CN101122974B - Un-manned plane fairway layout method based on Voronoi graph and ant colony optimization algorithm - Google Patents

Abstract

The invention provides an unmanned aircraft route planning method based on combination of Voronoi diagram and ant group optimizing algorithm. Firstly a model is established in accordance with properties of various threaten sources, e. g. landform, radar, missile and anticraft gun, and the unmanned aircraft route cost includes threaten costs and fuel oil costs; then initial pheromone values are given to each edge of the Voronoi diagram, enabling an ant to begin the search from the Voronoi node nearest to the start point. The marching Voronoi edge is selected based ob the status shift rule, then the search at the Voronoi node nearest to the target point is finished; And when all the ants have finished their own candidate route selection, the pheromones at each edge of the Voronoi diagram are updated in accordance with the improving and updating rules, wherein the pheromones at edges with no ant passing by are evaporated, and the process is repeated until an optimal unmanned aircraft route is found out. The method is characterized by good real-time and high-speed performance, and the found route is closer to the actual optimal unmanned aircraft route.

Description

Path Planning for Unmanned Aircraft Vehicle method based on Voronoi figure and ant colony optimization algorithm

(1) technical field

Unmanned plane (Unmanned Aerial Vehicle) is a kind of dynamic, may command, can carry multiple-task equipment, carry out multiple combat duty and can reusable Unmanned Tactical Aircraft.Because advantages such as its zero injures and deaths risk and high maneuverability have caused the great attention of the various countries militaries.And routeing (Path Planning) is as the key components of unmanned plane mission planning system, its target is to calculate, select the flight air route of optimum or suboptimum in reasonable time, to the operating resources of reasonable distribution unmanned plane, realize that the maximum fighting efficiency of unmanned plane plays crucial effects.At present, the research at the routeing technical elements both at home and abroad just further develops to intellectuality, real-time, realizability direction, but also is in the preliminary research stage basically.Ant group optimization (Ant Colony Optimization) algorithm is the bionical optimized Algorithm of ant colony foraging behavior in a kind of simulation Bugdom of recent development, this algorithm has adopted the positive feedback self-catalysis mechanism that walks abreast, have stronger robustness, good Distributed Calculation mechanism, be easy to and advantages such as additive method combines, showing its excellent performance and huge development potentiality aspect many complicated optimum problem solving.

The present invention is directed to the characteristics of Path Planning for Unmanned Aircraft Vehicle, solved the Path Planning for Unmanned Aircraft Vehicle problem based on Voronoi figure and improvement ant colony optimization algorithm.Compare with traditional routeing method, the method that this invention proposed has good real-time performance and rapidity, and the optimum air route of actual unmanned plane is more approached in the air route that is searched.This method is the effective technical way that solves Path Planning for Unmanned Aircraft Vehicle under the complicated dynamic environment, and simultaneously, the present invention also can be applicable to technical fields such as the robot path planning, urban transportation vehicle path planning under the complex environment.

(2) background technology

At present, Chang Yong Path Planning for Unmanned Aircraft Vehicle algorithm has feasibility direction algorithm, A* algorithm, genetic algorithm etc.

Feasibility direction algorithm is developed by gradient method, and is the same with general gradient search optimizing method, by continuous change controlled quentity controlled variable, up to the performance index optimum.Its distinctive feature is that it finds the best of control variable to allow direction earlier, and then changes controlled quentity controlled variable in the direction.Therefore, this method can also satisfy constraint condition when the adjusting control variable diminishes performance index.The limitation of feasibility direction algorithm is that speed of convergence is slow, and may obtain a locally optimal solution but not globally optimal solution.All method speed of convergence based on gradient method all have bigger uncertainty, and it is bigger that its speed of convergence is influenced by terrain profile.

Genetic algorithm provides a kind of general framework of finding the solution complicated problem, and it is less demanding to the detail of problem, and the class of problem is had very strong robustness.Yet genetic algorithm is used in may be more time-consuming comparatively speaking in the routeing, generally is not suitable for being used for planning in real time, but present counting system is serial, and genetic algorithm has implicit concurrency, and this makes it that very big development potentiality be arranged.The shortcoming of this algorithm is: gene and controlled variable are difficult to select, and algorithm also premature convergence and stagnation behavior may occur sometimes.

The A* algorithm reaches the purpose that reduces the hunting zone, improves computing velocity by the heuristic information guiding search.Heuristic information is generally got from the search volume certain intermediate node to the estimated value of the optimum cost of destination node.Utilize this heuristic information guiding search can produce optimum solution.Therefore heuristic information choose most importantly, if too simple, then the intermediate node number of Zhan Kaiing can increase: if too complicated, then calculate the time of estimated value cost and also increase, so the consideration of should trading off.Heuristic function will obtain by the method that examination is gathered preferably at present, makes algorithm application be very restricted.The shortcoming of A* algorithm maximum is that the search volume demand is too big, and computing time is long.

Therefore, these methods all fail effectively to solve routeing problem of unmanned plane at present from practical significance.

Ant colony optimization algorithm is a kind of emerging heuristic bionic intelligence optimized Algorithm, people have been penetrated into a plurality of applications by single originally traveling salesman problem (Traveling SalesmanProblem) field to the research of ant colony optimization algorithm at present, develop into solution multidimensional dynamic combined optimization problem by solving one dimension static optimization problem, be extended to research in the continuous domain scope gradually by the research in the discrete domain scope, and in the hardware realization of ant colony optimization algorithm, also obtained a lot of breakthroughs, thereby make this emerging bionical optimized Algorithm show vitality and vast potential for future development.

Be different from other bionic intelligence algorithm, ant colony optimization algorithm has adopted positive feedback mechanism, characteristics such as the concurrency that is embodied in the ant group searching process, concertedness, self-organization, dynamic, strong robustness conform to many requirements of complicated battlefield surroundings, so ant colony optimization algorithm can be used for solving the self-adaptation routeing problem of unmanned plane.But basic ant colony optimization algorithm exist search time long, sink into shortcoming such as locally optimal solution easily, therefore must make improvements when ant colony optimization algorithm solves the Path Planning for Unmanned Aircraft Vehicle problem using.Based on this, the present invention proposes a kind of improved ant colony optimization algorithm model, and be successfully applied to the Path Planning for Unmanned Aircraft Vehicle problem that solves under the complicated dynamic environment in conjunction with the ant colony optimization algorithm of Voronoi figure after with this improvement.

(3) summary of the invention

A kind of Path Planning for Unmanned Aircraft Vehicle method of the present invention based on Voronoi figure and ant colony optimization algorithm, it has proposed a kind of improved ant colony optimization algorithm model, and is successfully applied to the Path Planning for Unmanned Aircraft Vehicle problem that solves under the complicated dynamic environment in conjunction with the ant colony optimization algorithm of Voronoi figure after with this improvement

Occurring in nature, this class social animal of picture ant, ability of single ant and intelligence are very simple, no matter but they nest, look for food, migrate, clean complex behaviors such as ant cave by what mutual coordination, the division of labor, cooperation finished that worker ant or queen all can not have enough abilities to command to finish.The food source of ant always random scatter can find as long as we just examine that around ant nest after after a while, ant can be found a shortest path from the ant nest to the food source.Scientist once studied ant group's foraging behavior by " doube bridge experiment ".Discovery is except finding the shortest path between nest and the food source, and the ant group has extremely strong adaptive faculty to environment.For example when original shortest path became infeasible owing to the appearance of a new barrier, the ant group energy found a new shortest path rapidly.

In actual life, we always can observe a large amount of ants and form the path that is close to straight line between nest and food source, rather than curve or circle wait other shape, shown in Fig. 1 (a).Ant colony can not only be finished complicated task, and the variation that can also conform, when barrier occurring suddenly on ant group moving line, each ant distribution is uniform at the beginning, canal path length whether not, ant is always earlier by select each paths with equiprobability, shown in Fig. 1 (b).Ant can stay pheromones on the path of its process in motion process, and the existence and the intensity thereof of this material of energy perception, and instructs own travel direction with this, and ant tends to the high direction of pheromone concentration and moves.Just leave over often than the quantity of information on the short path in equal time, then select also to increase, thereupon shown in Fig. 1 (c) than the ant of short path.Be not difficult to find out, because the ant cluster behavior that a large amount of ants are formed has shown a kind of information positive feedback phenomenon, be that the ant of passing by on a certain path is many more, then the late comer selects the probability in this path just big more, search for food by this information interchange mechanism exactly between the ant individuality, and finally advance along shortest path, shown in Fig. 1 (d).

How does the ant group finish these complex tasks? the bionicist passes through a large amount of observations, discovers, ant is when seeking food, can on the path of its process, discharge the distinctive pheromones of a kind of ant, make other ants in the certain limit can feel this material, and tend to move towards the high direction of this material intensity.Therefore, ant group's collective behavior shows as a kind of information positive feedback phenomenon: the ant number of process is many more on certain paths, the pheromones that stays on it is also just many more (certainly, passing meeting is in time evaporated gradually), ant selected the probability in this path also high more afterwards, thereby had more increased the intensity of pheromones on this path.

Ant colony optimization algorithm is a kind of new bionic intelligence computation schema, and unmanned plane self-adaptation routeing can utilize the following characteristics of ant colony optimization algorithm cleverly:

(1) constantly scatter under the booster action of biological information hormone ant, new information can be added in the environment very soon.And because the evaporation of biological information hormone is upgraded, old information can constantly be lost, and embodies a kind of dynamic perfromance;

(2) self also distribute the biological information hormone simultaneously because many ants experience the biological information hormone that scatters in environment, this makes different ants have different selection strategies, has distributivity;

(3) optimal route is to obtain by the cooperation of numerous ants is searched, and becomes the selected route of most of ants, and this process has concertedness;

(4) between the ant individuality, between the colony and with environment between interaction, influence each other, cooperate mutually, the task of the complexity that can finish, this adaptability shows as the robustness of ant colony optimization algorithm;

(5) self-organization makes the behavioural trend structuring of ant colony, and its reason has been to comprise the process of a positive feedback.This process has utilized global information as feedback, and positive feedback makes the more excellent self-strengthening of separating in the phylogeny process, makes separating towards the direction of global optimization of problem constantly change, and finally can obtain more excellent relatively separating effectively.

Thus, ant colony optimization algorithm is actually class intelligence multiagent system, and its self-organization mechanism makes that ant colony optimization algorithm does not need all there is detailed understanding each aspect of asking problem.Self-organization is an ant colony optimization algorithm mechanism at the dynamic process that does not have system's entropy is increased in essence, has embodied the dynamic evolution from disorder to order.Characteristics such as the concurrency that is embodied in the ant colony optimization algorithm searching process, concertedness, self-organization, dynamic, strong robustness conform to many requirements of complicated battlefield surroundings.

Voronoi figure is a geometric figure important in the computational geometry, is widely applied to the occasion of multiple area dividing such as landform processing, has successfully solved and looked for closest approach, asked problems such as maximum empty circle, minimum spanning tree.The maximum characteristics that Voronoi figure is applied in the routeing are to threaten to generate under the source distribution situation by initial feasible path collection according to known battlefield to constitute ground Voronoi figure, wherein the Voronoi limit is the perpendicular bisector in discrete threat source, can guarantee that like this unmanned plane reduces the threat cost effectively in flight course.

Definition (Voronoi figure): the Euclidean distance between any 2 p and the q, (p q), establishes P={p to be designated as dist ₁, p ₂..., p _nBe the point of any n inequality on the plane, and the Voronoi figure of P correspondence is that a sub regions on plane is divided, and therefore whole plane is divided n unit, and they have following character: any 1 q is positioned at a p _iIn the pairing unit, and if only if for any P _i∈ P, j ≠ i has dist (q, P _i)＜dist (q, P _j).

The basic ideas of the planning environment being expressed by Voronoi figure are: will threaten the point of center as Voronoi figure, measure as " distance " of Voronoi figure adjacent domain to threaten size, make up the Voronoi figure that threatens configuration, " distance " big more then institute is compromised more little, each bar limit of Voronoi figure distance in the field of respective point threatens " distance " maximum, thereby compromised corresponding minimum.

A kind of Path Planning for Unmanned Aircraft Vehicle method of the present invention based on Voronoi figure and ant colony optimization algorithm, this method is specific as follows:

(1) unmanned plane based on Voronoi figure threatens the source modeling

Voronoi figure is a kind of the expression a little or the geometry of entity sets approximate information.A given point or entity sets, the plane just can be divided into the protruding net apart from each point or entity minimum distance, and this protruding net promptly is called Voronoi figure.Can be effectively the point in the geography information, object and zone be showed with topological structure by Voronoi figure, and can represent qualitative relationships and fuzzy geography information in the natural language, thereby this qualitative relationships is measured by these topological relations.Fig. 2 has provided certain unmanned plane and has threatened the source distribution situation map.

For constituting leg-of-mutton three threat sources, all there is a unique circumscribed circle, the triangle that claims those circumscribed circles not comprise any other threat source is the Delaunay triangle, and the circumcenter is called the Voronoi point, the Voronoi point is coupled together just constituted Voronoi figure.The point that Voronoi schemes on each bar limit is equidistant to corresponding two threat sources.From these characteristics, Voronoi figure threatens the perpendicular bisector in source to constitute by per two, point in the polygon that the Voronoi limit constitutes threatens the distance in source littler to the distance in threat source than the point outside polygon to it, be that point on the limit of Voronoi figure is to all radars point farthest, so unmanned plane is safest beyond doubt along the words of the limit flight of Voronoi figure.

(2) threaten the source modeling

Threatening modeling is the synthtic price index of a complexity, and it changes along with the variation of the kind, feature and the aerial mission that threaten.The flight path that the trajectory planning system requirements of unmanned plane obtains can effectively be avoided the detection of enemy radar and the attack that the enemy threatens, and requirement avoids influencing the strategically located and difficult of access landform of flight, unfavorable factor such as weather extremes and artificial barrier is to guarantee the maximum survivability of unmanned plane.Suppose that unmanned plane keeps highly constant in the process of executing the task, speed is constant, and consider that the enemy defence area is in smooth region, unmanned plane just can't utilize orographic factor to impend to avoid motor-driven so, and then the routeing problem just can turn to two dimension and plan (horizontal path just) problem.The present invention mainly considers to threaten from landform, radar, guided missile and antiaircraft gun, and carries out modeling according to the concrete feature in full spectrum of threats source.

＜1〉landform threatens

Landform threatens and mainly is meant the towering mountain peak that may cause obstacle on fixed altitudes to unmanned plane during flying.With cone approximate representation mountain peak, when regularly the go up a hill horizontal section at peak of the flying height one of aircraft is a circumference, mountain peak radius and aircraft are respectively d apart from the distance at center, mountain peak _TAnd d, smash probability P _T(d) but approximate representation be:

$P_T\left(d\right)=\left\{\begin{array}{ccc}0,& \mathrm{if}& d>10+{\mathrm{<figure-callout\; id="1"\; label="d\; T"\; filenames="H2007101217773E00022.png"\; state="\{\{state\}\}">d</figure-callout>}}_T\\ 1/d,& \mathrm{if}& 2+d_T\&le;d\&le;10+{\mathrm{<figure-callout\; id="1"\; label="d\; T"\; filenames="H2007101217773E00022.png"\; state="\{\{state\}\}">d</figure-callout>}}_T\\ 1,& \mathrm{if}& d<2+d_T\end{array}\right.---\left(1\right)$

＜2〉threat radar

When threatening, the threat of unmanned plane and biquadratic to the distance of radar are inversely proportional to radar.If radar maximum probe radius is d _Rmax, aircraft is d apart from the horizontal range of radar, then unmanned plane is by enemy radar detection probability P _R(d) but approximate representation be:

$P_R\left(d\right)=\left\{\begin{array}{ccc}0& \mathrm{if}& d>d_{R\max }\\ d_{R\max }^4/(d^4+d_{R\max }^4)& \mathrm{if}& d\&le;d_{R\max }\end{array}\right.---\left(2\right)$

＜3〉missile threat

Usually antiaircraft missile is main ground air defense weapon, and according to the killing area characteristics of guided missile, its killing area can be approximately dolioform as can be known, and level cross-sectionn radius of a circle d is the function of height, and has maximum radius on a certain height.P _M(d) represent the probability that aircraft is hit by guided missile.If d _MmaxBe the maximum radius of guided missile damage volume, kill probability P then _M(d) but approximate representation be:

$P_M\left(d\right)=\left\{\begin{array}{ccc}0& \mathrm{if}& d>d_{M\max }\\ d_{M\max }^4/(d^4+d_{M\max }^4)& \mathrm{if}& d\&le;d_{M\max }\end{array}\right.---\left(3\right)$

＜4〉antiaircraft gun threatens

The modeling method that antiaircraft gun threatens is similar to missile threat.P _C(d) represent the probability that aircraft is hit by antiaircraft gun.If d _CmaxBe the maximum radius of enemy's antiaircraft gun damage volume, but then the kill probability approximate representation is:

$P_C\left(d\right)=\left\{\begin{array}{ccc}0& \mathrm{if}& d>d_{C\max }\\ d_{C\max }/(d+d_{C\max })& \mathrm{if}& d\&le;d_{C\max }\end{array}\right.---\left(4\right)$

(3) calculate based on the air route cost of Voronoi figure

Unmanned aerial vehicle flight path planning is the flight path that constraint condition is satisfied in planning according to task object.Constraint condition comprises that unmanned plane finishes the security performance and the fuel performance of assignment of mission.So the air route cost of unmanned plane comprises its suffered threat cost and fuel oil cost.When single unmanned plane during along each the bar limit flight of Voronoi figure, all will have certain cost, the threat cost of remembering i bar limit is J _f ⁱ, the fuel oil cost is J _t ⁱ, total cost J on i bar limit then _iBe designated as:

$J_i=k{J^i}_f+(1-k)J_t^i---\left(5\right)$

Wherein, k is the weight coefficient of security performance and fuel performance, k ∈ [0,1], and its value can be decided according to the performed task of unmanned plane, if the security when task is paid attention to flight, then k selects less value; If task needs the rapidity of aircraft, then k selects bigger value.In a word, the size of weighting depends on the importance of claim and the overall target of feasibility.

Threat such as radar, guided missile source has constituted the deterrent of closing on the Voronoi limit with it, with limit i is example, its threat cost is the threat integration along limit i, can be reduced to the threat cost sum that limit i goes up M discrete point, and then threatening cost is that M discrete point is the function of parameter to N distance that threatens.

$J_t^i=L_i\underset{n=1}{\overset{N}{\mathrm{\&Sigma;}}}\underset{m=1}{\overset{M}{\mathrm{\&Sigma;}}}g\left(d_{m,n}\right)---\left(6\right)$

Wherein,

$g\left(d_{m,n}\right)=\left\{\begin{array}{c}{k}_T\&CenterDot;P_T\left(d_{m,n}\right)\\ k_R\&CenterDot;P_R\left(d_{m,n}\right)\\ k_M\&CenterDot;P_M\left(d_{m,n}\right)\\ k_C\&CenterDot;P_C\left(d_{m,n}\right)\end{array}\right.---\left(7\right)$

k _T, k _R, k _M, k _C, k _WWith k _fBe weight coefficient, reflection respectively threatens the relative significance level in source.Specifically as shown in Figure 3.

Suppose the constant airspeed of unmanned plane, so the fuel oil that unmanned plane during flying consumed just is directly proportional with the length in flight air route, flies over the fuel oil that i bar limit consumes and be in the section of cruising:

J ⁱ _f＝L _i (8)

(5) based on Voronoi figure with improve the routeing of ant colony optimization algorithm

At first, Voronoi is schemed each limit provide the plain value of initial information, making ant begin search from the nearest Voronoi node of distance starting point, according to the Voronoi limit that the selection of state transitions rule is advanced, is that terminal point finishes search with the nearest Voronoi node of distance objective point.When all ants finish each after the candidate air route is selected, according to the pheromones update rule pheromones on each limit among the Voronoi figure is upgraded, wherein do not have the limit of ant process to carry out the pheromones evaporation, repeat this process until reaching termination condition.

When the ant k that goes out as node a selected node b, its state transition probability was:

$P_{k(a,b)}\left(t\right)=\left\{\begin{array}{ccc}\frac{{\mathrm{\&tau;}}_{\mathrm{ab}}^{\mathrm{\&alpha;}}\left(t\right){\mathrm{\&eta;}}_{\mathrm{ab}}^{\mathrm{\&beta;}}\left(t\right)}{\underset{b\&Element;\mathrm{allowed}\left(a\right)}{\mathrm{\&Sigma;}}{\mathrm{\&tau;}}_{\mathrm{sb}}^{\mathrm{\&alpha;}}\left(t\right){\mathrm{\&eta;}}_{\mathrm{ab}}^{\mathrm{\&beta;}}\left(t\right)}& \mathrm{if}& b\&Element;\mathrm{allowed}\left(a\right)\\ 0& \mathrm{otherwise}& \end{array}\right.---\left(9\right)$

Wherein, τ _Ab(t) the pheromones value on the ab of limit among the expression Voronoi figure, η _Ab(t) expression node a is with respect to the observability of node b, η _Ab(t)=1/J _{A, b}, J _{A, b}It is total cost of limit ab; Allowed (a) is all feasible node set that k ant can be arrived by node a; α is the heuristic factor of information, and the relative importance of expression track has reflected information role when ant moves that ant is accumulated in motion process; β is the heuristic factor of expectation, and the relative importance of expression visibility has reflected that ant heuristic information in motion process selects the attention degree of being subjected in the path ant.

In case all ants have been finished the selection course in candidate air route separately, need upgrade the pheromones value on each limit, update rule is as follows:

τ(a，b)＝(1-ρ)τ(a，b)+ρΔτ(a，b)(10)

$\mathrm{\&Delta;\&tau;}(a,b)=\underset{k=1}{\overset{\mathrm{num}}{\mathrm{\&Sigma;}}}{\mathrm{\&Delta;\&tau;}}_k(a,b)---\left(11\right)$

Wherein, num is the quantity of ant, and ρ represents the pheromones volatility coefficient, and then 1-ρ represents the residual factor of pheromones, and the span of ρ is:

Δ τ _k(a, b) expression ant k through limit ab after the pheromones increment, its value is provided by following formula:

${\mathrm{\&Delta;\&tau;}}_k(a,b)=\left\{\begin{array}{cc}Q/J_{k\min }& \mathrm{if}(a,b)\&Element;\mathrm{path}\\ 0& \mathrm{otherwise}\end{array}\right.---\left(12\right)$

Q represents pheromones intensity in the formula, and the Q value is a constant; J _KminThe minimum cost of expression ant k in this circulation.

In sum, a kind of Path Planning for Unmanned Aircraft Vehicle method of the present invention, the flow process of this method based on Voronoi figure and ant colony optimization algorithm as shown in Figure 4, its specific implementation step is as follows:

The first step: scheme according to threatening source distribution to construct Voronoi, and calculate total cost on every limit among the Voronoi figure; Parameter initialization, Voronoi figure composes on every limit the plain value of initial information;

Second step: all ants are placed the Voronoi node of graph nearest apart from starting point, and select next node, finish search procedure until all ants according to formula (9);

The 3rd step: calculate the cost of feasible path according to formula (5), and upgrade the optimal path that is found;

The 4th step: upgrade the plain value of all Voronoi side informations with reference to optimal path in the current circulation, rule is suc as formula shown in (10)～(12);

The 5th step: if satisfy the loop ends condition, then loop ends and written-out program result of calculation go on foot otherwise jump to second.

The present invention is a kind of based on satisfactory decision-making ant colony intelligence unmanned plane self-adaptation routeing method, its advantage and the effect of being reached are: Voronoi figure is combined with ant colony optimization algorithm can effectively solve the problem of Path Planning for Unmanned Aircraft Vehicle, and having good real-time performance and rapidity, the optimum air route of actual unmanned plane is more approached in the air route that is searched.

This method is the effective technical way that solves Path Planning for Unmanned Aircraft Vehicle under the complicated dynamic environment, and simultaneously, the present invention also can be applicable to technical fields such as the robot path planning, urban transportation vehicle path planning under the complex environment.

(4) description of drawings

The ant group seeks the process of food in Fig. 1 reality

Fig. 2 threatens source distribution (threatening the center, source with an expression)

The threat cost of Fig. 3 Voronoi figure limit i is calculated

Fig. 4 Path Planning for Unmanned Aircraft Vehicle flow process of the present invention

The unmanned plane air route that Fig. 5 generates with the improvement ant colony optimization algorithm in Voronoi figure

Fig. 6 increases the unmanned plane air route that generates behind the threat source

Number in the figure and symbol description are as follows:

D (1, n)---the 1st discrete point is to n distance that threatens

D (1, n+1)---the 1st discrete point is to n+1 distance that threatens

(5) embodiment

Improve ant colony optimization algorithm carries out routeing in Voronoi figure feasibility in order to verify, the present invention is a kind of based on satisfactory decision-making ant colony intelligence unmanned plane self-adaptation routeing method, utilize unmanned plane threatening environment shown in Figure 2 to test, its specific implementation step is as follows:

The first step: scheme according to threatening source distribution to construct Voronoi, and calculate total cost on every limit among the Voronoi figure; Parameter initialization: num=30, α=2, β=5, ρ=0.1, Q=100, k=0.6.Voronoi figure composes on every limit the plain value of initial information;

Second step: all ants are placed the Voronoi node of graph nearest apart from starting point, and select next node, finish search procedure until all ants according to formula (9):

$P_{k(a,b)}\left(t\right)=\left\{\begin{array}{ccc}\frac{{\mathrm{\&tau;}}_{\mathrm{ab}}^2\left(t\right){\mathrm{\&eta;}}_{\mathrm{ab}}^5\left(t\right)}{\underset{b\&Element;\mathrm{allowed}\left(a\right)}{\mathrm{\&Sigma;}}{\mathrm{\&tau;}}_{\mathrm{sb}}^2\left(t\right){\mathrm{\&eta;}}_{\mathrm{ab}}^5\left(t\right)}& \mathrm{if}& b\&Element;\mathrm{allowed}\left(a\right)\\ 0& \mathrm{otherwise}& \end{array}\right.$

The 3rd step: calculate the cost of feasible path according to formula (5), and upgrade the optimal path that is found:

$J_i=0.6{J^i}_f+0.4J_t^i$

The 4th step: upgrade the plain value of all Voronoi side informations with reference to optimal path in the current circulation, rule is suc as formula shown in (10)～(12):

τ(a，b)＝0.9τ(a，b)+0.1Δτ(a，b)

$\mathrm{\&Delta;\&tau;}(a,b)=\underset{k=1}{\overset{30}{\mathrm{\&Sigma;}}}{\mathrm{\&Delta;\&tau;}}_k(a,b)$

${\mathrm{\&Delta;\&tau;}}_k(a,b)=\left\{\begin{array}{cc}100/J_{k\min }& \mathrm{if}(a,b)\&Element;\mathrm{path}\\ 0& \mathrm{otherwise}\end{array}\right.$

The 5th step: if satisfy the loop ends condition, then loop ends and written-out program result of calculation go on foot otherwise jump to second.

Fig. 5 represents that unmanned plane enters the synoptic diagram that threatening area is executed the task, and on behalf of threat source, " ★ " such as radar, guided missile, its mid point represent unmanned plane starting point, " ▲ " expression task object point, and feasible air route is represented with solid line in Fig. 5.

Threaten the source negligible amounts among Fig. 6,, plan once more, obtain simulation result at last shown in the solid line among Fig. 6 when the quantity that increases the threat source.

Claims (1)

1. Path Planning for Unmanned Aircraft Vehicle method based on Voronoi figure and ant colony optimization algorithm, it is characterized in that: the concrete steps of this method are as follows:

The first step: scheme according to threatening source distribution to construct Voronoi, and calculate total cost on every limit among the Voronoi figure; Parameter initialization is for composing the plain value of initial information in every limit of Voronoi figure;

Second step: all ants are placed the Voronoi node of graph nearest apart from starting point, and according to formula

$P_{k(a,b)}\left(t\right)=\left\{\begin{array}{cc}\frac{{\mathrm{\&tau;}}_{\mathrm{ab}}^{\mathrm{\&alpha;}}\left(t\right){\mathrm{\&eta;}}_{\mathrm{ab}}^{\mathrm{\&beta;}}\left(t\right)}{\underset{b\&Element;\mathrm{allowed}\left(a\right)}{\mathrm{\&Sigma;}}{\mathrm{\&tau;}}_{\mathrm{ab}}^{\mathrm{\&alpha;}}\left(t\right){\mathrm{\&eta;}}_{\mathrm{ab}}^{\mathrm{\&beta;}}\left(t\right)}& \mathrm{if\; b}\&Element;\mathrm{allowed}\left(a\right)\\ 0& \mathrm{otherwise}\end{array}\right.$

Select next node, finish search procedure until all ants;

Wherein, τ _Ab(t) the pheromones value on the ab of limit among the expression Voronoi figure; η _Ab(t) expression node a is with respect to the observability of node b, η _Ab(t)=1/J _{A, b}, J _{A, b}It is total cost of limit ab; Allowed (a) is all feasible node set that k ant arrived by node a; α is the heuristic factor of information, and the relative importance of expression track has reflected information role when ant moves that ant is accumulated in motion process; β is the heuristic factor of expectation, and the relative importance of expression visibility has reflected that ant heuristic information in motion process selects the attention degree of being subjected in the path ant;

The 3rd step: according to formula

$J_i={\mathrm{kJ}}_f^i+(1-k)J_t^i$

Calculate the cost of feasible path, and upgrade the optimal path that is found;

Wherein, k is the weight coefficient of security performance and fuel performance, k ∈ [0,1], and its value is decided according to the performed task of unmanned plane, if the security when task is paid attention to flight, then k selects less value; If task needs the rapidity of aircraft, then k selects bigger value; In a word, the size of weighting depends on the importance of claim and the overall target of feasibility;

The 4th step: upgrade the plain value of all Voronoi side informations with reference to optimal path in the current circulation, rule is shown in following formula;

τ(a，b)＝(1-ρ)τ(a，b)+ρΔτ(a，b)

$\mathrm{\&Delta;\&tau;}(a,b)=\underset{k=1}{\overset{\mathrm{num}}{\mathrm{\&Sigma;}}}\mathrm{\&Delta;}{\mathrm{\&tau;}}_k(a,b)$

Wherein, num is the quantity of ant, and ρ represents the pheromones volatility coefficient, and then 1-ρ represents the residual factor of pheromones, and the span of ρ is: Δ τ _k(a, b) expression ant k through limit ab after the pheromones increment, its value is provided by following formula:

$\mathrm{\&Delta;}{\mathrm{\&tau;}}_k(a,b)=\left\{\begin{array}{cc}Q/J_{k\min }& \mathrm{if}(a,b)\&Element;\mathrm{path}\\ 0& \mathrm{otherwise}\end{array}\right.;$

Wherein, Q represents pheromones intensity, and the Q value is a constant; J _KminThe minimum cost of expression ant k in this circulation;

The 5th step: if satisfy the loop ends condition, then loop ends and written-out program result of calculation go on foot otherwise jump to second.

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