CN106197426A - A kind of unmanned plane emergency communication paths planning method and system - Google Patents

Abstract

The present invention relates to a kind of unmanned plane emergency communication paths planning method and system, its method comprises the following steps: step 1. carries out three-dimensional grid division to the space of unmanned plane during flying, obtain grid node, build grid chart, the starting point of mark unmanned plane and impact point, and mark radar distribution and terrain information；Step 2. is distributed according to the radar in unmanned plane during flying space and terrain information builds flight threat modeling, builds flight simultaneously and limits model；Step 3. limits model construction flight path according to flight threat modeling and flight；Flight path is optimized by step 4. by Chaos Genetic Algorithm, determines final flight path.Hinge structure, the present invention can avoid safely the interference of obstruction and radar, arrives and specifies the terminal flight time the shortest and path optimum, has preferable real-time and rapidity.

Description

A kind of unmanned plane emergency communication paths planning method and system

Technical field

The present invention relates to unmanned air vehicle technique field, particularly to a kind of unmanned plane emergency communication paths planning method and be System.

Background technology

Unmanned plane is in emergency communication, territory security protection, assist alert, emergency relief and field of agricultural cultivation to obtain more and more in the air Being widely applied, unmanned plane more gains great popularity because of the VTOL of its uniqueness, hovering performance. at complicated, limited sky In between, unmanned plane can utilize its hovering, horizontal fly, the flying method such as inverted flight shuttles back and forth between barrier, complete communication base station and take Build, topographic survey, scene capture, the task such as rescue and relief work.Unlike the vehicle on two dimensional surface and mobile robot, appoint How collision is all fatal for depopulated helicopter, directly results in preplanned mission failure, autonomous quickly hedging, searching optimal trajectory Ability is that it completes the key point of task.Therefore the most real-time on the premise of guaranteeing safe flight optimal path is cooked up Have become as the current problem demanding prompt solution of unmanned plane industry.

At present, conventional Path Planning for Unmanned Aircraft Vehicle algorithm is linear, nonlinear programming approach, A* algorithm, ant group algorithm etc..

Linearly, non-linear unmanned plane paths planning method, can consider the most in all directions during unmanned plane during flying with road Safety that footpath is relevant, enforceability etc., shortcoming is that these methods to solve a series of constrained optimization problems, computationally intensive, Calculating time length, convergence rate are slow and easy impact by local minimum is absorbed in locally optimal solution, it is adaptable to local tracks is planned.

A* algorithm, due to its fireballing feature, is widely used, and its performance relies on choosing of heuristic function, and Reality obtains optimum heuristic function acquire a certain degree of difficulty, therefore A* algorithm typically can only search one the most excellent Path, the information that obtains in search first can not be utilized when unmanned plane during flying flight path changes, can only re-start Search, this is accomplished by path planning again of long time.

Ant group algorithm is that Formica fusca randomly chooses a direction search when looking for food, after a Formica fusca finds food source, with Certain mode tells that companion carries food together, and the path walked after many Formica fusca cooperation carryings has reformed into the shortest road Footpath.Ant group algorithm is a kind of Global Planning, can consider multiple target simultaneously, but when burst change occurs in environment or needs in real time During planning, ant group algorithm it cannot be guaranteed that in finite time quickly search path.

Summary of the invention

The technical problem to be solved is to provide a kind of unmanned plane emergency communication paths planning method and system, energy Safety avoids the interference of obstruction and radar, arrives and specifies the terminal flight time the shortest and path optimum, simultaneously original On the basis of genetic algorithm, utilize chaos sequence to the intersection controlling in genetic manipulation and variation, it is to avoid completely random operation Blindness, has preferable real-time and rapidity, and the flight path obtained more approaches the unmanned plane optimal trajectory of reality.

The technical scheme is that a kind of unmanned plane emergency communication paths planning method, Comprise the following steps:

Step 1. carries out three-dimensional grid division to the space of unmanned plane during flying, obtains grid node, builds grid chart, described Mark the radar distribution in starting point and the impact point of unmanned plane, and mark unmanned plane during flying space on grid chart and landform is believed Breath；

Step 2. is distributed according to the radar in unmanned plane during flying space and terrain information builds flight threat modeling, simultaneously root Build flight according to the airmanship parameter of unmanned plane and limit model；

Step 3. limits model according to flight threat modeling and flight and builds between the starting point and impact point of unmanned plane Flight path；

Flight path is optimized by step 4. by Chaos Genetic Algorithm, determines final flight path.

The invention has the beneficial effects as follows: the interference of obstruction and radar can be avoided safely, arrive and specify terminal flight Shortest time and path are optimum, simultaneously on the basis of original genetic algorithm, utilize chaos sequence to the friendship controlling in genetic manipulation Fork and variation, replace intersection and the mutation operation of original completely random, accurately determine whether to carry out to intersect or mutation operation and Determine two aspects such as particular location of intersection or mutation operation, it is to avoid the blindness of random operation, make the solution precision tried to achieve Height, fast convergence rate, the technical program has preferable real-time and rapidity simultaneously, make unmanned plane perform emergency communication, During the tasks such as air rescue, the flight path searched more approaches the unmanned plane optimal trajectory of reality.

On the basis of technique scheme, the present invention can also do following improvement.

Further, described flight threat modeling particularly as follows:

Min W=k₁J_threat+k₂J_fuel；

Wherein J_threatFor threat radar cost, k₁(k₁∈ (0,1)) it is the weight of threat radar cost, J_fuelFor fuel oil generation Valency, k₂(k₂∈ (0,1)) it is the weight of fuel penalty.

Above-mentioned further scheme is used to provide the benefit that: the restriction threatened by threat radar and fuel oil is modeled, it is ensured that Unmanned plane, without threatening flight, selects unmanned plane during flying path more accurate.

Further, described flight restriction model includes:

Unmanned plane changes distance r that attitude the flies nonstop to starting point less than unmanned plane between barrier point when running into barrier Distance S_min, r ＜ S_min。

Above-mentioned further scheme is used to provide the benefit that: to be limited by the distance that unmanned plane is flown nonstop to when changing attitude Fixed, can guarantee that unmanned plane is by setting path safe flight.

Further, described flight restriction model includes:

The time T that unmanned plane hovers when detecting barrier_minTotal duration t, T less than unmanned plane during flying_min＜ t.

Above-mentioned further scheme is used to provide the benefit that: the limit of time of being hovered when detecting barrier by unmanned plane Fixed, can guarantee that unmanned plane is by the smooth and easy flight of setting path.

Further, described flight restriction model includes:

Minimum flight altitude H that the height H of flight path sets more than unmanned plane during flying_min, and set less than unmanned plane during flying The highest fixed flying height H_max, H_min＜ H ＜ H_max。

Above-mentioned further scheme is used to provide the benefit that: by the restriction of the height H of flight path, to can guarantee that unmanned plane By the smooth and easy flight of flight path.

Further, described flight restriction model includes:

Distance L of flight path is less than 1/2nd of unmanned plane during flying critical distance, L ＜ L_max/2。

Preferably, described flight limits model and includes: unmanned plane, along the positive direction flight of x coordinate axle, is currently located joint The coordinate of point is (x1, y1, z1), and the coordinate of next node is (x2, y2, z2), and unmanned plane is the most inclined The maximum angle Ф turned meets:

$\left\{\begin{array}{c}\left|\arctan \left(\frac{y_2-y_1}{x_2-x_1}\right)\right|\≤{\mathrm{\φ}}_{max}\\ \left|\arctan \left(\frac{z_2-z_1}{x_2-x_1}\right)\right|\≤{\mathrm{\γ}}_{max}\end{array}\right.;$

Wherein Ф_maxExtreme angles for the deflection of unmanned plane horizontal direction；у_maxThe limit for the deflection of unmanned plane vertical direction Angle.

Above-mentioned further scheme is used to provide the benefit that: by the restriction of the height H of flight path, to can guarantee that unmanned plane By the smooth and easy flight of flight path.

Further, by Chaos Genetic Algorithm flight path is optimized and specifically includes following steps:

Step 4.1. utilizes chaotic motion Logistic mapping equation x_k+1=ux_k(1-x_k) (1), stochastic generation original chaotic Sequence, wherein u is to control parameter, and as u=4, above formula is completely in chaos state, and is traversal in [0,1]；

Chaos Variable X that step 4.2. will once produce^KIt is mapped to new Chaos Variable X, simultaneously by whole time by (2) formula Go through interval [0,1] and be mapped to the interval [a, b] of optimized variable；

$X=a+x_i^k(b-a)---\left(2\right);$

Step 4.3. utilizes described Chaos Variable, carries out Chaos Search；

The interval range [a, b] that the arbitrary width produced by chaos generator performs task at unmanned plane is interior to flight road Line carries out disturbance, and flight path is in real time according to the departure degree L of impact point and initial point line simultaneously_iAnd unmanned plane distance prestige The minimum distance C of the side of body_i, calculate section in flight path individual just when f (X), obtain section individual just when entering from high to low Row sequence, eliminates just when 10% low section individuality, remaining individual just when 90% high section；

Step 4.4. circulation step 4.1 to step 4.3, until section individual amount reaches setting scale, is combined into initial kind Group；

Step 4.5. randomly chooses two pairing individualities in initial population in 90% high individuality, by chaotic crossover Rule carries out intersection operation；

Pairing individuality is carried out mutation operation by chaotic mutation rule by step 4.6., obtains multiple variation individuality, by multiple changes In different individuality and initial population, in sequence, the individuality of front 10% merges the secondary population of composition；

In two same individual in secondary colony one is deleted by step 4.7., simultaneously in secondary population just when After in sequence, the individuality of 10% is eliminated, and obtains in secondary population individual just when high 90%；

Step 4.8. carries out Chaos-Genetic operation in secondary population just when 90% high individuality, generates multiple heredity Body, and constitute genetic groups, genetic groups will be eliminated just when 90% low individuality, obtain in genetic groups just when high 10% is individual；

Step 4.9. will be decoded just when 10% high individuality in genetic groups, and planning draws final unmanned plane during flying Path.

Above-mentioned further scheme is used to provide the benefit that: to add chaos operator in genetic algorithm, utilize chaos sequence Control the intersection in genetic manipulation and variation, to replace intersection and the variation behaviour of original completely random under certain probability Make, avoid the blindness that completely random operates；Effectively prevent from and overcome evolution causes screening because population diversity reduces The most accurate.

Further, just when calculate particularly as follows:

$f\left(X\right)={\mathrm{\Σ}}_{i=1}^{N-1}\frac{Li}{\left(N-i\right)*d0}*\frac{10}{di,\min }*l_i\left(X_i\right)*C_i;$

WhereinIt it is the departure degree penalty coefficient of path deviation impact point and initial point line；

Li is i-th path point distance to impact point, and d0 is the length of air route section, generally according to the navigation mode of unmanned plane Determine,

(N-i) * d0 is the distance along initial point Yu target link direction, l_iIt it is the actual range of route segment；WhereinIt it is the penalty coefficient of safety；

Di, min are the minimum distances that the i-th route segment path distance threatens, C_iFor path impact point and its former point it Between deflection angle meet the penalty coefficient of constraint.

Above-mentioned further scheme is used to provide the benefit that: to promote just when the precision calculated, it is ensured that route selection is optimum.

Another technical scheme that the present invention solves above-mentioned technical problem is as follows: a kind of unmanned plane emergency communication path planning system System, including:

Divide module, for the space of unmanned plane during flying is carried out three-dimensional grid division, obtain grid node, build grid Figure, the radar marked on described grid chart in starting point and the impact point of unmanned plane, and mark unmanned plane during flying space divides Cloth and terrain information；

MBM, threatens mould for being distributed to build to fly with terrain information according to the radar in unmanned plane during flying space Type, builds flight according to the airmanship parameter of unmanned plane simultaneously and limits model；

Build route module, for limiting model in the starting point of unmanned plane and target according to flight threat modeling and flight Flight path is built between point；

Screening module, for being optimized flight path by Chaos Genetic Algorithm, determines final flight path.

The invention has the beneficial effects as follows: the interference of obstruction and radar can be avoided safely, arrive and specify terminal flight Shortest time and path are optimum, simultaneously on the basis of original genetic algorithm, utilize chaos sequence to the friendship controlling in genetic manipulation Fork and variation, replace intersection and the mutation operation of original completely random, accurately determine whether to carry out to intersect or mutation operation and Determine two aspects such as particular location of intersection or mutation operation, it is to avoid the blindness of completely random operation, make the solution tried to achieve Precision is high, fast convergence rate, and the technical program has preferable real-time and rapidity simultaneously, makes unmanned plane perform emergent leading to During the tasks such as letter, air rescue, the flight path searched more approaches the unmanned plane optimal trajectory of reality.

Accompanying drawing explanation

Fig. 1 is the flow chart of the present invention a kind of unmanned plane emergency communication paths planning method；

Fig. 2 is the module frame chart of the present invention a kind of unmanned plane emergency communication path planning system.

In accompanying drawing, the list of parts representated by each label is as follows:

1, module is divided, 2, MBM, 3, build route module, 4, screening module.

Detailed description of the invention

Being described principle and the feature of the present invention below in conjunction with accompanying drawing, example is served only for explaining the present invention, and Non-for limiting the scope of the present invention.

As it is shown in figure 1, a kind of unmanned plane emergency communication paths planning method, comprise the following steps:

Step 1. carries out three-dimensional grid division to the space of unmanned plane during flying, obtains grid node, builds grid chart, described Mark the radar distribution in starting point and the impact point of unmanned plane, and mark unmanned plane during flying space on grid chart and landform is believed Breath；

Step 2. is distributed according to the radar in unmanned plane during flying space and terrain information builds flight threat modeling, simultaneously root Build flight according to the airmanship parameter of unmanned plane and limit model；

Step 3. limits model according to flight threat modeling and flight and builds between the starting point and impact point of unmanned plane Flight path；

Flight path is optimized by step 4. by Chaos Genetic Algorithm, obtains final flight path.

Preferably, described flight threat modeling particularly as follows:

Min W=k₁J_threat+k₂J_fuel；

Wherein J_threatFor threat radar cost, k₁(k₁∈ (0,1)) it is the weight of threat radar cost, J_fuelFor fuel oil generation Valency, k₂(k₂∈ (0,1)) it is the weight of fuel penalty；Threat radar cost is the restriction of unmanned plane during flying height, and flying height surpasses Cross setting height, easily investigated by radar, there is tracked risk, be that unmanned plane needs to keep away rule；Fuel penalty is nothing The weight of man-machine own load fuel oil, meets the fuel demand of unmanned plane shuttle flight, and flying distance is long, easily causes unmanned plane Cannot return, be that unmanned plane needs to evade.

Preferably, described flight restriction model includes:

Unmanned plane when changing attitude distance r flown nonstop to less than the starting point of unmanned plane to distance S between impact point_min, R ＜ S_min。

Preferably, described flight restriction model includes:

The time T that unmanned plane hovers when detecting barrier_minTotal duration t, T less than unmanned plane during flying_min＜ t.

Preferably, described flight restriction model includes:

Minimum flight altitude H that the height H of flight path sets more than unmanned plane during flying_min, and set less than unmanned plane during flying The highest fixed flying height H_max, H_min＜ H ＜ H_max。

Preferably, described flight restriction model includes:

Distance L of flight path is less than 1/2nd of unmanned plane during flying critical distance, L ＜ L_max/2。

Preferably, described flight limits model and includes: unmanned plane, along the positive direction flight of x coordinate axle, is currently located joint The coordinate of point is (x1, y1, z1), and the coordinate of next node is (x2, y2, z2), and unmanned plane is the most inclined The maximum angle Ф turned meets:

$\left\{\begin{array}{c}\left|\arctan \left(\frac{y_2-y_1}{x_2-x_1}\right)\right|\≤{\mathrm{\φ}}_{max}\\ \left|\arctan \left(\frac{z_2-z_1}{x_2-x_1}\right)\right|\≤{\mathrm{\γ}}_{max}\end{array}\right.;$

Wherein Ф_maxExtreme angles for the deflection of unmanned plane horizontal direction；у_maxThe limit for the deflection of unmanned plane vertical direction Angle.

Preferably, by Chaos Genetic Algorithm flight path is optimized and specifically includes following steps:

Step 4.1. utilizes chaotic motion Logistic mapping equation x_k+1=ux_k(1-x_k) (1), stochastic generation original chaotic Sequence, wherein u is to control parameter, and as u=4, above formula is completely in chaos state, and is traversal in [0,1]；

Chaos Variable X that step 4.2. will once produce^KIt is mapped to new Chaos Variable X, simultaneously by whole time by (2) formula Go through interval [0,1] and be mapped to the interval [a, b] of optimized variable；

$X=a+x_i^k(b-a)---\left(2\right);$

Step 4.3. utilizes described Chaos Variable, carries out Chaos Search；

The interval range [a, b] that the arbitrary width produced by chaos generator performs task at unmanned plane is interior to flight road Line carries out disturbance, and flight path is in real time according to the departure degree L of impact point and initial point line simultaneously_iAnd unmanned plane distance prestige The minimum distance C of the side of body_i, calculate section in flight path individual just when f (X), obtain section individual just when entering from high to low Row sequence, eliminates just when 10% low section individuality, remaining individual just when 90% high section；

Step 4.4. circulation step 4.1 to step 4.3, until section individual amount reaches setting scale, is combined into initial kind Group；

Step 4.5. randomly chooses two pairing individualities in initial population in 90% high individuality, by chaotic crossover Rule carries out intersection operation；

Pairing individuality is carried out mutation operation by chaotic mutation rule by step 4.6., obtains multiple variation individuality, by multiple changes In different individuality and initial population, in sequence, the individuality of front 10% merges the secondary population of composition；

In two same individual in secondary colony one is deleted by step 4.7., simultaneously in secondary population just when After in sequence, the individuality of 10% is eliminated, and obtains in secondary population individual just when high 90%；

Step 4.8. carries out Chaos-Genetic operation in secondary population just when 90% high individuality, generates multiple heredity Body, and constitute genetic groups, genetic groups will be eliminated just when 90% low individuality, obtain in genetic groups just when high 10% is individual；

Step 4.9. will be decoded just when 10% high individuality in genetic groups, and planning draws final unmanned plane during flying Path.

Section individuality is a flight section in whole flight path, as the gene in Chaos Genetic Algorithm；

Chaotic crossover determines operation: select intervalWhen carrying out intersecting operation, two pairings selected Body, according to x_k+1=ux_k(1-x_k), the currency x of a produced independent chaos sequence_k1Determine if to intersect, if x_k1∈P_cThen carry out intersecting operating, otherwise, do not intersect.

The determination of intersection position, using individual for circuit as gene, according to the length of chromosome in gene, is classified as some Section, can several or 1 be one section, interval (0,1) is also classified into some subintervals simultaneously, the most each subinterval is the most corresponding A section in chromosome, for needing to carry out intersecting the individuality operated, according to x_k+1=ux_k(1-x_k), another of generation is only The currency x of vertical chaos sequence_k2Affiliated subinterval determines the position carrying out intersection operation gene section；

Two selected chromosome corresponding gene sections are swapped, produces two new individualities, thus complete chaos and hand over Fork；

Chaotic mutation operates: the determination of variation, as the determination method intersected, and the simply interval P of variation_mGenerally should be smaller than Transposition section P_c, only as the currency x of chaos sequence_k3∈P_m, just carry out mutation operation；

The determination of variation position, makees N decile by interval (0,1), and wherein N is the length of chromosome, works as further according to chaos sequence Front value x_k4The gene location of residing subinterval definitive variation；

Individual to the pairing produced through chaotic crossover, carry out mutation operation by chaotic mutation rule；x_iFor mutation operation Front i-th is individual, its corresponding vector being made up of the component of N, x_iJ () is jth component, x '_iJ () is for variation after The jth component that i-th is individual, σ_iJ () is the mutation scaling of jth component approximation, use chaotic mutation form as follows:

x′_i(j)=x_i(j)+σ_i(j)K_j(0,1)；Wherein K (0,1) is the sequence of chaos rule change.

The variation individuality that will obtain through aforesaid operations, by individual for multiple variations individual just when high 10% with initial population Merge and constitute secondary population, same individual in colony is carried out filter operation simultaneously, retain one of them, and to same or Other similar individuality carries out the chaotic mutation operation that probability is interior in a big way, to ensure the multiformity of colony, it is to avoid algorithm falls into Enter local minimum.

Preferably, just when calculate particularly as follows:

$f\left(X\right)={\mathrm{\Σ}}_{i=1}^{N-1}\frac{Li}{\left(N-i\right)*d0}*\frac{10}{di,\min }*l_i\left(X_i\right)*C_i;$

WhereinIt it is the departure degree penalty coefficient of path deviation impact point and initial point line；

Li is i-th path point distance to impact point, and d0 is the length of air route section, generally according to the navigation mode of unmanned plane Determine,

(N-i) * d0 is the distance along initial point Yu target link direction, l_iIt it is the actual range of route segment；WhereinIt it is the penalty coefficient of safety；

Di, min are the minimum distances that the i-th route segment path distance threatens, C_iFor path impact point and its former point it Between deflection angle meet the penalty coefficient of constraint.

As in figure 2 it is shown, a kind of unmanned plane emergency communication path planning system, including:

Divide module 1, for the space of unmanned plane during flying is carried out three-dimensional grid division, obtain grid node, build grid Figure, the radar marked on described grid chart in starting point and the impact point of unmanned plane, and mark unmanned plane during flying space divides Cloth and terrain information；

MBM 2, threatens mould for being distributed to build to fly with terrain information according to the radar in unmanned plane during flying space Type, builds flight according to the airmanship parameter of unmanned plane simultaneously and limits model；

Build route module 3, for limiting model at the starting point of unmanned plane and mesh according to flight threat modeling and flight Flight path is built between punctuate；

Screening module 4, for being screened flight path by Chaos Genetic Algorithm, must determine flight path.

What flight path was optimized by Chaos Genetic Algorithm implements:

Systematic parameter is initialized: the x of Chaos Variable_kInitial value is 0.2；The interval range a of unmanned plane during flying task, The initial value of b is a=0, b=2000m；Take the longest current flying distance for=2000m；Make primary carrier iterations k initial value It is 0, cycle-index i=1；By the initial value x of Chaos Variable₀Substitute into x_k+1=4x_k(1-x_k), it is iterated computing, and above formula is obtained The substitution x arrived_k+1Substitute intoObtain the impact point of unmanned plane during flying and the departure degree X of initial point line, order K=k+1；

Calculate unmanned plane during flying path just when；

Whether unmanned plane during flying path distance is more than the 2000m of unmanned plane during flying range prediction, if it is, by unmanned plane The value of flying distance is assigned to the maximum of man-machine flying distance prediction；If it is not, then perform i=i+1；

Judge that cycle-index i is whether more than 20 or whether the Chaos Variable iterations k of primary carrier is more than iteration time Several 100, if it is, calculate each paths in flight path just when, the path obtained is just when being ranked up from high to low, right Eliminate just when 10% low individuality, remaining individual just when high 90%；If it is not, then by the initial value x of Chaos Variable₀Substitute into x_k+1=4x_k(1-x_k) it is iterated computing, then it is circulated.

The foregoing is only presently preferred embodiments of the present invention, not in order to limit the present invention, all spirit in the present invention and Within principle, any modification, equivalent substitution and improvement etc. made, should be included within the scope of the present invention.

The foregoing is only presently preferred embodiments of the present invention, not in order to limit the present invention, all spirit in the present invention and Within principle, any modification, equivalent substitution and improvement etc. made, should be included within the scope of the present invention.

Claims (10)

1. a unmanned plane emergency communication paths planning method, it is characterised in that comprise the following steps:

Step 1. carries out three-dimensional grid division to the space of unmanned plane during flying, obtains grid node, builds grid chart, at described grid The radar distribution in starting point and the impact point of unmanned plane, and mark unmanned plane during flying space and terrain information is marked on figure；

Step 2. is distributed according to the radar in unmanned plane during flying space and terrain information builds flight threat modeling, simultaneously according to nothing Man-machine airmanship parameter builds flight and limits model；

Step 3. limits model according to flight threat modeling and flight and builds flight between the starting point and impact point of unmanned plane Route；

Flight path is optimized by step 4. by Chaos Genetic Algorithm, determines final flight path.

A kind of unmanned plane emergency communication paths planning method, it is characterised in that described flight threatens Model is particularly as follows: min W=k₁J_threat+k₂J_fuel；

Wherein J_threatFor threat radar cost, k₁(k₁∈ (0,1)) it is the weight of threat radar cost, J_fuelFor fuel penalty, k₂ (k₂∈ (0,1)) it is the weight of fuel penalty.

A kind of unmanned plane emergency communication paths planning method, it is characterised in that described flight limits Model includes:

Unmanned plane change when running into barrier distance r that attitude flies nonstop to less than unmanned plane starting point between barrier point away from From S_min, r ＜ S_min。

A kind of unmanned plane emergency communication paths planning method, it is characterised in that described flight limits Model includes:

The time t that unmanned plane hovers when barrier being detected is more than the longest T of unmanned aerial vehicle (UAV) control response_min, T_min＜ t.

A kind of unmanned plane emergency communication paths planning method, it is characterised in that described flight limits Model includes:

Minimum flight altitude H that the height H of flight path sets more than unmanned plane during flying_min, and set less than unmanned plane during flying The highest flying height H_max, H_min＜ H ＜ H_max。

A kind of unmanned plane emergency communication paths planning method, it is characterised in that described flight limits Model includes:

Distance L of flight path is less than 1/2nd of unmanned plane during flying critical distance, L ＜ L_max/2。

A kind of unmanned plane emergency communication paths planning method, it is characterised in that described flight limits Model includes: unmanned plane flies along the positive direction of x coordinate axle, and the coordinate being currently located node is (x1, y1, z1), next joint The coordinate of point is (x2, y2, z2), and the maximum angle Ф that unmanned plane both horizontally and vertically deflects meets:

$\left\{\begin{array}{c}\left|\arctan \left(\frac{y_2-y_1}{x_2-x_1}\right)\right|\≤{\mathrm{\φ}}_{max}\\ \left|\arctan \left(\frac{z_2-z_1}{x_2-x_1}\right)\right|\≤{\mathrm{\γ}}_{max}\end{array}\right.;$

Wherein Ф_maxExtreme angles for the deflection of unmanned plane horizontal direction；у_maxLimiting angle for the deflection of unmanned plane vertical direction Degree.

A kind of unmanned plane emergency communication paths planning method, it is characterised in that pass through Chaos-Genetic Flight path is optimized and specifically includes following steps by algorithm:

Step 4.1. utilizes chaotic motion Logistic mapping equation x_k+1=ux_k(1-x_k) (1), stochastic generation original chaotic sequence Row, wherein u is to control parameter, and as u=4, above formula is completely in chaos state, and is traversal in [0,1]；

Chaos Variable X that step 4.2. will once produce^KIt is mapped to new Chaos Variable X, simultaneously by whole traversal district by (2) formula Between [0,1] be mapped to the interval [a, b] of optimized variable；

$X=a+x_i^k(b-a)---\left(2\right)$

Step 4.3. utilizes described Chaos Variable, carries out Chaos Search；

Flight path is entered in unmanned plane performs the interval range [a, b] of task by the arbitrary width produced by chaos generator Row disturbance, flight path is in real time according to the departure degree L of impact point and initial point line simultaneously_iAnd unmanned plane distance threat Minimum distance C_i, calculate section in flight path individual just when f (X), obtain section individual just when arranging from high to low Sequence, eliminates just when 10% low section individuality, remaining individual just when 90% high section；

Step 4.4. circulation step 4.1 to step 4.3, until section individual amount reaches setting scale, is combined into initial population；

Step 4.5. randomly chooses two pairing individualities in initial population in 90% high individuality, by chaotic crossover rule Carry out intersecting and operate；

Pairing individuality is carried out mutation operation by chaotic mutation rule by step 4.6., obtains multiple variation individuality, by multiple variations In body and initial population, in sequence, the individuality of front 10% merges the secondary population of composition；

In two same individual in secondary colony one is deleted by step 4.7., simultaneously in secondary population just when sequence After in, the individuality of 10% is eliminated, and obtains in secondary population individual just when high 90%；

Step 4.8. carries out Chaos-Genetic operation in secondary population just when 90% high individuality, generates multiple heredity individual, and Constitute genetic groups, genetic groups will be eliminated just when 90% low individuality, obtain in genetic groups just when high 10% Body；

Step 4.9. will be decoded just when 10% high individuality in genetic groups, and planning draws final unmanned plane during flying road Footpath.

A kind of unmanned plane emergency communication paths planning method, it is characterised in that concrete just when calculating For:

$f\left(X\right)={\mathrm{\Σ}}_{i=1}^{N-1}\frac{Li}{\left(N-i\right)*d0}*\frac{10}{di,\min }*l_i\left(X_i\right)*C_i;$

WhereinIt it is the departure degree penalty coefficient of path deviation impact point and initial point line；

Li is i-th path point distance to impact point, and d0 is the length of air route section, and the navigation mode generally according to unmanned plane is true Fixed；

(N-i) * d0 is the distance along initial point Yu target link direction, l_iIt it is the actual range of route segment；WhereinIt is The penalty coefficient of safety；

Di, min are the minimum distances that the i-th route segment path distance threatens, C_iInclined between impact point and its former point in path Corner meets the penalty coefficient of constraint.

10. a unmanned plane emergency communication path planning system, it is characterised in that including:

Divide module (1), for the space of unmanned plane during flying is carried out three-dimensional grid division, obtain grid node, build grid chart, Described grid chart marks starting point and the impact point of unmanned plane, and radar distribution in mark unmanned plane during flying space and Terrain information；

MBM (2), for being distributed according to the radar in unmanned plane during flying space and terrain information structure flight threat modeling, Build flight according to the airmanship parameter of unmanned plane simultaneously and limit model；

Build route module (3), for limiting model in the starting point of unmanned plane and target according to flight threat modeling and flight Flight path is built between point；

Screening module (4), for being screened flight path by Chaos Genetic Algorithm, must determine flight path.