 2019-04-23 | Unmanned aerial vehicle cluster air combat method based on wolf warns trapping behavior |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110069076A true CN110069076A (en) | 2019-07-30 |
| CN110069076B CN110069076B (en) | 2020-06-09 |
Family
ID=67368415
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910327148.9A Active CN110069076B (en) | 2019-04-23 | 2019-04-23 | Unmanned aerial vehicle cluster air combat method based on wolf warns trapping behavior |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110069076B (en) |
Cited By (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110879609A (en) * | 2019-10-18 | 2020-03-13 | 合肥工业大学 | Method and device for quickly reconstructing unmanned aerial vehicle formation information interaction topology in confrontation environment |
| CN111142553A (en) * | 2019-12-11 | 2020-05-12 | 北京航空航天大学 | Unmanned aerial vehicle cluster autonomous task allocation method based on biological predation energy model |
| CN111176327A (en) * | 2020-01-16 | 2020-05-19 | 北京航空航天大学 | Multi-agent system enclosure control method and system |
| CN111190420A (en) * | 2020-01-07 | 2020-05-22 | 大连理工大学 | Cooperative search and capture method for multiple mobile robots in security field |
| CN111474953A (en) * | 2020-03-30 | 2020-07-31 | 清华大学 | Multi-dynamic-view-angle-coordinated aerial target identification method and system |
| CN111552314A (en) * | 2020-05-09 | 2020-08-18 | 北京航空航天大学 | Self-adaptive formation tracking control method for multiple unmanned aerial vehicles |
| CN111553404A (en) * | 2020-04-23 | 2020-08-18 | 中国电子科技集团公司第五十四研究所 | Communication network node self-adaptive dynamic planning method |
| CN111722643A (en) * | 2020-06-12 | 2020-09-29 | 北京航空航天大学 | Unmanned aerial vehicle cluster dynamic task allocation method imitating wolf colony cooperative hunting mechanism |
| CN112269396A (en) * | 2020-10-14 | 2021-01-26 | 北京航空航天大学 | Unmanned aerial vehicle cluster cooperative confrontation control method for eagle pigeon-imitated intelligent game |
| CN112284188A (en) * | 2020-09-25 | 2021-01-29 | 南京航空航天大学 | Unmanned aerial vehicle attack and defense decision method based on self-adaptive step discrete wolf cluster algorithm |
| CN112925345A (en) * | 2021-01-26 | 2021-06-08 | 北京航空航天大学 | Cluster enclosure control method of unmanned combat aircraft imitating wolf hunting behaviors |
| CN113268078A (en) * | 2021-04-20 | 2021-08-17 | 四川大学 | Target tracking and trapping method for self-adaptive environment of unmanned aerial vehicle group |
| CN113627082A (en) * | 2021-08-03 | 2021-11-09 | 北京航空航天大学 | Unmanned intelligent cluster confrontation control method based on wolf hunting behavior bionics |
| CN114185362A (en) * | 2021-12-07 | 2022-03-15 | 北京航空航天大学 | Unmanned aerial vehicle cluster task dynamic allocation method based on suburb information entropy |
| CN114489124A (en) * | 2022-01-05 | 2022-05-13 | 南京邮电大学 | Method for ensuring consistency action of unmanned aerial vehicle formation |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104794526A (en) * | 2015-05-13 | 2015-07-22 | 哈尔滨工程大学 | Automatic ship anti-collision method optimized by wolf colony search algorithm |
| CN106020215A (en) * | 2016-05-09 | 2016-10-12 | 北京航空航天大学 | Near-distance air combat automatic decision-making method based on single-step prediction matrix gaming |
| CN109327427A (en) * | 2018-05-16 | 2019-02-12 | 中国人民解放军战略支援部队信息工程大学 | A kind of dynamic network variation decision-making technique and its system in face of unknown threat |
-
2019
- 2019-04-23 CN CN201910327148.9A patent/CN110069076B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104794526A (en) * | 2015-05-13 | 2015-07-22 | 哈尔滨工程大学 | Automatic ship anti-collision method optimized by wolf colony search algorithm |
| CN106020215A (en) * | 2016-05-09 | 2016-10-12 | 北京航空航天大学 | Near-distance air combat automatic decision-making method based on single-step prediction matrix gaming |
| CN109327427A (en) * | 2018-05-16 | 2019-02-12 | 中国人民解放军战略支援部队信息工程大学 | A kind of dynamic network variation decision-making technique and its system in face of unknown threat |
Non-Patent Citations (6)
| Title |
|---|
| HAIBIN DUAN等: "Unmanned aerial systems coordinate target allocation based on wolf behaviors", 《SCIENCE CHINA INFORMATION SCIENCES》 * |
| HAOHAN CHEN等: "Multiple Unmanned Aerial Vehicle Autonomous Formation via Wolf Packs Mechanism", 《2016 IEEE/CSAA INTERNATIONAL CONFERENCE ON AIRCRAFT UTILITY SYSTEMS (AUS)》 * |
| 史振庆等: "基于协同攻击区的航空集群最优空间构型研究", 《兵工学报》 * |
| 朱磊等: "航空集群协同探测最优编队构型研究", 《火力与指挥控制》 * |
| 段海滨等: "从狼群智能到无人机集群协同决策", 《中国科学：信息科学》 * |
| 牛轶峰等: "无人机集群作战概念及关键技术分析", 《国防科技》 * |
Cited By (25)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110879609A (en) * | 2019-10-18 | 2020-03-13 | 合肥工业大学 | Method and device for quickly reconstructing unmanned aerial vehicle formation information interaction topology in confrontation environment |
| CN110879609B (en) * | 2019-10-18 | 2023-03-31 | 合肥工业大学 | Method and device for quickly reconstructing unmanned aerial vehicle formation information interaction topology in confrontation environment |
| CN111142553A (en) * | 2019-12-11 | 2020-05-12 | 北京航空航天大学 | Unmanned aerial vehicle cluster autonomous task allocation method based on biological predation energy model |
| CN111190420A (en) * | 2020-01-07 | 2020-05-22 | 大连理工大学 | Cooperative search and capture method for multiple mobile robots in security field |
| CN111190420B (en) * | 2020-01-07 | 2021-11-12 | 大连理工大学 | Cooperative search and capture method for multiple mobile robots in security field |
| CN111176327A (en) * | 2020-01-16 | 2020-05-19 | 北京航空航天大学 | Multi-agent system enclosure control method and system |
| CN111474953A (en) * | 2020-03-30 | 2020-07-31 | 清华大学 | Multi-dynamic-view-angle-coordinated aerial target identification method and system |
| CN111474953B (en) * | 2020-03-30 | 2021-09-17 | 清华大学 | Multi-dynamic-view-angle-coordinated aerial target identification method and system |
| CN111553404A (en) * | 2020-04-23 | 2020-08-18 | 中国电子科技集团公司第五十四研究所 | Communication network node self-adaptive dynamic planning method |
| CN111553404B (en) * | 2020-04-23 | 2023-05-23 | 中国电子科技集团公司第五十四研究所 | Self-adaptive dynamic planning method for communication network node |
| CN111552314B (en) * | 2020-05-09 | 2021-05-18 | 北京航空航天大学 | Self-adaptive formation tracking control method for multiple unmanned aerial vehicles |
| CN111552314A (en) * | 2020-05-09 | 2020-08-18 | 北京航空航天大学 | Self-adaptive formation tracking control method for multiple unmanned aerial vehicles |
| CN111722643A (en) * | 2020-06-12 | 2020-09-29 | 北京航空航天大学 | Unmanned aerial vehicle cluster dynamic task allocation method imitating wolf colony cooperative hunting mechanism |
| CN112284188A (en) * | 2020-09-25 | 2021-01-29 | 南京航空航天大学 | Unmanned aerial vehicle attack and defense decision method based on self-adaptive step discrete wolf cluster algorithm |
| CN112269396A (en) * | 2020-10-14 | 2021-01-26 | 北京航空航天大学 | Unmanned aerial vehicle cluster cooperative confrontation control method for eagle pigeon-imitated intelligent game |
| CN112269396B (en) * | 2020-10-14 | 2021-09-28 | 北京航空航天大学 | Unmanned aerial vehicle cluster cooperative confrontation control method for eagle pigeon-imitated intelligent game |
| CN112925345A (en) * | 2021-01-26 | 2021-06-08 | 北京航空航天大学 | Cluster enclosure control method of unmanned combat aircraft imitating wolf hunting behaviors |
| CN112925345B (en) * | 2021-01-26 | 2022-01-28 | 北京航空航天大学 | Cluster enclosure control method of unmanned combat aircraft imitating wolf hunting behaviors |
| CN113268078A (en) * | 2021-04-20 | 2021-08-17 | 四川大学 | Target tracking and trapping method for self-adaptive environment of unmanned aerial vehicle group |
| CN113627082A (en) * | 2021-08-03 | 2021-11-09 | 北京航空航天大学 | Unmanned intelligent cluster confrontation control method based on wolf hunting behavior bionics |
| CN113627082B (en) * | 2021-08-03 | 2024-02-13 | 北京航空航天大学 | Unmanned intelligent cluster countermeasure control method based on wolf group hunting behavior bionical |
| CN114185362A (en) * | 2021-12-07 | 2022-03-15 | 北京航空航天大学 | Unmanned aerial vehicle cluster task dynamic allocation method based on suburb information entropy |
| CN114185362B (en) * | 2021-12-07 | 2023-06-06 | 北京航空航天大学 | Unmanned aerial vehicle cluster task dynamic allocation method based on suburban wolf information entropy |
| CN114489124A (en) * | 2022-01-05 | 2022-05-13 | 南京邮电大学 | Method for ensuring consistency action of unmanned aerial vehicle formation |
| CN114489124B (en) * | 2022-01-05 | 2023-07-28 | 南京邮电大学 | Method for guaranteeing consistency action of unmanned aerial vehicle formation |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110069076B (en) | 2020-06-09 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110069076A (en) | A kind of unmanned plane cluster air battle method for surrounding and seize behavior based on violent wolf | |
| CN108680063B (en) | A kind of decision-making technique for extensive unmanned plane cluster dynamic confrontation | |
| CN106705970B (en) | Multi-unmanned aerial vehicle collaborative path planning method based on ant colony algorithm | |
| CN106843269B (en) | A kind of unmanned plane formation method based on small birds cluster fly mechanics | |
| CN104881043B (en) | A kind of multiple no-manned plane for many dynamic objects is intelligent coordinated to examine printing method | |
| CN110069075B (en) | Cluster super-mobile obstacle avoidance method imitating pigeon group emergency obstacle avoidance mechanism | |
| Li et al. | Path planning of unmanned aerial vehicle based on improved gravitational search algorithm | |
| CN108594853A (en) | Unmanned plane approach to formation control | |
| CN109669475A (en) | Multiple no-manned plane three-dimensional formation reconfiguration method based on artificial bee colony algorithm | |
| Chen et al. | Path planning under obstacle-avoidance constraints based on ant colony optimization algorithm | |
| CN106979784A (en) | Non-linear trajectory planning based on mixing dove group's algorithm | |
| CN107063255A (en) | A kind of three-dimensional Route planner based on improvement drosophila optimized algorithm | |
| CN109709981B (en) | Competitive antagonism method for unmanned aerial vehicle group | |
| CN113282061A (en) | Unmanned aerial vehicle air game countermeasure solving method based on course learning | |
| CN114840020A (en) | Unmanned aerial vehicle flight path planning method based on improved whale algorithm | |
| Chen et al. | Design and verification of UAV maneuver decision simulation system based on deep q-learning network | |
| CN113741186B (en) | Double-aircraft air combat decision-making method based on near-end strategy optimization | |
| Mengying et al. | Online path planning algorithms for unmanned air vehicle | |
| Chen et al. | Deep reinforcement learning based strategy for quadrotor UAV pursuer and evader problem | |
| CN114911269B (en) | Networking radar interference strategy generation method based on unmanned aerial vehicle group | |
| CN114167899B (en) | Unmanned plane bee colony collaborative countermeasure decision-making method and system | |
| CN111773722A (en) | Method for generating maneuver strategy set for avoiding fighter plane in simulation environment | |
| Xie et al. | Self-organization method of usv swarm target strike task based on ant colony algorithm | |
| Wentao et al. | UAV Air Combat Maneuver Decision Algorithm Based on Dynamic Grid Planning Environment | |
| Meng et al. | UAV Attack and Defense Optimization Guidance Method Based on Target Trajectory Prediction |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |