CN104359473A - Collaborative flight path intelligent planning method for formation flying of unmanned planes under dynamic environment - Google Patents

Abstract

The invention discloses a collaborative flight path intelligent planning method for formation flying of unmanned planes under a dynamic environment. The method comprises the steps of offline intelligent planning of a formation pre-flying collaborative flight path of the unmanned planes, online replanning of the flight path for avoiding threats, collaborative rebuilding of a formation team and the like. The offline intelligent planning of the formation pre-flying collaborative flight path of the unmanned planes adopts an intelligent planning method based on a Voronoi graph and an ant colony algorithm, and an integrated optimal pre-flying flight path with collaborative time can be planned off line for the unmanned plane. The replanning of the online flight path adopts an intelligent flight path planning method based on an RRT (Rail Rapid Transit) algorithm, and quick flight path correction can be provided when the unmanned plane formation meets a sudden threat. The collaborative rebuilding of the formation team adopts a method combining unmanned plane formation member flying speed adjustment and coiling maneuvering. According to the method, pre-flying collaborative flight path offline planning and generation of online replanned flight path are provided for the unmanned plane formation, a collaborative rebuilding scheme is provided, and the online rebuilding problem of unmanned plane formation flying cooperativity is solved.

Description

The collaborative flight path Intelligent planning method of UAV Formation Flight under a kind of dynamic environment

Technical field

The present invention relates to the collaborative path planning method of UAV Formation Flight, particularly relate to a kind of intelligent path planning method under dynamic environment, belong to trajectory planning field.

Background technology

UAV Formation Flight is that unmanned plane multi rack with autonomic function carries out three-dimensional arrangement according to certain version, ensures the stable of formation in its flight course, but can adjust dynamically according to the change of external circumstances and mission requirements.The form into columns energy force rate unit flight of the success ratio of executing the task and anti-accident of unmanned plane is much higher, obtains at present applying more and more widely.

The trajectory planning of UAV Formation Flight comprises off-line trajectory planning and online trajectory planning, formation optimal trajectory was generated according to the various algorithm off-line of the Information Pull such as existing environment before flight, in practical flight process, due to the uncertainty of battlefield surroundings, when formation is on preset flight path during practical flight, if there is pop-up threats, now predetermined flight path can not meet optimum or safety requirements, just requires that carrying out online weight-normality to flight path draws.

The algorithm of existing many online flight paths at present, but majority is for unit, the difficulty that the online flight path weight-normality of forming into columns is drawn is that formation flight distinctive time, spatial cooperation compared with unit make Path Planning occur retraining phenomenon that is more, shot array, how ensureing that the weight-normality of flight path is drawn in addition can not to destruction flight pattern, or flight path weight-normality draw after how to carry out rebuilding for destroyed concertedness be also emphasis and difficult point.

Summary of the invention

Technical matters to be solved:

The UAV Formation Flight that the object of this invention is to provide under a kind of dynamic environment works in coordination with flight path Intelligent planning method, draws and concertedness Problems of Reconstruction for the flight path weight-normality solved when UAV Formation Flight meets with pop-up threats online.

Technical scheme:

In order to realize above function, the invention provides the collaborative flight path Intelligent planning method of UAV Formation Flight under a kind of dynamic environment, it is characterized in that: realize according to following steps:

Step 1, adopts Voronoi figure to turn to a series of line segment by discrete for continuous print flight space; Then utilize ant group algorithm to be that each frame unmanned plane searches many flight paths to be selected, then work in coordination with time decision principle according to optimum and choose every frame unmanned plane and finally treat flight mark, and set the flying speed of each frame unmanned plane with this; Be the collaborative flight path during unmanned aerial vehicle design off-line flight of formation flight in advance;

Step 2, real-time detection also judges whether unmanned plane working direction exists barrier and pop-up threats, if existed, starts to carry out flight path weight-normality draw process in the moment detecting pop-up threats, adopts RRT algorithm to walk around threat fast and revert on pre-flight mark;

Step 3, for the unmanned aerial vehicle design walking around threat returns the air route of forming into columns, makes this unmanned plane and other unmanned planes rebuild concertedness state of flight, completes the concertedness reconstruction that unmanned plane is formed into columns.

Further, the concrete grammar of described step 3 is:

Step 3.1, first, determines that concertedness rebuilds the moment, is defined as the moment that every frame unmanned plane all walks around threat, send signal to determine when when actual realization being and walking around threat by every frame unmanned plane to other unmanned planes;

Step 3.2, then, determines the residue pre-flight mark length of every frame unmanned plane;

Step 3.3, can the adjustment judging by depending merely on speed realize synergitic reconstruction and namely arrive destination simultaneously, if can not, need unmanned plane and carry out wait of spiraling, rebuild until concertedness can be realized by the adjustment depending merely on speed.

Further, Voronoi figure is constructed in step 1 by the following method:

The one group of polygon be made up of the perpendicular bisector of the central point line segment in the adjacent threat source of connection two, chooses every frame unmanned plane in formation as a virtual threat point, and is suitably choosing several point as virtual threat point near threatening area boundary;

Utilize ant group algorithm to be the concrete grammar that each frame unmanned plane searches many flight paths to be selected in step 1 to be:

A. the determination of ant reference position, using the set of the Voronoi figure vertex set adjacent with unmanned plane starting point as ant reference position;

B. the determination of end condition, the end condition that every ant carries out route searching should be arrive destination node set;

C. the generation of many group solutions, by selecting suitable parameter, gets caulocarpic union, to obtain several groups of different solutions.

Further, the employing RRT algorithm described in step 2 walks around the threat concrete grammar revert on pre-flight mark fast: first selected start node q in mission area _initas the root node of tree, build random tree by the mode constantly expanding leaf node from root node; First with Probability p _gselect target position q _goalas random targets point q _rand, or with probability 1-p _gstochastic choice random targets point q in task space _rand; Then chosen distance random targets point q in all leaf nodes of current random tree _randnearest leaf node, and be referred to as neighbor node q _near; Then from q _nearto q _randdirection extend the distance ε of a step-length, obtain a new node q _new.In extension process, judge whether to have with known threatening area to conflict, if Lothrus apterus accepts this new node q _new, and be added to the node of random tree; If q _newhave with threatening area and conflict, illustrate that the new node that this time expands does not meet safety requirements, then give up this new node, and re-start random targets point q _randchoose.

Further, the concertedness reconstruction that unmanned plane is formed into columns is completed by the following method:

If each unmanned plane is the excess time arrived needed for target when carrying out concertedness and rebuilding:

$\left\{\begin{array}{c}{\mathrm{tr}}_i=[{\mathrm{tr}}_i\min ,{\mathrm{tr}}_i\max ]\\ {\mathrm{tr}}_i\min =L_{\mathrm{ri}}/V_{\max }\\ {\mathrm{tr}}_i\max =L_{\mathrm{ri}}/V_{\min }\end{array}\right.,i=\mathrm{1,2},...,N_U$

In formula, tr _ithat the i-th frame unmanned plane rebuilds in concertedness the time range that the moment arrives target, referred to as excess time, tr _imin is set tr _iin minimum value, namely this unmanned plane flies to the time of impact point according to maximal rate, tr _imax is set tr _iin maximal value; L _rithat the i-th frame unmanned plane rebuilds the flight path length of moment distance objective point, referred to as residue length in concertedness;

The method wherein increasing the time of unmanned plane arrival target is: allow the i-th frame unmanned plane be n away from threatening the track points of point to sentence fixing turning rate ω _risecondary circular motion, the time that each circle increases is

$\mathrm{\Δt}=\frac{2\mathrm{\π}}{\mathrm{\ω}}$

Wherein, ω is obtained by following formula

$\mathrm{\ω}=\frac{q\×n_{y\max }}{V_{\max }}$

In formula, n _ymaxbe maximum normal g-load, therefore concertedness reconstruction model output information comprises the number of turns of spiraling that concertedness rebuilds the unmanned plane that the flying speed in moment after moment, every frame unmanned plane and needs spiral.

Beneficial effect:

The UAV Formation Flight that the present invention designs under a kind of dynamic environment works in coordination with path planning method, with Voronoi figure, ant group algorithm and Quick Extended random tree (RRT) algorithm for instrument, for unmanned plane is formed into columns provide off-line to fly in advance generation that collaborative flight path and online weight-normality draw flight path, and a set of concertedness reconstruction model is proposed, in order to solve the synergitic online Problems of Reconstruction of UAV Formation Flight.

Accompanying drawing explanation

Below in conjunction with drawings and Examples, the present invention is further described:

Fig. 1 is the algorithm flow chart that ant group algorithm of the present invention solves unit Multiple routes planning;

Fig. 2 is collaborative time optimal decision diagram;

Fig. 3 is that flight path weight-normality of the present invention draws overall framework;

Fig. 4 is concertedness process of reconstruction of the present invention;

Fig. 5 is the diamond box that the present invention emulates employing;

Fig. 6 is that battlefield surroundings Voronoi of the present invention schemes;

Fig. 7 of the present inventionly meets with pop-up threats schematic diagram on pre-flight road;

Fig. 8 is RRT algorithm path re-planning analogous diagram of the present invention.

Embodiment

The UAV Formation Flight that the invention provides under a kind of dynamic environment works in coordination with flight path Intelligent planning method, and for making object of the present invention, clearly, clearly, and the present invention is described in more detail with reference to accompanying drawing examples for technical scheme and effect.Should be appreciated that concrete enforcement described herein is only in order to explain the present invention, is not intended to limit the present invention.

The UAV Formation Flight under dynamic environment of the present invention is adopted to work in coordination with flight path Intelligent planning method, specifically according to following steps.

Step 1, complete the planning flying collaborative flight path based on Voronoi figure and the unmanned plane formation off-line of ant group algorithm in advance.When forming into columns larger, to form into columns as target when entirety carries out hiding threat is too large, being unfavorable for hiding, therefore carrying out trajectory planning for every frame unmanned plane, ensureing that formation is constant by time-constrain.Specific implementation is: cross threaten district place of safety according to formation be form into columns each member specify respective impact point, as long as ensure that all members arrive respective specified point at one time and just can ensure that formation is not destroyed.

First, according to known environmental threat information, turn to a series of line segment with Voronoi figure by discrete for continuous print flight space, and calculate the flight path cost of every bar line segment.

The Voronoi figure of structure is the one group of polygon be made up of the perpendicular bisector of the central point line segment in the adjacent threat source of connection two, when constructing, adds suitable virtual threat point.What virtual threat was put chooses the security having influence on every frame unmanned plane and enter threatening area and exit threatening area, after considering, choose every frame unmanned plane in formation as a virtual threat point, and suitably choose several point as virtual threat point near threatening area boundary.

The flight path cost that unmanned plane flies on each limit comprises threat cost and fuel penalty.Article i-th, the threat cost on limit is: J _{threat, i}, calculating for simplifying, each limit being divided into six sections, getting three points wherein summation replaces the threat cost on whole piece limit, as shown in Figure 1, namely

$J_{\mathrm{threat},i}=L_i\underset{j=1}{\overset{N}{\mathrm{\Σ}}}(\frac{1}{d_{1/6i,j}^4}+\frac{1}{d_{1/2i,j}^4}+\frac{1}{d_{5/6i,j}^4})$

In formula, N is for threatening some sum, L _ithe length on i-th limit.

The fuel consume of unmanned plane is directly proportional to flight path length, therefore fuel penalty can be expressed as: J _{fuel, i}=L _i.In sum, the flight path cost on i-th limit is: J _i=k ₁j _{threat, i}+ (1-k ₁) J _{fuel, i}, wherein k ₁for weight coefficient, k ₁the larger expression of value is more paid attention to threat cost.

Then, ant group algorithm is utilized to be that every frame unmanned plane searches out many flight paths to be selected.The search volume of ant group algorithm is each summit of the Voronoi figure after construction complete.

Ant group algorithm is applied in Multiple routes planning problem, should note following some:

(1) determination of ant reference position.In general discrete optimization problems of device, ant is placed on arbitrary node place at random as reference position, and in trajectory planning problem, using the set of the Voronoi figure vertex set adjacent with unmanned plane starting point as ant reference position.

(2) determination of end condition.General discrete optimization problems of device requires that ant travels through all nodes, and gets back to starting point; And in trajectory planning problem, the end condition that every ant carries out route searching should be arrive destination node set, instead of fill up taboo list.

(3) organize the generation of solution more.Many alternative flight paths to being provided for every frame unmanned plane in this stage, therefore when using ant group algorithm, by selecting suitable parameter, getting caulocarpic union, to obtain several groups of different solutions.

Use N _cmaxrepresent the number of times often performing an ant group algorithm iteration, N _amaxrepresenting that every frame unmanned plane performs the number of times of ant group algorithm, is the node set that ant group algorithm provides to be searched by the Voronoi figure constructed.The concrete steps that ant group algorithm solves unit many flight paths problem are as follows:

A, N is set _a=1, each frame unmanned plane all performs a step b ~ g;

B, the parameter arranged in ant group algorithm;

C, m ant is placed in reference position set at random;

D, according to state transition probability formula select next node, once search for until all ants complete;

E, calculate the target function value of each ant, record current optimum solution, lastest imformation element;

F, as iterations N _c=N _c+ 1 reaches N _cmaxshi Zhihang next step, otherwise jump to step c;

G, export the result that this ant group algorithm obtains, N _a=N _a+ 1, work as N _areach N _amaxtime, current unmanned aerial vehicle flight path planning terminates, otherwise jumps to step b.

Algorithm flow chart as shown in Figure 1.

Finally, then work in coordination with time decision principle according to optimum and choose every frame unmanned plane and finally treat flight mark, and set the flying speed of each frame unmanned plane with this.

If the jth bar flight path length of the i-th frame unmanned plane is L _i,j, the flying speed of every frame unmanned plane can at [V _min, V _max] change in scope, then the time range arriving impact point along this flight path is T _i,j∈ [L _i,j/ V _max, L _i,j/ V _min], the time range of the i-th frame unmanned plane arrival impact point is the union of k bar flight path time, i.e. T _i=T _{i, 1}∪ T _{i, 2}∪ ... ∪ T _i,k.Collaborative function J is created as collaborative variable using the time arriving target _c, the collaborative function expression of the i-th frame unmanned plane jth bar flight path is,

J _c,i,j＝k ₂J _i,j+(1-k ₂)T _i,j

Wherein J _i,jthe flight path cost of the i-th frame unmanned plane jth bar flight path, k ₂for weight coefficient.After a flight path is determined, J _i,jbe a fixed value, J _{c, i, j}only T _i,jfunction, because multimachine will arrive simultaneously, therefore the setting of time of arrival should meet:

$T_a=T_1\∩T_2\∩...\∩T_{N_U}$

N _ufor the number of unmanned plane, therefore the collaborative function of a whole group of planes can be expressed as:

$J_c=\underset{i=1}{\overset{N_U}{\mathrm{\Σ}}}{J}_{c,i}\left(T_a\right)$

Fig. 2 indicates three frame unmanned planes, and every frame unmanned plane has the relation of collaborative function under three alternative flight paths and collaborative variable.Under the constraint meeting the collaborative time, require that the collaborative functional value of a whole group of planes is minimum, therefore determining the optimum collaborative time (ETA) is T _aminimum value in set, and the final flight path obtaining every frame unmanned plane.After setting ETA, just can obtain the flying speed of every frame unmanned plane, thus complete every planning of frame unmanned aerial vehicle flight path and the setting of flying speed.

The form into columns weight-normality of online flight path of step 2, the unmanned plane completed based on RRT algorithm is drawn.

RRT algorithm carries out the structure of random tree by the incremental mode of progressive alternate, first selected start node q in mission area _initas the root node of tree, build random tree by the mode constantly expanding leaf node from root node.First with Probability p _gselect target position q _goalas random targets point q _rand, or with probability 1-p _gstochastic choice random targets point q in task space _rand; Then chosen distance random targets point q in all leaf nodes of current random tree _randnearest leaf node, and be referred to as neighbor node q _near; Then from q _nearto q _randdirection extend the distance ε of a step-length, obtain a new node q _new.In extension process, judge whether to have with known threatening area to conflict, if Lothrus apterus accepts this new node q _new, and be added to the node of random tree; If q _newhave with threatening area and conflict, illustrate that the new node that this time expands does not meet safety requirements, then give up this new node, and re-start random targets point q _randchoose.By so continuous extension expansion, when the leaf node in random tree and target location are enough near time, then think that the structure work of random tree completes, be now initial with the leaf node that distance objective position is nearest, upwards search for father node successively, then can obtain a feasible path from reference position to target location.

Random node q is generated in the task environment of RRT path planning _randposition time adopt Chaos Variable, common chaotic maps has that Logistic maps, Henon maps, and wherein Logistic mapping table reveals good randomness and the generation of its chaos sequence is comparatively simple.Logistic maps and has succinct mathematical form, and its mathematical expression formula is as follows,

x _k+1＝μx _k(1-x _k)，0<x _k<1

Wherein x _kfor Chaos Variable, k=1,2 ... N is iterations, and μ is control variable.μ more close to 4 place, x _kspan close to be evenly distributed in whole 0 to 1 region, therefore the actual Logistic of choosing controling parameters should close to or equal 4.When μ=4 time, system is in chaos state completely, can realize x _ktraversal in [0,1] scope.

Utilize chaos sequence to produce random node, the randomness that random node is chosen can be ensured, ergodicity and the regularity of chaotic dynamics can be made full use of again, make the random node in random tree growth course cover whole task environment as far as possible.RRT algorithm is with 1-p _gprobability in task environment, select q _rand, q _randposition produced by two Logistic sequences of mapping.

When the pop-up threats that unmanned plane detects interferes with the pre-flight mark of self, start to carry out flight path weight-normality in the moment detecting pop-up threats and draw process, adopting RRT algorithm to walk around threat fast revert on pre-flight mark, notes avoiding known environmental threat simultaneously.

Step 3, complete unmanned plane form into columns concertedness rebuild.The delay free when walking around pop-up threats, the time coordination of pre-flight mark is destroyed, and needs to re-establish.

First, determine that concertedness rebuilds the moment, be defined as the moment that every frame unmanned plane all walks around threat, send signal to determine to other unmanned planes when when actual realization being and walking around threat by every frame unmanned plane, the overall framework that the flight path weight-normality shown in Fig. 3 is drawn indicates the moment of carrying out concertedness reconstruction.

Then, determine the residue pre-flight mark length of every frame unmanned plane, can the adjustment judging by depending merely on speed realize synergitic reconstruction and namely arrive destination simultaneously, if can not, need unmanned plane and carry out wait of spiraling, rebuild until concertedness can be realized by the adjustment depending merely on speed.

If each unmanned plane is the excess time arrived needed for target when carrying out concertedness and rebuilding:

$\left\{\begin{array}{c}{\mathrm{tr}}_i=[{\mathrm{tr}}_i\min ,{\mathrm{tr}}_i\max ]\\ {\mathrm{tr}}_i\min =L_{\mathrm{ri}}/V_{\max }\\ {\mathrm{tr}}_i\max =L_{\mathrm{ri}}/V_{\min }\end{array}\right.,i=\mathrm{1,2},...,N_U$

In formula, tr _ithat the i-th frame unmanned plane rebuilds in concertedness the time range that the moment arrives target, referred to as excess time, tr _imin is set tr _iin minimum value, namely this unmanned plane flies to the time of impact point according to maximal rate, in like manner, tr _imax is set tr _iin maximal value.L _rithat the i-th frame unmanned plane rebuilds the flight path length of moment distance objective point, referred to as residue length in concertedness.

As shown in Figure 4, the method wherein increasing the time of unmanned plane arrival target is concertedness reconstruction model process flow diagram: allow the i-th frame UAV be n away from threatening the track points of point to sentence fixing turning rate ω _risecondary circular motion, the time that each circle increases is

$\mathrm{\&Delta;t}=\frac{2\mathrm{\&pi;}}{\mathrm{\&omega;}}$

Wherein, ω is obtained by following formula

$\mathrm{\&omega;}=\frac{q\&times;n_{y\max }}{V_{\max }}$

In formula, n _ymaxit is maximum normal g-load.

Therefore concertedness reconstruction model output information comprises the number of turns of spiraling that concertedness rebuilds the unmanned plane that the flying speed in moment after moment, every frame unmanned plane and needs spiral.

In order to verify the validity of the present invention's routeing under dynamic environment, carry out following emulation experiment.Formation adopts formation diamond formation as shown in Figure 5, and arranges unmanned plane performance: V _min=150km/h, V _max=200km/h.According to step 1, in battlefield surroundings, construct Voronoi figure as shown in Figure 6, in figure, " " represents threat, and threat radius is 20m, being set to of simulation parameter: weight coefficient k ₁, k ₂all get 0.5, ant number m=10, N _cmax=50, N _a=10, α=1, β=1, ρ=0.7, Q=1, the every frame unmanned aerial vehicle flight path obtained is:

UAV1 starting point → 11 → 22 → 24 → 58 → 57 → 56 → 55 → 47 → 48 → 45 → UAV1 terminal

UAV2 starting point → 14 → 15 → 7 → 11 → 22 → 24 → 58 → 57 → 56 → 55 → 47

→ 48 → 45 → UAV2 terminal

UAV3 starting point → 7 → 11 → 10 → 51 → 49 → 50 → 33 → 34 → 39 → 35 → 37 → 31 → UAV3 terminal

UAV4 starting point → 7 → 11 → 22 → 24 → 58 → 57 → 56 → 55 → 47 → 48 → 45 → UAV4 terminal

The flight path length of the flight path length of UAV1 to be the flight path length of 1100.5m, UAV2 be 1132.7m, UAV3 is the flight path length of 1172.2m, UAV4 is 1124.5m, and the collaborative time is 21.1s, and the flight setting speed of each unmanned plane is: v ₁=52.2m/s, v ₂=53.7m/s, v ₃=55.6m/s, v ₄=53.3m/s.In step 2, suppose that UAV3 has met with pop-up threats, threat radius is 30m, and as shown in Figure 7, in emulation, the Selecting parameter of RRT algorithm is: p _g=0.5, ε=10m, the path re-planning locally flight path obtained as shown in Figure 8.Then carry out concertedness reconstruction according to step 3, UAV3 meets with pop-up threats in the t=9.3s moment, walks around pop-up threats and revert to pre-flight mark, therefore carry out concertedness reconstruction when t=11.1s, Lr in the t=11.1s moment ₁=521.1m, Lr ₂=541.1m, Lr ₃=656.2m, Lr ₄=532.9m, can obtain tr ₁=[9.4,12.5] s, tr ₂=[9.7,13.0] s, tr ₃=[11.8,15.7] s, tr ₄=[9.6,12.8] s, the collaborative time ETA=11.8s of residual paths.The speed of every frame UAV is reset to:

$v_{1\mathrm{replan}}=\frac{{\mathrm{Lr}}_1}{\mathrm{ETA}}=\frac{521.1m}{11.8s}=158.98\mathrm{km}/h,$

$v_{2\mathrm{replan}}=\frac{{\mathrm{Lr}}_2}{\mathrm{ETA}}=\frac{541.1m}{11.8s}=165.08\mathrm{km}/h,$

v _3replan＝V _max＝200km/h，

$v_{4\mathrm{replan}}=\frac{{\mathrm{Lr}}_4}{\mathrm{ETA}}=\frac{532.9m}{11.8s}=162.58\mathrm{km}/h,$

The overall coordination time is 11.1+11.8s=22.9s.

Be understandable that, for those of ordinary skills, can be equal to according to technical scheme of the present invention and inventive concept thereof and replace or change, and all these change or replace the protection domain that all should belong to the claim appended by the present invention.

Claims (5)

1. the collaborative flight path Intelligent planning method of UAV Formation Flight under dynamic environment, is characterized in that: realize according to following steps:

Step 1, adopts Voronoi figure to turn to a series of line segment by discrete for continuous print flight space; Then utilize ant group algorithm to be that each frame unmanned plane searches many flight paths to be selected, then work in coordination with time decision principle according to optimum and choose every frame unmanned plane and finally treat flight mark, and set the flying speed of each frame unmanned plane with this; Be the collaborative flight path during unmanned aerial vehicle design off-line flight of formation flight in advance;

Step 2, real-time detection also judges whether unmanned plane working direction exists barrier and pop-up threats, if existed, starts to carry out flight path weight-normality draw process in the moment detecting pop-up threats, adopts RRT algorithm to walk around threat fast and revert on pre-flight mark;

Step 3, for the unmanned aerial vehicle design walking around threat returns the air route of forming into columns, makes this unmanned plane and other unmanned planes rebuild concertedness state of flight, completes the concertedness reconstruction that unmanned plane is formed into columns.

2. the collaborative flight path Intelligent planning method of UAV Formation Flight under a kind of dynamic environment according to claim 1, is characterized in that: the concrete grammar of described step 3 is:

Step 3.1, first, determines that concertedness rebuilds the moment, is defined as the moment that every frame unmanned plane all walks around threat, send signal to determine when when actual realization being and walking around threat by every frame unmanned plane to other unmanned planes;

Step 3.2, then, determines the residue pre-flight mark length of every frame unmanned plane;

Step 3.3, can the adjustment judging by depending merely on speed realize synergitic reconstruction and namely arrive destination simultaneously, if can not, need unmanned plane and carry out wait of spiraling, rebuild until concertedness can be realized by the adjustment depending merely on speed.

3. the collaborative flight path Intelligent planning method of UAV Formation Flight under a kind of dynamic environment according to claim 1, is characterized in that: construct Voronoi figure in step 1 by the following method:

The one group of polygon be made up of the perpendicular bisector of the central point line segment in the adjacent threat source of connection two, chooses every frame unmanned plane in formation as a virtual threat point, and is suitably choosing several point as virtual threat point near threatening area boundary;

Utilize ant group algorithm to be the concrete grammar that each frame unmanned plane searches many flight paths to be selected in step 1 to be:

A. the determination of ant reference position, using the set of the Voronoi figure vertex set adjacent with unmanned plane starting point as ant reference position;

B. the determination of end condition, the end condition that every ant carries out route searching should be arrive destination node set;

C. the generation of many group solutions, by selecting suitable parameter, gets caulocarpic union, to obtain several groups of different solutions.

4. the collaborative flight path Intelligent planning method of UAV Formation Flight under a kind of dynamic environment according to claim 1, is characterized in that:

Employing RRT algorithm described in step 2 walks around the threat concrete grammar revert on pre-flight mark fast: first selected start node q in mission area _initas the root node of tree, build random tree by the mode constantly expanding leaf node from root node; First with Probability p _gselect target position q _goalas random targets point q _rand, or with probability 1-p _gstochastic choice random targets point q in task space _rand; Then chosen distance random targets point q in all leaf nodes of current random tree _randnearest leaf node, and be referred to as neighbor node q _near; Then from q _nearto q _randdirection extend the distance ε of a step-length, obtain a new node q _new.In extension process, judge whether to have with known threatening area to conflict, if Lothrus apterus accepts this new node q _new, and be added to the node of random tree; If q _newhave with threatening area and conflict, illustrate that the new node that this time expands does not meet safety requirements, then give up this new node, and re-start random targets point q _randchoose.

5. the collaborative flight path Intelligent planning method of UAV Formation Flight under a kind of dynamic environment according to claim 1, is characterized in that: complete the concertedness that unmanned plane forms into columns by the following method and rebuild:

If each unmanned plane is the excess time arrived needed for target when carrying out concertedness and rebuilding:

$\left\{\begin{array}{c}{\mathrm{tr}}_i=[{\mathrm{tr}}_i\min ,{\mathrm{tr}}_i\max ]\\ {\mathrm{tr}}_i\min =L_{\mathrm{ri}}/V_{\max }\\ {\mathrm{tr}}_i\max =L_{\mathrm{ri}}/V_{\min }\end{array}\right.,i=\mathrm{1,2},...,N_U$

In formula, tr _ithat the i-th frame unmanned plane rebuilds in concertedness the time range that the moment arrives target, referred to as excess time, tr _imin is set tr _iin minimum value, namely this unmanned plane flies to the time of impact point according to maximal rate, tr _imax is set tr _iin maximal value; L _rithat the i-th frame unmanned plane rebuilds the flight path length of moment distance objective point, referred to as residue length in concertedness;

The method wherein increasing the time of unmanned plane arrival target is: allow the i-th frame unmanned plane be n away from threatening the track points of point to sentence fixing turning rate ω _risecondary circular motion, the time that each circle increases is

$\mathrm{\&Delta;t}=\frac{2\mathrm{\&pi;}}{\mathrm{\&omega;}}$

Wherein, ω is obtained by following formula

$\mathrm{\&omega;}=\frac{g\&times;n_{y\max }}{V_{\max }}$

In formula, n _ymaxmaximum normal g-load,

Therefore concertedness reconstruction model output information comprises the number of turns of spiraling that concertedness rebuilds the unmanned plane that the flying speed in moment after moment, every frame unmanned plane and needs spiral.