CN106020230A - Task distribution method for multiple unmanned planes within constraint of energy consumption - Google Patents

Abstract

The invention relates to a task distribution method for multiple unmanned planes within constraint of energy consumption. unmanned plane formation is composed of two task planes and one service plane, a host computer uses an improved genetic algorithm to distribute task points to the task planes, a triple crossover operator is introduced so that an obtained route of the unmanned plane formation is minimal and routes of the task planes are balanced as possible; constraint of energy consumption of the task planes is taken into consideration, and to make energy consumed by the task planes to be fewer as possible, a route gathering point is determined on the basis of ideal routes of the task planes, the service plane provides energy supply at the gathering point, a complete route of the unmanned planes is obtained finally, and distribution of all task points is completed. Compared with traditional task distribution of the unmanned planes, the method of the invention improves the task completing efficiency of the unmanned planes, so that the routes through which the task planes visit a task point are balanced as possible; and introduction of the energy supply point has great significance in guaranteeing smooth task distribution of the unmanned planes.

Description

A kind of multiple no-manned plane method for allocating tasks under power consumption constraint

Technical field

Patent of the present invention relates to multiple no-manned plane task distribution field, for solving appointing of aerial many unmanned vehicles limited energy Business assignment problem.

Background technology

Along with the further investigation to robotics, and being obviously improved of system capability, the application of robot system obtains Develop rapidly.At present, robot can apply to widely field, and robot replaces the mankind so that the mankind are from numerous Freeing in weight, dangerous work, particularly some danger are higher or the unapproachable special occasions of the mankind, example As nuclear leakage, aerial unmanned plane take photo by plane monitoring, Mars explore, the application demand such as fire rescue and military anti-terrorism.For Complicated loaded down with trivial details job task, the factors such as it is longer that individual machine people completes the time spent by task, and efficiency is low cause single machine Device people cannot meet the demand of some task, and therefore, multiple-mobile-robot system can be applied to workers and peasants by the mankind The fields such as industry produces, military and national defense and scientific research realize reducing human labour intensity, improves work efficiency, avoid people The purposes such as member's injures and deaths and extension mankind's activity space.

Modern war uses robot replace soldier to perform the task of danger, ground forces and non-can be reduced to greatest extent The injures and deaths of belligerent personnel, due to the decomposability of these tasks, robot cooperated complete different sons with multiple concurrently and appoint Business obviously can than single robot much faster.For multi-robot system, task distribution is effectively to utilize multiple mobile robot System resource, to give full play to the important foundation of system effectiveness advantage, its importance degree is with the function difference of DBMS member The increase of the structural complexity of property and task and increase.The heredity calculation that professor Holland of Michigan university of the U.S. proposes Method (Genetic Algorithm, GA) is to solve for the effective ways of the combinatorial optimization problem of complexity.Distribute at multi-robotic task In system, the model of Task Allocation Problem can be obtained by multiple traveling salesmen problem analysis.The mathematics of traveling salesman problem the earliest Planning is to be proposed by Dantzig (1959) et al., multiple traveling salesmen problem (Multiple Traveling Salesman Problem, Rear abbreviation MTSP) refer to that M travelling salesman, from same city or different cities, walks an itinerary respectively, In addition to city of setting out so that each city has and only travelling salesman's process, it is eventually returned to city of setting out, and total distance The shortest.

In task assignment procedure, each robot is equivalent to each travelling salesman, and each city that travelling salesman accesses is equivalent to machine People access each task point, difference is that the process that task is distributed is class travelling salesman's model, be travelling salesman without Return the close copy of starting point.

Owing to above traditional method does not consider the problem of each task engine air route equilibrium in task assignment procedure, and task The problem of machine power consumption constraint, and for unmanned aerial vehicle (UAV) control field, task engine power consumption constraint is again a key that must solve Problem.

Summary of the invention

The technical problem to be solved in the present invention is, for above-mentioned technological deficiency, it is provided that the multiple no-manned plane under a kind of power consumption constraint The method of task distribution, it is possible to the front task of multiple no-manned plane is carried out reasonable distribution and carries out air route optimization；Meanwhile, solve The certainly supply problem of multiple no-manned plane energy consumption；Adapt it to following other air taskings such as air fighting battlefield and air transport divide Join problem.

For solving above-mentioned technical problem, the present invention adopts the following technical scheme that

Multiple no-manned plane method for allocating tasks under a kind of power consumption constraint, it is characterised in that in the formation of given unmanned plane, each task Point and under the consumption constraint of task function, minimizes air route for Target Assignment task, for making to appoint with balance optimizing air route simultaneously The energy that business machine expends is the fewest, determines the air route point of task engine on the basis of the preferable air route of task engine, and by taking Business machine provides energy recharge at point, finally gives the complete air route of unmanned plane, completes the distribution of all tasks point；Tool Body comprises the steps:

Step 1: task distribution system initializes；Mainly include that host computer initializes, ground control cabinet initializes, clock Initialize, unmanned plane is formed into columns and is initialized；Described unmanned plane is formed into columns at least three frame unmanned planes, respectively task engine A, appoints Business machine B, provides the server C of the energy for task engine；Set front and have multiple task point, and task is counted out more than appointing Business machine number, unmanned plane is formed into columns before receiving out and saying the word, is positioned at starting point O and awaits orders；

Step 2: set up the communication between ground control cabinet, position location satellite and unmanned plane formation: when performing front task, Each task point, the positional information of unmanned plane is obtained in real time by satellite positioning tech；

Step 3: the positional information of each to unmanned plane and front task point is returned to ground control cabinet, ground by position location satellite in real time Face control station real-time reception, analyze and process these positional informationes；

Step 4: host computer passes through Revised genetic algorithum, introduces three exchange crossover operators and each task point is distributed to each Business machine, obtains preferable air route l₀, i.e. task engine is not by the air route of power consumption constraint；Setting up task point distribution model is class MTSP Model, is travelling salesman without the class MTSP model returned；

Step 5: obtain the position of energy recharge point P: host computer combines constraints simulation task engine in preliminary ideal Air route l₀Upper flight, obtains first energy recharge point P₀Position；

Step 6: set unmanned plane formation and arrive first energy recharge point P₀After, task distribution system is to remaining task Point makees task distribution again, seeks next energy recharge point, the most always circular flow step 2, step 3, step simultaneously Rapid 4, step 5, until completing the distribution of all tasks point；

Step 7: after all task points are assigned, control station sends control command, and unmanned plane is formed into columns and received order, From O point, each task point is gone to perform the task of distribution along the planning air route specified.

In technique scheme, task engine A and task engine B is isomorphism unmanned plane, and server C and task engine are isomery Unmanned plane, and server C energy consumption limit be enough to ensure that all tasks, task engine A and task engine B limited energy The energy need to be carried out in flight course supplement.

In technique scheme, described energy recharge point P is the point of task engine depleted of energy, be also server C with Task engine A, the point of task engine B；And disregard the time converged spent by the moment, the flight route of server C For going to each energy recharge point successively from starting point；In each iteration cycle, judge that each task engine is the need of carrying out energy Source feeds, and energy recharge point P is positioned at the line midpoint of a certain moment task engine A and B, when a team forms into columns Danone During the supply point P of source, task engine A, the energy of task engine B have exhausted, the flight path of server C for by The straight line path of some O to energy recharge point P.

In technique scheme, when the air route length of individual task machine is i.e. up to air route ultimate value, energy is gone in this task engine Source supply point.

In technique scheme, after setting task engine is formed into columns and accessed all tasks point in step 4, rest on last Task point, it is not necessary to return starting point, obtain task engine preliminary not by the air route l of power consumption constraint₀, namely preferable air route l₀。

The distribution of each task point to each task engine, is introduced three exchange crossover operators and is made by Revised genetic algorithum of the present invention Task engine air route the shortest, each, the total air route of task engine substantially equalizes.Task Allocation Problem can be described as: front has multiple Business point, and task counts out more than task engine number, an aerial unmanned plane of team is formed into columns and is just awaited orders in starting point, host computer etc. Treat task point and the positional information of unmanned plane passed back by satellite positioning tech, when the distribution side of battlefield, front each task point After formula determines, ground control cabinet sending control command, task engine is received new order and is gone to investigate some points determining position, Arrive the location point specified to go to perform a certain task.

In the present invention, by triangular right-angle limit less than hypotenuse principle, point P is set in the line of task engine A, B At Dian, can guarantee that the efficient utilization rate of the energy, make task engine in the preferable air route of deviation and the process of arrival energy recharge point P In, the energy minimization consumed, finally determine the position of energy recharge point P；Disregard spent by energy recharge point Time, then, after primary energy has fed, unmanned plane is formed into columns and is restarted to be allocated remaining task point, simultaneously Seek next energy recharge point, until completing the distribution of all tasks point.

During the unmanned plane task of the present invention is formed into columns, task engine A and task engine B is isomorphism, server C and task engine For isomery unmanned plane, isomery unmanned plane has different hardware configurations and body structure, performs for dissimilar task point Investigate, access, the task such as strike；As the unmanned plane that server is high-performance, high cost of energy recharge, flight speed Degree can (flight Mach number of unmanned plane be flight speed and the ratio of velocity of sound in its flying height of unmanned plane more than 3 Mach Value, such as mach one or 1M are one times of velocity of sound), meanwhile, the engine thrust-weight ratio of the unmanned plane in this method sets Be 9 (thrust-weight ratio of unmanned plane refers to motor power and engine weight or the ratio of body weight, it represent electromotor or Thrust produced by body Unit Weight, the thrust-weight ratio of current world advanced person's electromotor typically between 7.5-9.0, the U.S. This numerical value of F-119 electromotor can reach 11.0), it follows that the cost of server is the highest, therefore more than Formation mode can complete combat duty with less operation cost-effective.Task engine A and task engine B is flying to respectively During task point, its energy consumption is restricted, and server C is High Performance Unmanned Aerial Vehicle, therefore suffered energy consumption restriction More weak, during execution task, its energy has been enough to ensure that task.

In sum, the present invention relates to a kind of method solving the distribution of multiple no-manned plane limited energy task.Described unmanned plane is compiled Team is made up of two frame task engines and a frame server, it is adaptable to following air fighting battlefield and other Task Allocation Problems. By technology such as satellite fixes, obtain unmanned plane and the positional information of task point in real time；Ground control cabinet receives position location satellite The positional information passed back；Host computer uses Revised genetic algorithum that each task point is distributed to each task engine, at traditional something lost Propagation algorithm introduces three exchange crossover operators so as to get unmanned plane formation air route minimum, and the air route of each task engine is to the greatest extent Amount equilibrium；Simultaneously, it is considered to the power consumption constraint problem of task engine, the energy for making task engine expend is the fewest, in task engine Preferable air route on the basis of determine the air route point of task engine, and provided energy recharge by server at point.Finally Obtain the complete air route of unmanned plane, complete the distribution of all tasks point.Compared to traditional multiple no-manned plane Task Allocation Problem, The present invention proposes the unmanned plane formation mode of a kind of low cost, simple in construction；Secondly, the present invention is in traditional heredity calculation In method introduce three exchange crossover operators so as to get unmanned plane formation air route the least, improve multiple no-manned plane system and complete The efficiency of task, limits each task engine and accesses the deadline of task point, makes multiple task engine access the road boat of task point Road equalizes as far as possible, takes full advantage of multiple no-manned plane system；Again, the present invention for one group of energy will exhaust unmanned Machine is formed into columns, and introduces the method to set up of energy recharge point, smoothly completes for guarantee unmanned plane task distribution and has very Great meaning.

Accompanying drawing explanation

Fig. 1 is the multiple no-manned plane method for allocating tasks communication scheme under power consumption constraint of the present invention；

Fig. 2 is the multiple no-manned plane task allocation flow figure under power consumption constraint of the present invention；

Fig. 3 is task engine ideal air route of the present invention schematic diagram；

Fig. 4 is the multiple no-manned plane task distribution air route schematic diagram under power consumption constraint of the present invention.

It is embodied as measure

In order to further illustrate technical scheme, the present invention will be described in detail for comparison accompanying drawing.

Fig. 1 is the communication scheme between ground control cabinet of the present invention, position location satellite and unmanned plane are formed into columns.

Obtaining each unmanned plane, the positional information of task point by position location satellite, position location satellite is by each task point, unmanned seat in the plane Confidence breath is returned to ground control cabinet, and ground control cabinet receives, analyzes and process these positional informationes, by the something lost improved Propagation algorithm obtains the task distribution path of task engine, thus obtains the planning air route of task distribution.

Fig. 2 is the flow chart of the multiple no-manned plane method for allocating tasks under power consumption constraint of the present invention, mainly comprises the steps that

Step 1: task distribution system initializes, mainly includes that host computer initializes, ground control cabinet initializes, clock Initialize, unmanned plane is formed into columns and is initialized；

Step 1-1: unmanned plane is set and forms into columns: A, B, C.Wherein A, B are task engine, and C is server.Unmanned plane Form into columns before receiving out and saying the word, be positioned at starting point O；

Step 1-2: unmanned plane initializes and specifically includes that gyroscope initializes, accelerometer initializes, gaussmeter is initial Change, barometer initializes, GPS initializes, photographic head initializes, single-chip microcomputer initializes；

Step 2: set up the communication between ground control cabinet, position location satellite and unmanned plane formation；

Step 3: ground control cabinet, position location satellite and unmanned plane carry out information transmission between forming into columns.As shown in Figure 1 Communication scheme between ground control cabinet, position location satellite and unmanned plane formation；

Step 3-1: when performing the air mission in battlefield, front, obtain each task in real time by technology such as satellite fixes Point, the positional information of unmanned plane；

Step 3-2: each task point, unmanned plane positional information are returned to ground control cabinet by position location satellite；

Step 3-3: ground control cabinet receives, analyzes and process the positional information passed back；

Step 4: each task point is distributed to two frame task engines by Revised genetic algorithum by host computer, obtains task mechanism Think air route l₀。

Step 4-1: the air route of task engine ideal as shown in Figure 3 schematic diagram.Set up task point distribution model, its mathematical model As follows:

Wherein, x_ijk、y_kiFor 0-1 decision variable: work as x_ijkWhen=1, represent task engine k by arc (i, j), otherwise x_ijk=0； Work as y_kiWhen=1, represent that task engine k accesses task point i, otherwise y_ki=0；Starting point O that unmanned plane is formed into columns is represented with 0, It is referred to as initial point；Point 1 ..., n represents the task point that 2 frame task engines need to access；c_ijFor task engine through corresponding segmental arc (i, j) The distance traveled through；t_kThe time consumed after having flown for kth task engine；v_kFor the speed of kth task engine, Wherein:

v₁=v_A=1M；

v₂=v_B=1M；

v₃=v_C=3M；

Wherein, 1M or mach one are 1 times of velocity of sound；

Object function is:

T=min (max (t₁,t₂)) (1)

Wherein:

$t_k=\underset{i=0}{\overset{n}{\Σ}}\underset{j=0}{\overset{n}{\Σ}}{c}_{ij}{x}_{ijk}/v_k;k=1,2---\left(2\right)$

Constraints is:

$\underset{k=1}{\overset{2}{\Σ}}{y}_{ki}=\left\{\begin{array}{cc}2& i=0\\ 1& i=1,2,...,n\end{array}\right.---\left(3\right)$

$\underset{i=0}{\overset{n}{\Σ}}{x}_{ijk}=y_{kj};j=0,1,...,i;k=0,1,2---\left(4\right)$

$\underset{j=0}{\overset{n}{\Σ}}{x}_{ijk}=y_{ki};i=0,1,...,j;k=0,1,2---\left(5\right)$

X=(x_ijk)∈S (6)

In the model, formula (1) represents in two frame task engines, and the task engine used time making the access task point time maximum minimizes； Formula (2) represents the time that each task engine consumes after having accessed all tasks point；Formula (3) represents from starting point 0 (namely Point O) set out, all task points are only strictly accessed once by some task engine；Formula (4) represents that the terminal of any bar arc is appointed Business point only has a starting point task point and is attached thereto；Formula (5) represents that the starting point task point of any bar arc only has a terminal task Point is attached thereto；Formula (6) represents the solution eliminating the imperfect circuit of composition, and S is that branch road eliminates constraint, i.e. eliminates and cannot constitute completely The solution of whole route.

Step 4-2: the optimal conditions utilizing Revised genetic algorithum that each task is pressed illuminated (1) distribute to task engine A and B: use three exchanges to intersect, chromosome coding and matrix coding/decoding method, population is repeatedly selected, intersect, The genetic manipulations such as variation, constantly produce the population of new generation more adapting to environment than parent.

Step 4-2-1: task is carried out genetic coding.Task engine A, B carry out task distribution, total N+1 task Point, represents the starting point of all unmanned planes with point 0, puts 1 ... N represents the task point that 2 frame task engine needs access.Attached Add 1 virtual sign, represent 1 virtual task point.They have identical coordinate with starting point 0, go out the most each time Existing virtual sign means that task engine accesses starting point 0, but in the case of having accessed for task, virtual symbol occurs Number time do not return starting point 0.Each route represents the access air route of some task engine.Insignificant for avoiding the occurrence of Subpath, it will be assumed that the distance from task point 0 to task point 0 is infinitely great.Such as, work as N=6, i.e. have 6 to appoint During business point, chromosome code is 0～7, and wherein " 7 " are the virtual sign added, the Task Allocation Problem of 2 frame task engines, Its item chromosome coding is:

1

2

5

7

4

3

6

Then the air route of this task engine A, B is expressed as:

0-1-2-5 0-4-3-6

During algorithm operating, it is possible that virtual sign is in the situation at the two ends of item chromosome:

1

2

5

6

4

3

7

The most now the air route of task engine A, B is expressed as:

1-2-5-6-4-3 0-0

Distance from task point 0 to task point 0 is infinitely great, so the distance maximum in task engine at present is infinitely great. Minimize due to Prescribed Properties air route maximum again, so such chromosome can be eliminated.

Step 4-2-2: population size selects.Suitably population size is significant to the convergence of genetic algorithm.Colony The least being difficult to tries to achieve satisfied result, and colony then calculates complexity too greatly.Rule of thumb, population size typically takes 10～200.

Step 4-2-3: fitness function.Owing to optimization aim is for minimizing the deadline, object function is made to obtain as exponential transform To fitness function:

F=a exp (-b × T) (7)

Wherein: a, b are arithmetic number.

Step 4-2-4: select.The method using roulette selection, each individual selected probability is equal to the suitable of it Should be worth with individual fitness in whole population and ratio.If certain individual i, it is just when for f_i, then its selected probability It is expressed as:

$P_i=f_i/\underset{k=1}{\overset{M}{\Σ}}{f}_k---\left(8\right)$

Step 4-2-5: intersect and operate.Introducing three exchange crossover operators makes the maximum of each task engine air route length minimize, Thus realize equalizing the air route length of each task engine.Three exchange crossover operators use 3 parent chromosome hybridization to occur 1 Filial generation, this method increases the quantity of parent chromosome, retains and is exaggerated filial generation and inherits the characteristic of parent excellent genes, Accelerate the search speed of algorithm.In case of 6 task points, describe the friendship of three exchange crossover operators in detail Fork process:

The non symmetrical distance of 6 task points is as follows, distributes the method for the task that task point distance is symmetrical and still fits With.

Distance between 6 task points

	0	1	2	3	4	5	6
								0	∞	1	7	6	4	2	5
1	2	0	6	5	5	4	4
								2	1	7	0	3	1	2	3
3	4	3	5	0	5	6	7
								4	4	1	5	3	0	2	7
5	5	3	4	2	1	0	7
								6	3	2	7	6	5	4	0

Randomly choose three individualities and carry out three exchange intersections:

α=1 257436

β=2 734165

γ=3 645721

The distance in α air route and be 23, the maximum distance in individual task machine is 14；

The distance in β air route and be 27, the maximum distance in individual task machine is 20；

The distance in γ air route and be 34, the maximum distance in individual task machine is 20.Randomly choosing starting point is 1, makes 1 Become first position of three male parents.

α=1 257436

β=1 652734

γ=1 364572

L=1 * * * * * *

Because d (1,2)=6, d (1,6)=4, d (1,3)=5, so d (1,2) > d (1,3) > d (1,6), so

α=1 625743

β=1 652734

γ=1 645723

L=1 6 * * * * *

By parity of reasoning, and the offspring finally obtained is:

L=1 652743

The distance of L and be 20, the maximum distance in individual task machine is 13, so total distance of L and and individual task The maximum in machine air route is respectively less than α, β and γ.

Step 4-2-6: mutation operation.In the present invention, the chromosome disorder of class MTSP multi-robotic task distribution system uses Gene on exchange mutation, i.e. two random sites of exchange.

Step 4-2-7: matrix decodes.In the operation situation of 6 task points, item chromosome therein is:

α=1 257436

Then the air route of 2 frame task engines is respectively:

0-1-2-5 0-4-3-6

The i.e. air route of task engine A is 0-1-2-5；The air route of task engine B is 0-4-3-6.

Set up the task point Distance matrix D of a 6*6, as follows:

$D=\left[\begin{array}{ccccccc}\mathrm{\∞}& 1& 7& 6& 4& 2& 5\\ 2& 0& 6& 5& 5& 4& 4\\ 1& 7& 0& 3& 1& 2& 3\\ 4& 3& 5& 0& 5& 6& 7\\ 4& 1& 5& 3& 0& 2& 7\\ 5& 3& 4& 2& 1& 0& 7\\ 3& 2& 7& 6& 5& 4& 0\end{array}\right]$

According to there being the access path of each task engine, the reachability matrix X of task engine A, B can be obtained₁、X₂As follows:

$X_1=\left[\begin{array}{ccccccc}0& 1& 0& 0& 0& 0& 0\\ 0& 0& 1& 0& 0& 0& 0\\ 0& 0& 0& 0& 0& 1& 0\\ 0& 0& 0& 0& 0& 0& 0\\ 0& 0& 0& 0& 0& 0& 0\\ 0& 0& 0& 0& 0& 0& 0\\ 0& 0& 0& 0& 0& 0& 0\end{array}\right]$

$X_2=\left[\begin{array}{ccccccc}0& 0& 0& 0& 1& 0& 0\\ 0& 0& 0& 0& 0& 0& 0\\ 0& 0& 0& 0& 0& 0& 0\\ 0& 0& 0& 0& 0& 0& 1\\ 0& 0& 0& 1& 0& 0& 0\\ 0& 0& 0& 0& 0& 0& 0\\ 0& 0& 0& 0& 0& 0& 0\end{array}\right]$

The reachability matrix X{X of each task engine₁,X₂With each task point constitute Distance matrix D carry out dot product respectively, The matrix obtained is the distance matrix of each task engine flight pathFinally give flying of each task engine Walking along the street line length.Such as, the distance matrix that the walked distance of task engine A can be obtained above it is:

$D_1^{*}=D\·X_1=\left[\begin{array}{ccccccc}\mathrm{\∞}& 1& 0& 0& 0& 0& 0\\ 0& 0& 6& 0& 0& 0& 0\\ 0& 0& 0& 0& 0& 2& 0\\ 0& 0& 0& 0& 0& 0& 0\\ 0& 0& 0& 0& 0& 0& 0\\ 0& 0& 0& 0& 0& 0& 0\\ 0& 0& 0& 0& 0& 0& 0\end{array}\right]$

$D_2^{*}=D\·X_2=\left[\begin{array}{ccccccc}0& 0& 0& 0& 4& 0& 0\\ 0& 0& 0& 0& 0& 0& 0\\ 0& 0& 0& 0& 0& 0& 0\\ 0& 0& 0& 0& 0& 0& 7\\ 0& 0& 0& 3& 0& 0& 0\\ 0& 0& 0& 0& 0& 0& 0\\ 0& 0& 0& 0& 0& 0& 0\end{array}\right]$

So, air route length summation S of the task engine A after can distributing in the hope of algorithm_A=1+6+2=9；Task engine B's Air route length summation S_B=4+7+3=14；

Step 4-3: setting task engine is formed into columns after having accessed all tasks point, rests on last task point, it is not necessary to return Return starting point, obtain task engine ideal air route l as shown in Figure 3₀；

Step 5: obtain the position of energy recharge point P, the multiple no-manned plane task under a kind of power consumption constraint as shown in Figure 4 Distribution air route schematic diagram；

Step 5-1: host computer combines the constraints (power consumption constraint of task engine: S_A=S_B≤S₀, and the energy Supply point is positioned at the midpoint constraints of the line midpoint of two frame task engine A, B: S_{A`P</sub>=S<sub>B`P}) simulate task engine at preferable air route l₀Upper flight, obtains first energy recharge point P₀Position, energy recharge point P as shown in Figure 4；

Step 5-2: the point of two task engines A, B and server C is a some P, and disregards and converge spent by the moment Time, the flight route of server C for going to each energy recharge point successively from starting point.Further, for changing each time In generation, system judges the air route length of individual task machine to be the most i.e. up to air route ultimate value, and (single air route maximum is S₀), When the air route of a certain task engine will reach the limit values, energy recharge point is gone in task engine.Wherein, task engine A, B Flight speed be 1M (1M is one times of velocity of sound), the flight speed of server C is 3M；Iteration cycle is 200ms； The flight route length of A, B, C is respectively as follows: S_A、S_B、S_C；Air line distance between A, B is S_AB；A'、B' For the preferable air route l of task engine deviation₀Deviation point；Task engine is once by a length of S of maximum flight route of power consumption constraint₀； S_A`B`For deviation point A' and the air line distance of B'；S_{A`P</sub>For deviation point A' and the air line distance of point P；S<sub>B`P}For deviation Point B' and the air line distance of point P；The power consumption constraint of task engine, and the midpoint of the position of energy recharge point Constraints is:

$\left\{\begin{array}{c}{S}_A=S_B\&le;S_0\\ S_A+\frac{1}{2}{S}_{A`B`}=S_0\\ S_B+\frac{1}{2}{S}_{A`B`}=S_0\\ S_{A^{\&prime;}P}=S_{B^{\&prime;}P}\end{array}\right.$

Task engine formation can be obtained by constraints above condition and perform the position of certain energy recharge point P in task process；

Step 6: unmanned plane is formed into columns and arrived after first energy recharge point, system to remaining task point again as task minute Join, seek next energy recharge point in combination with constraints.The most always circular flow step 2, step 3, step 4, Step 5, until completing the distribution of all tasks point, finally determines the boat that multiple no-manned plane task based on limited energy is distributed Road；

Step 7: after all task points are assigned, control station sends control command, and unmanned plane is formed into columns and received order, From O point, task point is gone to perform task along specifying air route.

To sum up, the present invention devises a kind of optimization and the method for allocating tasks in equilibrium unmanned plane formation air route, obtains unmanned plane Planning air route；Simultaneously, it is contemplated that the energy recharge problem in actual application, while task engine accesses each task point, Energy recharge point solution procedure is set.The method has practical advantage, it is adaptable to other aerial multi-task plannings Occasion；Expand multiple no-manned plane task distribution application, provide good dividing for air transport and the planning of aerial battlefield Method of completing the square.

Claims (5)

1. the multiple no-manned plane method for allocating tasks under a power consumption constraint, it is characterised in that: in the formation of given unmanned plane, each task Point and under the consumption constraint of task function, minimizes air route for Target Assignment task, for making task engine with balance optimizing air route simultaneously The energy expended is the fewest, determines the air route point of task engine, and converged by server on the basis of the preferable air route of task engine Chalaza provides energy recharge, finally gives the complete air route of unmanned plane, completes the distribution of all tasks point；Specifically include following step Rapid:

Step 1: task distribution system initializes；Mainly include host computer initialize, ground control cabinet initialize, clock initialize, Unmanned plane is formed into columns and is initialized；Described unmanned plane is formed into columns at least three frame unmanned planes, respectively task engine A, task engine B, for appointing Business machine provides the server C of the energy；Set front and have multiple task point, and task is counted out more than task engine number, unmanned plane Form into columns before receiving out and saying the word, be positioned at starting point O and await orders；

Step 2: set up the communication between ground control cabinet, position location satellite and unmanned plane formation: when performing front task, logical Cross satellite positioning tech and obtain each task point, the positional information of unmanned plane in real time；

Step 3: the positional information of each to unmanned plane and front task point is returned to ground control cabinet by position location satellite in real time, ground is controlled Platform real-time reception processed, analyze and process these positional informationes；

Step 4: host computer passes through Revised genetic algorithum, introduces three exchange crossover operators and each task point is distributed to each task engine, Obtain preferable air route l₀, i.e. task engine is not by the air route of power consumption constraint；Setting up task point distribution model is class MTSP model, i.e. For travelling salesman without the class MTSP model returned；

Step 5: obtain the position of energy recharge point P: host computer combines constraints simulation task engine at preliminary preferable air route l₀ Upper flight, obtains first energy recharge point P₀Position；

Step 6: set unmanned plane formation and arrive first energy recharge point P₀After, task distribution system is to remaining task point weight New work task is distributed, and seeks next energy recharge point, circular flow step 2, step 3, step 4, step the most always simultaneously 5, until completing the distribution of all tasks point；

Step 7: after all task points are assigned, control station sends control command, and unmanned plane is formed into columns and received order, from O Point sets out, and goes to each task point to perform the task of distribution along the planning air route specified.

Multiple no-manned plane method for allocating tasks under power consumption constraint the most according to claim 1, it is characterised in that: task engine A Being isomorphism unmanned plane with task engine B, server C and task engine are isomery unmanned plane, and server C energy consumption limits and is enough to ensure that Complete all tasks, task engine A and task engine B limited energy need to carry out in flight course the energy supplement.

Multiple no-manned plane method for allocating tasks under power consumption constraint the most according to claim 1, it is characterised in that: the described energy Supply point P is the point of task engine depleted of energy, is also server C and task engine A, the point of task engine B；And not Meter converges the time spent by the moment, and the flight route of server C for going to each energy recharge point successively from starting point；Each Judge in individual iteration cycle each task engine the need of carrying out energy recharge, energy recharge point P be positioned at a certain moment task engine A with The line midpoint of B, when a team forms into columns and arrives energy recharge point P, task engine A, the energy of task engine B have exhausted, The flight path of server C is by the straight line path of starting point O to energy recharge point P.

Multiple no-manned plane method for allocating tasks under power consumption constraint the most according to claim 1, it is characterised in that: individual task When the air route length of machine is i.e. up to air route ultimate value, energy recharge point is gone in this task engine.

Multiple no-manned plane method for allocating tasks under power consumption constraint the most according to claim 1, it is characterised in that: in step 4 Setting task engine is formed into columns after having accessed all tasks point, rests on last task point, it is not necessary to returns starting point, obtains task Machine preliminary not by the air route l of power consumption constraint₀, namely preferable air route l₀。