CN108549402A - Unmanned aerial vehicle group method for allocating tasks based on quantum crow group hunting mechanism - Google Patents
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Abstract
The present invention relates to a kind of unmanned aerial vehicle group method for allocating tasks based on quantum crow group hunting mechanism, including：Establish the unmanned aerial vehicle group Task Assignment Model from multiple starting points to multiple tasks, including unmanned plane model number, starting and terminal point and distribution model；Initialize quantum crow group；Fitness calculating carried out to every quantum crow according to fitness function, the position of the corresponding quantum crow of calculated fitness function minimum value saves as global optimum's food position；Update the quantum position and position of every quantum crow；Fitness calculating is carried out to every quantum crow according to fitness function, determine the hiding food position of every quantum crow, optimal food position so far is found simultaneously, and global optimum's food position is exported if reaching greatest iteration algebraically, is mapped as task allocation matrix.The present invention solves discrete multiple constraint object function Solve problems, and discrete quantum crow algorithm has fast convergence rate, the high advantage of convergence precision as Evolution Strategies.
Description
Technical field
The present invention relates to a kind of unmanned aerial vehicle group method for allocating tasks, especially a kind of base is based on quantum crow group hunting mechanism
Unmanned aerial vehicle group method for allocating tasks, belong to unmanned plane autonomous control field.
Background technology
Unmanned plane is also known as unmanned vehicle (Unmanned Aerial Vehicle, UAV), its use process
In, operating personnel need not be carried, lift is provided with air force, can be flown by remote control or in pre-programmed control
System is lower to carry out autonomous flight, and particular task is executed by carrying task device.Unmanned plane has compact, using flexible, hidden
The advantages that covering property is good, adaptable, can under various severe, dangerous and extreme environments, complete some mankind can not reach and
The particular job and task being engaged in.The development of unmanned plane, production and application cost are far below manned aircraft, therefore in army
Thing and civil field suffer from wide application space.
The distribution of unmanned plane task is one of the key technology of unmanned plane autonomous control, is that unmanned plane realizes intelligent, autonomous
An important factor for flight and task execution.The distribution of unmanned plane task refers to during the entire process of task execution, by certain
Method for allocating tasks is that unmanned plane determines whether execution task and executes which kind of task, on the one hand rational task distribution can be protected
Demonstrate,prove the Least-cost of unmanned plane, the completion each task that another aspect again can be best.
It finds by prior art documents, Tang Chuanlin etc. exists《Electric light and control》
" the more UCAV air-to-ground attacks targets based on game theory point delivered on (2011, Vol.18, No.10, pp.28-31)
With " in propose Task Assignment Model, seek optimal task assignment with game theory algorithm, but algorithm model is complicated, precision is not
It is enough high and computationally intensive.MehmetDeng《Information Sciences》(2014, Vol.255,
No.10, pp.28-31) on " the Approximating the optimal mapping for weapon target that deliver
The method solution Weapon-Target Assignment Problem of assignment by fuzzy reasoning " fuzzy reasonings, but reasoning
Process is complicated, and computationally intensive, practicability is not high.With the development of intelligent heuristics computing technique, Intelligent Optimization Technique has been answered
For in the Task Allocation Problem of multiple no-manned plane.Li Wei etc. exists《Control and decision》(2010,Vol.25,No.9,pp.1359–
1364) particle cluster algorithm is applied to multiple no-manned plane by " the multiple no-manned plane method for allocating tasks based on particle cluster algorithm " delivered on
Task Allocation Problem, but particle cluster algorithm is easily trapped into local optimum, and convergence precision is to be improved.Li Yan etc. exists《Space flight
Journal》" the Cooperative Air Combat based on SA-DPSO hybrid optimization algorithms proposed on (2014, Vol.25, No.9, pp.1626-631)
Simulated annealing and particle cluster algorithm are combined and carry out unmanned plane task distribution by Fire Distribution ", and this method has preferably
Convergence rate, but it is easily trapped into dimension calamity, optimizing performance is inadequate.
Because above-mentioned unmanned plane method for allocating tasks is all nonlinear solution method, hold very much during solution
It easily is absorbed in local extremum, hardly results in globally optimal solution.And existing unmanned plane method for allocating tasks is carrying out unmanned aerial vehicle group
Various evaluation indexes and constraint are seldom considered in task distribution, therefore its application range is limited.From this, finding new appoint
Distribution method of being engaged in is of great value to improve the performance of unmanned plane operation.
Invention content
For the above-mentioned prior art, considering more terminals of a lot of points and suitable the technical problem to be solved by the present invention is to provide a kind of
Together in the unmanned aerial vehicle group method for allocating tasks based on quantum crow group hunting mechanism of dispersed problem.
In order to solve the above technical problems, a kind of unmanned aerial vehicle group task distribution based on quantum crow group hunting mechanism of the present invention
Method includes the following steps：
Step 1：Initialization greatest iteration algebraically is Tmax, establish the unmanned aerial vehicle group task from multiple starting points to multiple tasks
Distribution model：Assuming that there is the unmanned plane of U kind models to execute Q task from M starting point；
If the coordinate of m-th of starting point of unmanned plane isWherein 1≤m≤M, unmanned plane are appointed for q-th
The coordinate of business isWherein 1≤q≤Q is divided into L type to all unmanned planes according to starting point and model,
Wherein L=U × M, i.e. same type unmanned plane starting point having the same and belong to same model；
According to the starting point of the model of l type unmanned planes, the starting point coordinate for obtaining l type unmanned planes isWherein l=1,2 ..., L, then the starting point of l types unmanned plane be at a distance from q-th of task
Dl,qAnd meet：
The task allocation matrix of unmanned aerial vehicle group L row Q row allocation matrix A={ al,q|al,q∈{0,1}}L×QIt indicates, if l
The unmanned plane of a model executes q-th of task, then al,q=1, otherwise al,q=0；
If each unmanned plane has the D kind weapons, l type unmanned planes to be using the probability of d kind weaponsL types nobody
Machine is δ using the cost of d kind weaponsl,d, wherein 1≤d≤D, 1≤l≤L, the killing rate of d kinds q-th of task of weapon pair areWherein 1≤d≤D, 1≤q≤Q, if unmanned plane injures probability matrix P={ Pl,q,d|Pl,q,d∈[0,1]}L×Q×D,
Pl,q,dIt is degree of injuring and satisfaction of the l types unmanned plane using d kind q-th of task of weapon pair：Degree of the injuring threshold value of q-th of task is Wq, wherein 1≤q≤Q, if the value of q-th of task
For Vq, the quantity for possessing l type unmanned planes is Bl, the unmanned plane maximum formation number to the attack of q-th of task is Cq, l types without
Man-machine ultimate run is Rl, the ultimate run of whole unmanned planes is Omax, the flying speed of l type unmanned planes is Zl, whole nothings
The man-machine maximum flight time is Zmax；
Unmanned plane Task Assignment Model is respectively by target value revenue function, flying distance function, consumption bullet amount cost function
With target coverage rate function representation：
(1) normalization target value revenue function is：Wherein A
For task allocation matrix, A={ al,q|al,q∈{0,1}}L×Q, Pl,q,dD kinds q-th of task of weapon pair is used for l type unmanned planes
Injure probability, d is the type using weapon, and the type needs for the weapon that the unmanned planes of l types uses are set in advance, VqIt is
The value of q task, N are the unmanned plane number of actual participation task in task distribution,
It is worth for maximum task, max is to seek max function；
(2) normalization flying distance function is：
Wherein λ1, λ2For the weight of two factors, λ1+λ2=1, λ1,λ2>=0,For the length of longest path,RlFor the ultimate run of l type unmanned planes；
(3) normalization consumption bullet cost function is：The unmanned plane of wherein l types makes
The type needs of weapon are set in advance, δmaxFor maximum cost,
(4) normalization target coverage degree function is：
The unmanned aerial vehicle group Task Assignment Model meets following constraints：
(1) task troops constrain：The unmanned plane of each type sets out number and no more than possesses the type unmanned plane number
Mesh,
(2) unmanned plane combat radius constrains：Ensure the flying distance of unmanned plane within its combat radius, al,q×(Dl,q-
Rl)≤0 (l=1,2 ..., L；Q=1,2 ..., Q)；
(3) to the constraint of target Damage degree：The unmanned plane of execution task q is to the degree of injuring of task q not less than the task
Degree of injuring threshold value,Pl,q,dFor l
Type unmanned plane is using the probability of injuring of d kinds q-th of task of weapon pair, and d is the type using weapon, and the unmanned plane of l types uses
Weapon type needs be set in advance；
(4) constraint of the unmanned plane number of target of attack：It is maximum that it is no more than to the unmanned plane number of q-th of task attack
Formation number, i.e.,
(5) constraint of the voyage of target of attack：I.e. the voyage of strike mission is no more than given ultimate run,OmaxFor the ultimate run of all unmanned planes；
(6) constraint of the time of target of attack：The time of strike mission is no more than given maximum time,ZmaxFor the maximum flight time of all unmanned planes；
Population scale K, the dimension J=L × Q of optimization problem for determining quantum crow group, are incremented by according to l, mode incremental q
Arrange unmanned aerial vehicle group task allocation matrix A={ al,q|al,q∈{0,1}}L×QIn element, useElement in corresponding record unmanned aerial vehicle group task allocation matrix A；
Step 2：Initialize quantum crow group：
By the quantum position of i-th quantum crowBe set as per one-dimensionalWherein 1≤i≤K, 1≤j≤J, and the quantum position of i-th quantum crow is measured, obtain i-th
The position of quantum crowThe hiding food position of i-th quantum crow of initialization isWherein 1≤i≤K, t are iterations, set t=0 at the beginning；
To the jth dimension of the quantum position of i-th quantum crowIt measures, obtains the position of i-th quantum crow
Jth is tieed upWherein 1≤i≤K, 1≤j≤J,Be meet it is equally distributed
Random number；
Step 3：Fitness calculating, calculated fitness function are carried out to every quantum crow according to fitness function
The position of the corresponding quantum crow of minimum value saves as global optimum's food position
Step 4：Update the quantum position and position of every quantum crow:
I-th quantum crow randomly selects another quantum crow s in quantum crow group, followed by quantum crow s
It is found that the food position hidden by quantum crow s, quantum crow s find that the perception probability being followed is μ, ifQuantum crow i carries out the update of quantum position by strategy 1, and otherwise quantum crow i passes through tactful 2 amounts of progress
The update of sub- position；
Strategy 1 meets：I-th quantum crow carries out the update of position according to food position hiding quantum crow s, the
The renewal equation at the quantum rotation angle of the jth dimension of i quantum crow isWherein e1For
Constant determines the influence degree for guiding the position of the quantum crow to develop the quantum crow, and H is Flight Length；
Strategy 2 meets：I-th quantum crow carries out position according to itself hiding food position and optimal food position
Update, the renewal equation at the quantum rotation angle of the jth dimension of i-th quantum crow isWherein e2,e3For constant, determines and guide the quantum crow
The influence degree that develops to the quantum crow of position；
The evolution process of quantum position is as follows：
Wherein ζ=0.15/J is mutation probability, and abs () is to seek ABS function；
The position of quantum crow is obtained to quantum crow quantum position measurement, measurement rules are as follows：
Wherein 1≤i≤K, 1≤j≤J,It is to meet equally distributed random number；
Step 5：Fitness calculating is carried out to every quantum crow according to fitness function, determines every quantum crow
Hiding food position, while finding to the optimal food position of current iteration algebraically；
By the position of i-th quantum crowIt is assigned to task allocation matrix A, according toCarry out fitness calculating；
The hiding food position of quantum crow is chosen using greedy selection strategy, ifThenOtherwise
Step 6：If reaching greatest iteration algebraically Tmax, algorithm termination, execution step 7；Otherwise, t=t+1 is enabled, is returned
Step 4 continues；
Step 7：Global optimum's food position is exported, task allocation matrix is mapped as.
A kind of unmanned aerial vehicle group method for allocating tasks based on quantum crow group hunting mechanism of the present invention further includes：
Fitness evaluation process is as follows in step 3：First by the position of t i-th quantum crow of generationIt is assigned to and appoints
Be engaged in allocation matrix A, wherein the jth dimension of t i-th quantum crow of generationIt is assigned to al,q, according toFitness calculating is carried out, wherein
c1,c2,c3,c4,c5,c6For penalty coefficient, ω1,ω2,ω3,ω4For weighted factor, ω1+ω2+ω3+ω4=1,0
≤ω1,ω2,ω3,ω4≤ 1, max are to seek max function, and min is to seek minimum value function.
Beneficial effects of the present invention：The present invention is directed to the deficiency of existing unmanned aerial vehicle group method for allocating tasks, it is proposed that a kind of
Consider the unmanned aerial vehicle group Task Assignment Model of a lot of more terminals of point, while proposing a kind of quantum crow being suitable for dispersed problem
Group hunting mechanism is used to solve the Task Allocation Problem of unmanned aerial vehicle group.Compared with prior art, the present invention has fully considered nobody
Group of planes task encountered during distributing the case where executing multiple tasks from multiple starting points, while considering target value income
Function, flying distance function, consumption bullet amount cost function and the multiple targets of target coverage rate function, have the following advantages：
(1) present invention solves discrete multiple constraint object function Solve problems, and futuramic discrete quantum crow is calculated
Method is handled different target function as Evolution Strategies, using linear weight, and designed method has fast convergence rate,
The high advantage of convergence precision.
(2) relative to existing unmanned aerial vehicle group method for allocating tasks, the present invention can be solved effectively to the more mesh of unmanned aerial vehicle group
Constraint requirements are marked, illustrate that the applicability of this method is wider.
(3) it is more excellent than population to show that unmanned aerial vehicle group method for allocating tasks proposed by the invention can be obtained for simulation result
Change (Particle Swarm Optimization, PSO) method and realize more reasonably unmanned plane task allocation plan, to say
The validity of this method is illustrated.
Description of the drawings
Fig. 1 is unmanned aerial vehicle group task allocation process diagram；
Fig. 2 is the flow chart of quantum crow location updating；
Fig. 3 is the convergence curve that two methods realize the distribution of multiple no-manned plane task.
Specific implementation mode
The specific embodiment of the invention is illustrated below in conjunction with the accompanying drawings.
As shown in Figure 1, technical solution of the present invention includes the following steps：
Step 1：Establish the unmanned aerial vehicle group Task Assignment Model from multiple starting points to multiple tasks, it is assumed that there are U kind types here
Number unmanned plane execute Q task from M starting point.
If the coordinate of m-th of starting point of unmanned plane isWherein 1≤m≤M, unmanned plane are appointed for q-th
The coordinate of business isWherein 1≤q≤Q.L can be divided into according to its starting point and model to all unmanned planes
Type, wherein L=U × M, i.e. same type unmanned plane starting point having the same and belong to same model.
According to the starting point of the model of l type unmanned planes, the starting point coordinate that can obtain l type unmanned planes isWherein l=1,2 ..., L, the then starting point of l types unmanned plane and q-th of task distance Dl,q
For
The task allocation matrix of unmanned aerial vehicle group can use L row Q row allocation matrix A={ al,q|al,q∈{0,1}}L×QIt indicates, if
The unmanned plane of first of model executes q-th of task, then al,q=1, otherwise al,q=0.
If each unmanned plane has the D kind weapons, l type unmanned planes to be using the probability of d kind weaponsL types nobody
Machine is δ using the cost of d kind weaponsl,d, wherein 1≤d≤D, 1≤l≤L.The killing rate of d kinds q-th of task of weapon pair isWherein 1≤d≤D, 1≤q≤Q, if unmanned plane injures probability matrix P={ Pl,q,d|Pl,q,d∈[0,1]}L×Q×D,
Pl,q,dIt is that l types unmanned plane is using the degree of injuring of d kind q-th of task of weapon pairQ
Degree of the injuring threshold value of a task is Wq, wherein 1≤q≤Q.If the value of q-th of task is Vq, possess the quantity of l type unmanned planes
For Bl, the unmanned plane maximum formation number to the attack of q-th of task is Cq, the ultimate run of l type unmanned planes is Rl, institute whether there is or not
Man-machine ultimate run is Omax, the flying speed of l type unmanned planes is Zl, the maximum flight time of all unmanned planes is Zmax。
Unmanned plane Task Assignment Model can be by target value revenue function, flying distance function, consumption bullet amount cost function
With target coverage rate function representation.
(1) normalization target value revenue function is：Wherein A
For task allocation matrix, A={ al,q|al,q∈{0,1}}L×Q, Pl,q,dD kinds q-th of task of weapon pair is used for l type unmanned planes
Injure probability, d is the type using weapon, and the type needs for the weapon that the unmanned planes of l types uses are set in advance.VqIt is
The value of q task, N are the unmanned plane number of actual participation task in task distribution, It is worth for maximum task, max is to seek max function.
(2) normalization flying distance function is：
Wherein λ1, λ2For the weight of two factors, λ1+λ2=1, λ1,λ2>=0,For the length of longest path,RlFor the ultimate run of l type unmanned planes.
(3) normalization consumption bullet cost function is：The unmanned plane of wherein l types makes
The type needs of weapon are set in advance, δmaxFor maximum cost,
(4) normalization target coverage degree function is：
In addition to this, following constraints should also be met：
(1) task troops constrain.That is the unmanned plane of each type sets out number and no more than possesses the type unmanned plane
Number.
(2) unmanned plane combat radius constrains.It must assure that the flying distance of unmanned plane within its combat radius.al,q
×(Dl,q-Rl)≤0 (l=1,2 ..., L；Q=1,2 ..., Q).
(3) to the constraint of target Damage degree.This should be not less than to the degree of injuring of task q by executing the unmanned plane of task q
Degree of the injuring threshold value of business.
Pl,q,dIt is l types unmanned plane using the probability of injuring of d kinds q-th of task of weapon pair, d is the type for using weapon, the nothing of l types
The type needs of the man-machine weapon used are set in advance.
(4) constraint of the unmanned plane number of target of attack.It is maximum that it is no more than to the unmanned plane number of q-th of task attack
Formation number, i.e.,
(5) constraint of the voyage of target of attack.I.e. the voyage of strike mission is no more than given ultimate run.OmaxFor the ultimate run of all unmanned planes.
(6) constraint of the time of target of attack.I.e. the time of strike mission is no more than given maximum time.ZmaxFor the maximum flight time of all unmanned planes.
Then, it is determined that population scale K, the dimension J=L × Q of optimization problem of quantum crow group, it is incremental to be incremented by q according to l
Mode arranges unmanned aerial vehicle group task allocation matrix A={ al,q|al,q∈{0,1}}L×QIn element, useElement in corresponding record unmanned aerial vehicle group task allocation matrix A.
Step 2：Initialize quantum crow group.
By the quantum position of i-th quantum crowBe set as per one-dimensionalWherein 1≤i≤K, 1≤j≤J, and the quantum position of i-th quantum crow is measured to obtain i-th
The position of quantum crowThe hiding food position of i-th quantum crow of initialization isWherein 1≤i≤K.T is iterations, sets t=0 at the beginning.
To the jth dimension of the quantum position of i-th quantum crowIt measures, obtains the position of i-th quantum crow
Jth is tieed upWherein 1≤i≤K, 1≤j≤J,Be meet it is equally distributed
Random number.
Step 3：Fitness calculating, calculated fitness function are carried out to every quantum crow according to fitness function
The position of the corresponding quantum crow of minimum value saves as global optimum's food position
The process of fitness evaluation is as follows：
First by the position of t i-th quantum crow of generationIt is assigned to task allocation matrix A, wherein i-th amount of t generations
The jth of sub- crow is tieed upIt is assigned to al,q.According toFitness calculating is carried out, wherein
c1,c2,c3,c4,c5,c6For penalty coefficient, ω1,ω2,ω3,ω4For weighted factor, ω1+ω2+ω3+ω4=1,0
≤ω1,ω2,ω3,ω4≤ 1, max are to seek max function, and min is to seek minimum value function.
Step 4：Update the quantum position and position of every quantum crow.
As shown in Fig. 2, i-th quantum crow randomly selects another quantum crow s in quantum crow group, followed by
Quantum crow s come find by quantum crow s hide food position.Quantum crow s has found that the perception probability being followed is μ.IfQuantum crow i carries out the update of quantum position by strategy 1, and otherwise quantum crow i passes through tactful 2 amounts of progress
The update of sub- position.
Strategy 1：I-th quantum crow carries out the update of position according to food position hiding quantum crow s.I-th
The renewal equation at quantum rotation angle of the jth dimension of quantum crow isWherein e1It is normal
Number determines the influence degree for guiding the position of the quantum crow to develop the quantum crow, and H is Flight Length.
Strategy 2：I-th quantum crow carries out position more according to itself hiding food position and optimal food position
Newly.The renewal equation at the quantum rotation angle of the jth dimension of i-th quantum crow isWherein e2,e3For constant, determines and guide the quantum crow
The influence degree that develops to the quantum crow of position.
The evolution process of quantum position is as follows：
Wherein ζ=0.15/J is mutation probability, and abs () is to seek ABS function.
The position of quantum crow is obtained to quantum crow quantum position measurement.Measurement rules are as follows：
Wherein 1≤i≤K, 1≤j≤J,It is to meet equally distributed random number.
Step 5：Fitness calculating is carried out to every quantum crow according to fitness function, determines every quantum crow
Hiding food position, while finding optimal food position so far.
By the position of i-th quantum crowIt is assigned to task allocation matrix A.According toCarry out fitness calculating.
The hiding food position of quantum crow is chosen using greedy selection strategy, ifThenOtherwise
Step 6：If reaching greatest iteration algebraically, algorithm terminates, and executes step 7；Otherwise, t=t+1 is enabled, step is returned
Rapid four continue.
Step 7：Global optimum's food position is exported, task allocation matrix is mapped as.
Specific embodiment is as follows：
The setting of its model parameter is as follows：
The model number U=4 of unmanned plane, unmanned plane play points M=3, the coordinate of starting point be (368,319,150), (264,
44,264) coordinate of and (296,242,347.5), the number of tasks Q=10 of unmanned plane, the 1st task are (264,715,800),
Task value is 5, and degree of injuring threshold value is all 0.5；The coordinate of 2nd task is (225,605,670), and task value is 5, is injured
It is 0.5 to spend threshold value all；The coordinate of 3rd task is (168,538,340), and task value is 2, and degree of injuring threshold value is all 0.5；The
The coordinate of 4 tasks is (180,455,670), and task value is 1, and degree of injuring threshold value is all 0.5；The coordinate of 5th task is
(120,400,600), task value are 2, and degree of injuring threshold value is all 0.5；The coordinate of 6th task is (96,304,233), is appointed
Business value is 5, and degree of injuring threshold value is all 0.5；The coordinate of 7th task is (10,451,233), and task value is 5, degree of injuring
Threshold value is all 0.5；The coordinate of 8th task is (162,660,233), and task value is 5, and degree of injuring threshold value is all the 0.5, the 9th
The coordinate of a task is (110,561,45), and task value is 5, and degree of injuring threshold value is all 0.5；The coordinate of 10th task is
(105,473,1830), task value are 5, and degree of injuring threshold value is all 0.5.Unmanned plane weapon type D=2, wherein the 1st kind of model
The 2nd kind of weapon is used with the unmanned plane of the 2nd kind of model, the unmanned plane of the 3rd kind of model and the 4th kind of model uses the 1st kind of weapon, the
The cost of a kind of weapon is 5 units, and the cost of the 2nd kind of weapon is 3 units, and the unmanned plane of the 1st kind of model selects the 1st kind of force
The probability of device is 0.67, and it is 0.78 to select the probability of the 2nd kind of weapon；The unmanned plane of 2nd kind of model selects the probability of the 1st kind of weapon
It is 0.67, it is 0.78 to select the probability of the 2nd kind of weapon；It is 0.92 that the unmanned plane of 3rd kind of model, which selects the probability of the 1st kind of weapon,
It is 0.92 to select the probability of the 2nd kind of weapon；It is 0.92 that the unmanned plane of 4th kind of model, which selects the probability of the 1st kind of weapon, selects the 2nd
The probability of kind weapon is 0.92.The killing rate of the 1st task of 1st kind of weapon pair and the 2nd task is 0.92；1st kind of weapon pair
The killing rate of 3rd task, the 4th task and the 5th task is 0.8；The 6th task of 1st kind of weapon pair, the 7th task and
The killing rate of 8th task is 0.94；The killing rate of the 9th task of 1st kind of weapon pair and the 10th task is 0.6.2nd kind of force
The 1st task of device pair, the 2nd task, the 3rd task, the killing rate of the 4th task and the 5th task are all 0.8；2nd kind of force
The killing rate of the 6th task of device pair, the 7th task and the 8th task is 0.92；The killing rate of the 9th task of 2nd kind of weapon pair
It is 0.97；The killing rate of the 10th task of 2nd kind of weapon pair is 0.6.The quantity of 1st type unmanned plane is 5, ultimate run 300,
The quantity of 2nd type unmanned plane is 6, ultimate run 900.The quantity of 3rd type unmanned plane is 6, ultimate run 900.4th type without
Man-machine quantity is 15, ultimate run 1700.The quantity of 5th type unmanned plane is 3, ultimate run 300.6th type unmanned plane
Quantity be 5, ultimate run 900.The quantity of 7th type unmanned plane is 6, ultimate run 900.The quantity of 8th type unmanned plane
It is 4, ultimate run 1700.The quantity of 9th type unmanned plane is 5, ultimate run 300.The quantity of 10th type unmanned plane is 10,
Ultimate run is 900.The quantity of type 11 unmanned plane is 5, ultimate run 900.The quantity of 12nd type unmanned plane is 10, maximum
Voyage is 1700.Unmanned plane maximum formation number to task attack is all 8.Weight λ1=1, λ2=0, object function weights omega1
=0.322, ω2=0.214, ω3=0.1856, ω4=0.2784.Penalty coefficient c1=c2=c3=c6=50, c4=c5=0.
The unit of above-mentioned coordinate, voyage is all km.
The parameter setting of unmanned aerial vehicle group method for allocating tasks based on quantum crow group hunting mechanism is as follows：Population scale K
=20, maximum iteration 200, perception probability μ=0.1, the influence degree e to develop to the quantum crow1=0.06, e2=
0.03, e3=0.01, Flight Length H=2.
The parameter setting of unmanned aerial vehicle group method for allocating tasks based on particle cluster algorithm is shown in that Li Wei etc. exists《Control and decision》
" the multiple no-manned plane method for allocating tasks based on particle cluster algorithm " delivered on (2010, Vol.25No.9, pp.1359-1364),
Other parameters are identical as the unmanned aerial vehicle group method for allocating tasks based on quantum crow group hunting mechanism.
As shown in figure 3, under the conditions of above-mentioned parameter is arranged, realize that the convergence of multiple no-manned plane task distribution is bent for two methods
Line, the present invention have convergence effect faster.
It is as shown in the table for unmanned aerial vehicle group method for allocating tasks result based on quantum crow group hunting mechanism：
The unmanned plane of 1 each starting point of table corresponds to the model distribution of task
Wherein M1 indicates that first starting point, M2 indicate that first starting point, M3 indicate third starting point.Q1 to Q10 distinguishes table
Show the 1 to 10th task.U1 indicates that 1 model unmanned plane, U2 indicate that 2 model unmanned planes, U3 indicate that 3 model unmanned planes, U4 indicate 4
Model unmanned plane, 0 indicates that no unmanned plane executes the task from this starting point.
The present invention solves that traditional algorithm search speed is slow and computationally intensive, it is difficult to find the OPTIMAL TASK point of unmanned aerial vehicle group
Match, and the existing unmanned aerial vehicle group task distribution design based on intelligence computation seldom considers various evaluation index peace treaties
Beam, application range are limited.A kind of consideration unmanned aerial vehicle group Task Assignment Model is proposed, while proposing a kind of discrete quantum crow
Crow group hunting mechanism is used to solve the Task Allocation Problem of unmanned aerial vehicle group.Need steps of the method are：The first step is established from more
A starting point is to the unmanned aerial vehicle group Task Assignment Model of multiple tasks, including unmanned plane model number, starting and terminal point and distribution model.The
Two steps, initialization quantum crow group.Third walks, and carries out fitness calculating to every quantum crow according to fitness function, calculates
The position of the corresponding quantum crow of fitness function minimum value gone out saves as global optimum's food position.4th step updates every
The quantum position of quantum crow and position.5th step carries out fitness calculating, really according to fitness function to every quantum crow
The hiding food position of fixed every quantum crow, while optimal food position so far is found, if reaching greatest iteration
Algebraically then exports global optimum's food position, is mapped as task allocation matrix.
Claims (2)
1. a kind of unmanned aerial vehicle group method for allocating tasks based on quantum crow group hunting mechanism, which is characterized in that including following step
Suddenly：
Step 1：Initialization greatest iteration algebraically is Tmax, establish the unmanned aerial vehicle group task distribution from multiple starting points to multiple tasks
Model：Assuming that there is the unmanned plane of U kind models to execute Q task from M starting point；
If the coordinate of m-th of starting point of unmanned plane isWherein 1≤m≤M, q-th of task of unmanned plane
Coordinate isWherein 1≤q≤Q is divided into L type to all unmanned planes according to starting point and model, wherein
L=U × M, i.e. same type unmanned plane starting point having the same and belong to same model；
According to the starting point of the model of l type unmanned planes, the starting point coordinate for obtaining l type unmanned planes is
Wherein l=1,2 ..., L, then the starting point of l types unmanned plane is D at a distance from q-th of taskl,qAnd meet：
The task allocation matrix of unmanned aerial vehicle group L row Q row allocation matrix A={ al,q|al,q∈{0,1}}L×QIt indicates, if first of type
Number unmanned plane execute q-th of task, then al,q=1, otherwise al,q=0；
If each unmanned plane has the D kind weapons, l type unmanned planes to be using the probability of d kind weaponsL type unmanned planes make
Cost with d kind weapons is δl,d, wherein 1≤d≤D, 1≤l≤L, the killing rate of d kinds q-th of task of weapon pair areWherein 1≤d≤D, 1≤q≤Q, if unmanned plane injures probability matrix P={ Pl,q,d|Pl,q,d∈[0,1]}L×Q×D,
Pl,q,dIt is degree of injuring and satisfaction of the l types unmanned plane using d kind q-th of task of weapon pair：
Degree of the injuring threshold value of q-th of task is Wq, wherein 1≤q≤Q, if the value of q-th of task is Vq, possess l type unmanned planes
Quantity is Bl, the unmanned plane maximum formation number to the attack of q-th of task is Cq, the ultimate run of l type unmanned planes is Rl, entirely
The ultimate run of portion's unmanned plane is Omax, the flying speed of l type unmanned planes is Zl, the maximum flight time of whole unmanned planes is
Zmax；
Unmanned plane Task Assignment Model is respectively by target value revenue function, flying distance function, consumption bullet amount cost function and mesh
Mark coverage rate function representation：
(1) normalization target value revenue function is：Wherein A is task
Allocation matrix, A={ al,q|al,q∈{0,1}}L×Q, Pl,q,dQ-th of task is injured using d kinds weapon for l types unmanned plane
Probability, d are set in advance for the type needs of the weapon used using the unmanned plane of the type of weapon, l types, VqIt is q-th
The value of business, N are the unmanned plane number of actual participation task in task distribution,
It is worth for maximum task, max is to seek max function；
(2) normalization flying distance function is：Its
Middle λ1, λ2For the weight of two factors, λ1+λ2=1, λ1,λ2>=0,For the length of longest path,
RlFor the ultimate run of l type unmanned planes；
(3) normalization consumption bullet cost function is：What the unmanned plane of wherein l types used
The type needs of weapon are set in advance, δmaxFor maximum cost,
(4) normalization target coverage degree function is：
The unmanned aerial vehicle group Task Assignment Model meets following constraints：
(1) task troops constrain：The unmanned plane of each type sets out number and no more than possesses the type unmanned plane number,
(2) unmanned plane combat radius constrains：Ensure the flying distance of unmanned plane within its combat radius, al,q×(Dl,q-Rl)≤
0 (l=1,2 ..., L；Q=1,2 ..., Q)；
(3) to the constraint of target Damage degree：Degree of injuring injuring not less than the task of the unmanned plane of execution task q to task q
Threshold value is spent,Pl,q,dFor l types without
The man-machine probability of injuring using d kinds q-th of task of weapon pair, d are the type using weapon, the force that the unmanned planes of l types uses
The type needs of device are set in advance；
(4) constraint of the unmanned plane number of target of attack：It is no more than its maximum to the unmanned plane number of q-th of task attack to form into columns
Number, i.e.,
(5) constraint of the voyage of target of attack：I.e. the voyage of strike mission is no more than given ultimate run,OmaxFor the ultimate run of all unmanned planes；
(6) constraint of the time of target of attack：The time of strike mission is no more than given maximum time,ZmaxFor the maximum flight time of all unmanned planes；
Population scale K, the dimension J=L × Q of optimization problem for determining quantum crow group, are incremented by, mode incremental q is arranged according to l
Unmanned aerial vehicle group task allocation matrix A={ al,q|al,q∈{0,1}}L×QIn element, useIt is right
The element in unmanned aerial vehicle group task allocation matrix A should be recorded；
Step 2：Initialize quantum crow group：
By the quantum position of i-th quantum crowBe set as per one-dimensionalIts
In 1≤i≤K, 1≤j≤J, and the quantum position of i-th quantum crow is measured, obtains the position of i-th quantum crowThe hiding food position of i-th quantum crow of initialization isWherein 1≤i≤K, t are iterations, set t=0 at the beginning；
To the jth dimension of the quantum position of i-th quantum crowIt measures, obtains the jth of the position of i-th quantum crow
DimensionWherein 1≤i≤K, 1≤j≤J,Be meet it is equally distributed with
Machine number；
Step 3：Fitness calculating is carried out to every quantum crow according to fitness function, calculated fitness function is minimum
The position for being worth corresponding quantum crow saves as global optimum's food position
Step 4：Update the quantum position and position of every quantum crow:
I-th quantum crow randomly selects another quantum crow s in quantum crow group, is sent out followed by quantum crow s
The food position now hidden by quantum crow s, quantum crow s have found that the perception probability being followed is μ, ifAmount
Sub- crow i carries out the update of quantum position by strategy 1, and otherwise quantum crow i carries out the update of quantum position by strategy 2；
Strategy 1 meets：Food position that i-th quantum crow is hidden according to quantum crow s carries out the update of position, i-th
The renewal equation at quantum rotation angle of the jth dimension of quantum crow isWherein e1It is normal
Number determines the influence degree for guiding the position of the quantum crow to develop the quantum crow, and H is Flight Length；
Strategy 2 meets：I-th quantum crow carries out position more according to itself hiding food position and optimal food position
Newly, the renewal equation at the quantum rotation angle of the jth dimension of i-th quantum crow isWherein e2,e3For constant, determines and guide the quantum crow
The influence degree that develops to the quantum crow of position；
The evolution process of quantum position is as follows：
Wherein ζ=0.15/J is mutation probability, and abs () is to seek ABS function；
The position of quantum crow is obtained to quantum crow quantum position measurement, measurement rules are as follows：
Wherein 1≤i≤K, 1≤j≤J,It is to meet equally distributed random number；
Step 5：Fitness calculating is carried out to every quantum crow according to fitness function, determines hiding for every quantum crow
Food position, while finding to the optimal food position of current iteration algebraically；
By the position of i-th quantum crowIt is assigned to task allocation matrix A, according toCarry out fitness calculating；
The hiding food position of quantum crow is chosen using greedy selection strategy, ifThenOtherwise
Step 6：If reaching greatest iteration algebraically Tmax, algorithm termination, execution step 7；Otherwise, t=t+1, return to step are enabled
Four continue；
Step 7：Global optimum's food position is exported, task allocation matrix is mapped as.
2. a kind of unmanned aerial vehicle group method for allocating tasks based on quantum crow group hunting mechanism according to claim 1,
It is characterized in that：The process of fitness evaluation described in step 3 is as follows：
First by the position of t i-th quantum crow of generationIt is assigned to task allocation matrix A, wherein t i-th quantum crow of generation
The jth of crow is tieed upIt is assigned to al,q, according to
Fitness calculating is carried out, wherein
c1,c2,c3,c4,c5,c6For penalty coefficient, ω1,ω2,ω3,ω4For weighted factor, ω1+ω2+ω3+ω4=1,0≤
ω1,ω2,ω3,ω4≤ 1, max are to seek max function, and min is to seek minimum value function.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101136081A (en) * | 2007-09-13 | 2008-03-05 | 北京航空航天大学 | Unmanned operational aircraft multiple plains synergic tasks distributing method based on ant colony intelligent |
| CN104102791A (en) * | 2014-08-01 | 2014-10-15 | 哈尔滨工程大学 | Antenna array spare construction method based on quantum glowworm search mechanism |
| CN105225003A (en) * | 2015-09-23 | 2016-01-06 | 西北工业大学 | A kind of cuckoo searching algorithm solves the method for UAV multitask investigation decision problem |
| US20160304198A1 (en) * | 2014-12-03 | 2016-10-20 | Google Inc. | Systems and methods for reliable relative navigation and autonomous following between unmanned aerial vehicle and a target object |
| CN107045458A (en) * | 2017-03-09 | 2017-08-15 | 西北工业大学 | Unmanned plane cotasking distribution method based on multi-target quantum particle cluster algorithm |
| CN107622327A (en) * | 2017-09-15 | 2018-01-23 | 哈尔滨工程大学 | Multiple no-manned plane path planning method based on cultural ant colony search mechanisms |
-
2018
- 2018-03-19 CN CN201810224721.9A patent/CN108549402B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101136081A (en) * | 2007-09-13 | 2008-03-05 | 北京航空航天大学 | Unmanned operational aircraft multiple plains synergic tasks distributing method based on ant colony intelligent |
| CN104102791A (en) * | 2014-08-01 | 2014-10-15 | 哈尔滨工程大学 | Antenna array spare construction method based on quantum glowworm search mechanism |
| US20160304198A1 (en) * | 2014-12-03 | 2016-10-20 | Google Inc. | Systems and methods for reliable relative navigation and autonomous following between unmanned aerial vehicle and a target object |
| CN105225003A (en) * | 2015-09-23 | 2016-01-06 | 西北工业大学 | A kind of cuckoo searching algorithm solves the method for UAV multitask investigation decision problem |
| CN107045458A (en) * | 2017-03-09 | 2017-08-15 | 西北工业大学 | Unmanned plane cotasking distribution method based on multi-target quantum particle cluster algorithm |
| CN107622327A (en) * | 2017-09-15 | 2018-01-23 | 哈尔滨工程大学 | Multiple no-manned plane path planning method based on cultural ant colony search mechanisms |
Non-Patent Citations (2)
| Title |
|---|
| ASRI BEKTI PRATIWI: "A Hybrid Cat Swarm Optimization - Crow Search Algorithm for Vehicle Routing Problem with Time Windows", 《IEEE》 * |
| 王记丰 等: "基于量子粒子群优化算法的多机协同目标分配问题研究", 《船舶电子工程》 * |
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