CN104851121A - Winged insect group simulation method based on biological rule - Google Patents

Abstract

The present invention discloses a winged insect group simulation method based on the biological rule, which is characterized by comprising the following steps of (1) continuously acquiring the interaction force, the frictional force and the inherent noise force for each of all winged insect individuals in a winged insect group; (2) calculating the desired velocity of each winged insect individual at each moment according to the interaction force, the frictional force and the inherent noise; (3) acquiring the target velocity of each winged insect individual at each moment through correcting the desired velocity; (4) updating the latest space position of each of all the winged insect individuals by frames to simulate the winged insect group. According to the technical scheme of the invention, the winged insect group is simulated based on the method of the biological rule, and the movement locus of each winged insect individual in the simulated winged insect group is very similar to that of a real winged insect. Therefore, the method is persuasive and realistic, and can efficiently and realistically simulates the common behaviors of a winged insect group in the natural world.

Description

A kind of winged insect group analogy method based on biology rule

Technical field

The present invention relates to cluster of cartoons technical field, particularly relate to a kind of winged insect group analogy method based on biology rule.

Background technology

Along with the development of three-dimensional software technology and the raising of hardware computation ability, cluster of cartoons, as an emerging technical field, is more and more paid close attention to by people.In entertainment industry, cluster of cartoons technology can simulate thousands of role, while reduction shooting cost, reproduced grand scene truly.

Cluster of cartoons technology obtains development and perfect under the promotion of digital entertainment demand.The motion simulation of colony can show synchronism and the heterogeneity of group movement, but needs again to process considerable parameter in order to the Different Individual shown in colony.In order to build visual vivid effect, motion control is the part of most critical in cluster of cartoons simulation, namely how to define individual behavior, makes individuality possess independently kinetic characteristic and collision and avoid, and forms again natural group movement simultaneously.

Publication number is the fuzzy control method that the Chinese patent literature of CN 100580708C discloses a kind of generating group animation, the method comprises: user inputs the overall controling parameters of the individual amount of group, individual attribute and group, generates the cluster of cartoons required by user in real time; Individuality in group moves in virtual scene under the effect of vector field force, comprehensive field force, directed force and prevention power again, according to the distance of the current position of group individual with individual target location, the ratio shared by weight of vector field force, comprehensive field force, directed force and prevention power is adjusted by design weighting curve, realize the fuzzy control process of group, ensure that group steadily, naturally moves towards target location.

Insect is the biological group of occurring in nature most species, in computer graphics, animation and field of virtual reality.

Application publication number be CN103236072A patent document discloses a kind of winged insect group analogy method based on noise perception, comprising: (1) user's designated noise controling parameters, generate Target noise field and curl field thereof; (2) the given spatial information of user and mesh parameter, generates least consume field C; (3) the birth region of user given winged insect number, winged insect group and the basic flight speed s0 of winged insect, use and C continue to obtain the individual velocity amplitude in each moment of winged insect, these velocity amplitudes are utilized to upgrade the up-to-date locus of all winged insects frame by frame, simulation winged insect group.This invention generates by utilizing Perlin noise the noise behavior controlling winged insect group without loose noise field, and utilizes optimal path algorithm span least consume field to the flight path of the winged insect group that navigates.

But the method kind utilizing winged insect to simulate at present is not enriched, therefore, how efficient and winged insect colony realistically in simulated three-dimensional space be an important research topic.

Summary of the invention

The invention provides a kind of winged insect group analogy method based on biology rule, continue to calculate its acting force be subject in colony for every winged insect individuality, utilize this acting force to calculate after the individual desired speed value in each moment and collision avoidance algorithms correct desired speed value and obtain target speed value, recycling target speed value upgrades the up-to-date locus of all winged insects frame by frame, realize winged insect group simulation, high with the movement locus similarity of true winged insect, there is cogency and the sense of reality.

Based on a winged insect group analogy method for biology rule, comprise the steps:

(1) continue to obtain individual interaction force, friction force and the intrinsic noise power be subject in winged insect group of each winged insect;

(2) according to the interaction force obtained in step (1), friction force and intrinsic noise power, the individual desired speed value in each moment of each winged insect is calculated;

(3) collision avoidance algorithms utilized corrects the desired speed value calculated in step (2), obtains the individual target speed value in each moment of each winged insect;

(4) according to the target speed value obtained in step (4), the up-to-date locus of all winged insects is upgraded frame by frame, simulation winged insect group.

The present invention is by combining interaction force, friction force and intrinsic noise power, and design can simulate group's algorithm of the common behavior of winged insect group major part, thus can the common behavior of efficiently and realistically simulating nature Jie Zhong winged insect colony.

The compound eye structural of winged insect makes them can barrier or the individual impact on self of other winged insects in promptly perception surrounding environment, thus adjust oneself flying speed direction and velocity magnitude at short notice, avoid colliding, see list of references 1 with barrier or other individual generations;

List of references 1:STUMM-TEGETHOFF B., DICKE A.:Surface structure ofthe compound eye of various drosophila species and eye mutants of drosophilamelanogaster.Theoretical and Applied Genetics 44,6 (1974), 262-265.

Preferably, in step (1), the computing formula of the interaction force that each winged insect individuality is subject in winged insect group is:

$F_{i,\mathrm{int}}={\mathrm{\Σ}}_k{F}_{i,k},F_{i,k}=\frac{{\mathrm{\χ}}_k}{N_k}{\mathrm{\Σ}}_{j=1}^{N_k}(g\left(r_{\mathrm{ij}}\right){\hat{r}}_{\mathrm{ji}}+(1-|g\left(r_{\mathrm{ji}}\right)\left|\right){\hat{v}}_{\mathrm{ji}});$

I, j represent that arbitrary different winged insect is individual;

K={rep, ali, att}, subscript rep represents repulsion force, and subscript ali represents Following effect power, and subscript att represents attraction force;

F _{i, int}represent total interaction force that the individual i of winged insect is subject to;

F _{i, k}represent the interaction force representated by subscript k, namely represent repulsion force F during k=rep _{i, rep}, during k=ali, represent Following effect power F _{i, ali}, during k=att, represent attraction force F _{i, att};

χ _k>=0, represent the weight coefficient of each acting force;

N _krepresent that other close on individual number in the region of the individual i radius of influence division of winged insect;

r _ji＝||r _j-r _i|| ₂， ${\hat{r}}_{\mathrm{ji}}=(r_j-r_i)/r_{\mathrm{ji}},{\hat{v}}_{\mathrm{ji}}=(v_j-v_i)/{\left|\right|v_j-v_i\left|\right|}_2,$

Wherein,

R _jirepresent the distance between the individual i of winged insect and the individual j of winged insect;

R _i, r _jrepresent the position of winged insect individual i, j;

represent that the vector of unit length of the individual j position of winged insect is pointed in the individual i position of winged insect;

V _i, v _jrepresent the speed of winged insect individual i, j;

represent the vector of unit length of the difference of the individual j of winged insect and the individual i velocity vector of winged insect;

G (r _ji) represent piecewise function, as 0≤r _ji≤ r _reptime, g (r _ji)=-1,

Work as r _rep≤ r _ji≤ r _alitime, g (r _ji)=0;

Work as r _ali≤ r _ji≤ r _atttime, g (r _ji)=1;

R _reprepresent the radius of action of repulsion force, r _alirepresent the radius of action of Following effect power, r _attrepresent the radius of action of attraction force.

In order to improve the accuracy of simulation, calculate the individual friction force be subject in winged insect group of winged insect by friction formula in physics, preferably, in step (1), the computing formula obtaining the friction force that winged insect individuality is subject in winged insect group is:

F _i，fric＝-γ|v _i|v _i；

Wherein,

I represents that arbitrary winged insect is individual;

F _{i, fric}represent the friction force that winged insect individuality is subject to;

γ represents friction factor;

| v _i| represent the size of the speed of winged insect individuality;

V _irepresent the speed of winged insect individuality.

The kind of intrinsic noise power is a lot, choose wherein a kind of as intrinsic noise power, preferably, in step (1), described intrinsic noise power is white noise, white Gaussian noise, three-dimensional Perlin noise or Curl noise, and the computing formula obtaining the intrinsic noise power that winged insect individuality is subject in winged insect group is:

White noise W is three-dimensional random noise, by three one-dimensional random noise w ₁, w ₂, w ₃composition, computing formula is as follows:

W＝(w ₁，w ₂，w ₃)；

w _k＝Random(-δ _k，δ _k)，k＝1，2，3；

Wherein, δ _kfor any real number, Random (-δ, δ) function returns the random number of a span between (-δ, δ);

The computing formula of white Gaussian noise G is as follows:

$G=\mathrm{\λ}\sqrt{-2{\log W}_1}\sin \left(2\mathrm{\π}{W}_2\right);$

Wherein,

I represents that arbitrary winged insect is individual;

λ represents strength factor;

W ₁, W ₂represent two different white noises;

Three-dimensional Perlin noise P (r _i) computing formula be:

$P\left(r_i\right)=(P_1\left(\frac{r_i}{\mathrm{scale}}\right),P_2\left(\frac{r_i}{\mathrm{scale}}\right),P_3\left(\frac{r_i}{\mathrm{scale}}\right))\·\mathrm{gain};$

Wherein,

P ₁, P ₂and P ₃represent the Perlin noise of one of them dimension respectively;

Scale and gain is the smoothness that two noise parameter: scale are used for indirect control noises, gain is for adjusting the size of noise, reduce scale and can obtain more chaotic motion path, increase scale and can obtain more smooth motion path, increase the size that gain then can increase the random motion speed of winged insect individuality, can set according to demand.

Curl noise C (r _i) computing formula as follows:

$C\left(r_i\right)=\▿\×P\left(r_i\right);$

Wherein,

represent and the curl of vector is operated.

White noise, white Gaussian noise, Perlin noise and Curl noise are the most frequently used four kinds of noises of occurring in nature, are commonly used to simulate various spontaneous phenomenon.Simulate in the experimental result of fruit bat group in the present invention program, the visual effect of Curl noise is best, white Gaussian noise takes second place, and the effect of Perlin noise and white noise third, and user can select to adopt which kind of noise function as intrinsic noise power as required voluntarily.

In order to improve the accuracy of simulation, also introduce the reagentia power of winged insect individuality, preferably, in step (2), desired speed v _{i, pref}computing formula be:

${\stackrel{\·}{v}}_{i,\mathrm{pref}}=a_i=F_{i,\mathrm{int}}+F_{i,\mathrm{pro}}+F_{i,\mathrm{\ξ}};$

Wherein,

I represents that arbitrary winged insect is individual;

for expecting speed v _{i, pref}derivative, represent the rate of change of speed, i.e. acceleration;

A _irepresent the acceleration of winged insect individuality;

F _{i, ξ}represent intrinsic noise power;

F _{i, pro}represent self drive, computing formula is:

F _i，pro＝F _i，fric+F _i，res；

Wherein, F _{i, res}represent when winged insect is individual perceive danger in environment or interested thing time the reagentia power that shows, generally show as and flee from reaction (when perceiving danger) or pursuit reaction (when perceiving interested target).According to list of references 2 and list of references 3, the opposite direction tended to when winged insect is fled from toward dangerous arriving direction is run away, and has one 90 ° ~ 180 ° depart from random, to confuse the enemy, see list of references 2; Generally can fly to interested target as the crow flies when winged insect chases, see list of references 3;

List of references 2:DOMENICI, P., BLAGBURN, J.M., AND BACON, J.P.2011.Animal escapology ii:escape trajectory case studies.The Journal ofexperimental biology 214,15,2474-2494

List of references 3:LUKEMAN, R., LI, Y.-X., AND EDELSTEIN-KESHET, L.2010.Inferring individual rules from collective behavior.Proceedings of theNational Academy of Sciences 107,28,12576-12580.

The compound eye structural of winged insect makes them can barrier or the individual impact on self of other winged insects in promptly perception surrounding environment, thus adjust oneself flying speed direction and velocity magnitude at short notice, avoid colliding with barrier or other individual generations.In order to simulate this behavior of winged insect, need to adopt the motion of a kind of collision avoidance algorithms to winged insect to revise.Conventional collision avoidance algorithms has a lot, as social force algorithm, see list of references 4, MDE algorithm, see list of references 5, RVOs algorithm, see list of references 6.

List of references 4:HELBING, D., AND MOLNAR, P.1995.Social force modelfor Pedestrian dynamics.Phys.Rev.E 51,5,4282-4286.

List of references 5:TREUILLE A., COOPER S., POPOVIC Z.:Continuumcrowds.ACM Trans.Graph.25,3 (July 2006), 1160-1168.

List of references 6:VAN DEN BERG, J., GUY, S., LIN, M., AND MANOCHA, D.2011.Reciprocal n-body collision avoidance.In Robotics Research, C.Pradalier, R.Siegwart, and G.Hirzinger, Eds., vol.70 of Springer Tracts inAdvanced Robotics.Springer Berlin Heidelberg, 3-19.

Preferably, in step (3), target velocity v _{i, act}computing formula be:

v _i，act＝f _R(v _i，pref)；

Wherein,

I represents that arbitrary winged insect is individual;

F _rrepresent the collision avoidance algorithms based on RVOs; RVOs algorithm is widely used collision avoidance algorithms in crowd simulation, and especially have stable, efficient advantage when simulating high density, large-scale colony, the present invention is based on RVOs and carry out collision avoidance algorithms, effect is best.

V _{i, pref}represent desired speed.

In step (4), more new formula is:

${\stackrel{\·}{r}}_i=v_{i,\mathrm{act}};$

Wherein,

I represents that arbitrary winged insect is individual;

represent the rate of change of the individual position of winged insect.

The present invention utilizes the parameter of former frame picture to calculate the target velocity of each winged insect in a rear frame picture, obtain the up-to-date locus of all winged insects, upgrade the simulation winged insect group of certain frame and show animation, in all analog frame, repeating above-mentioned steps until animation simulation terminates.

Speed all herein and power are all vectors, represent size and Orientation.

Beneficial effect of the present invention:

Winged insect group analogy method based on biology rule of the present invention, winged insect group is simulated based on the method for biology rule by adopting, quite similar with the movement locus of true winged insect, there is cogency and the sense of reality, can the common behavior of efficiently and realistically simulating nature Jie Zhong winged insect colony.

Accompanying drawing explanation

Fig. 1 is the process flow diagram of technical solution of the present invention.

Embodiment

As shown in Figure 1, the winged insect group analogy method based on biology rule of the present embodiment, comprises the steps:

In step (1), the computing formula of the interaction force that each winged insect individuality is subject in winged insect group is:

$F_{i,\mathrm{int}}={\mathrm{\Σ}}_k{F}_{i,k},F_{i,k}=\frac{{\mathrm{\χ}}_k}{N_k}{\mathrm{\Σ}}_{j=1}^{N_k}(g\left(r_{\mathrm{ij}}\right){\hat{r}}_{\mathrm{ji}}+(1-|g\left(r_{\mathrm{ji}}\right)\left|\right){\hat{v}}_{\mathrm{ji}});$

I, j represent that arbitrary different winged insect is individual;

K={rep, ali, att}, subscript rep represents repulsion force, and subscript ali represents Following effect power, and subscript att represents attraction force;

F _{i, int}represent total interaction force that the individual i of winged insect is subject to;

F _{i, k}represent the interaction force representated by subscript k, namely represent repulsion force F during k=rep _{i, rep}, during k=ali, represent Following effect power F _{i, ali}, during k=att, represent attraction force F _{i, att};

χ _k>=0, represent the weight coefficient of each acting force;

N _krepresent that other close on individual number in the region of the individual i radius of influence division of winged insect;

r _ji＝||r _j-r _i|| ₂， ${\hat{r}}_{\mathrm{ji}}=(r_j-r_i)/r_{\mathrm{ji}},{\hat{v}}_{\mathrm{ji}}=(v_j-v_i)/{\left|\right|v_j-v_i\left|\right|}_2,$

Wherein,

R _jirepresent the distance between the individual i of winged insect and the individual j of winged insect;

R _i, r _jrepresent the position of winged insect individual i, j;

represent that the vector of unit length of the individual j position of winged insect is pointed in the individual i position of winged insect;

V _i, v _jrepresent the speed of winged insect individual i, j;

represent the vector of unit length of the difference of the individual j of winged insect and the individual i velocity vector of winged insect;

G (r _ji) represent piecewise function, as 0≤r _ji≤ r _reptime, g (r _ji)=-1, works as r _rep≤ r _ji≤ r _alitime, g (r _ji)=0; Work as r _ali≤ r _ji≤ r _atttime, g (r _ji)=1, wherein, r _reprepresent the radius of action of repulsion force, r _alirepresent the radius of action of Following effect power, r _attrepresent the radius of action of attraction force.

The computing formula obtaining the friction force that winged insect individuality is subject in winged insect group is:

F _i，fric＝-γ|v _i|v _i；

Wherein,

I represents that arbitrary winged insect is individual;

F _{i, fric}represent the friction force that winged insect individuality is subject to;

γ represents friction factor;

| v _i| represent the velocity magnitude of winged insect individuality;

V _irepresent the speed of winged insect individuality.

Described intrinsic noise power is Curl noise, Curl noise C (r _i) computing formula as follows:

$C\left(r_i\right)=\▿\×P\left(r_i\right);$

Wherein,

represent and the curl of vector is operated;

$P\left(r_i\right)=(P_1\left(\frac{r_i}{\mathrm{scale}}\right),P_2\left(\frac{r_i}{\mathrm{scale}}\right),P_3\left(\frac{r_i}{\mathrm{scale}}\right))\·\mathrm{gain};$

Wherein,

P ₁, P ₂and P ₃represent the Perlin noise of one of them dimension respectively;

Scale and gain is the smoothness that two noise parameter: scale are used for indirect control noises, and gain is for adjusting the size of noise;

$G=\mathrm{\λ}\sqrt{-2{\log W}_1}\sin \left(2\mathrm{\π}{W}_2\right);$

Wherein,

I represents that arbitrary winged insect is individual;

λ represents strength factor;

W ₁, W ₂represent two different white noises;

White noise W is three-dimensional random noise, by three one-dimensional random noise w ₁, w ₂, w ₃composition, computing formula is as follows:

W＝(w ₁，w ₂，w ₃)；

w _k＝Random(-δ _k，δ _k)，k＝1，2，3；

Wherein, δ _kfor any real number, Random (-δ, δ) function returns the random number of a span between (-δ, δ);

(2) according to the interaction force obtained in step (1), friction force and intrinsic noise power, the individual desired speed in each moment of each winged insect is calculated;

Desired speed v _{i, pref}computing formula be:

${\stackrel{\·}{v}}_{i,\mathrm{pref}}=a_i=F_{i,\mathrm{int}}+F_{i,\mathrm{pro}}+F_{i,\mathrm{\ξ}};$

Wherein,

I represents that arbitrary winged insect is individual;

represent desired speed v _{i, pref}derivative, represent the rate of change of speed, i.e. acceleration;

A _irepresent the acceleration of winged insect individuality;

F _{i, ξ}represent intrinsic noise power;

F _{i, pro}represent self drive, computing formula is:

F _i，pro＝F _i，fric+F _i，res；

Wherein, F _{i, res}represent when winged insect is individual perceive danger in environment or interested thing time the reagentia power that shows.

(3) utilize the collision avoidance algorithms based on RVOs to correct the desired speed value calculated in step (2), obtain the individual target velocity in each moment of each winged insect;

Target velocity v _{i, act}computing formula be:

v _i，act＝f _R(v _i，pref)；

Wherein,

I represents that arbitrary winged insect is individual;

F _rrepresent the collision avoidance algorithms based on RVOs;

V _{i, pref}represent desired speed.

(4) according to the target speed value obtained in step (3), the up-to-date locus of all winged insects is upgraded frame by frame, simulation winged insect group;

More new formula is:

${\stackrel{\·}{r}}_i=v_{i,\mathrm{act}};$

Wherein,

I represents that arbitrary winged insect is individual;

represent the rate of change of the individual position of winged insect.

Use the up-to-date locus of all winged insects, upgrade the simulation winged insect group of former frame and show animation, in all analog frame, repeat above-mentioned steps until animation simulation terminates, simulation completes.

In sum, the winged insect group analogy method based on biology rule of the present embodiment, winged insect group is simulated based on the method for biology rule by adopting, quite similar with the movement locus of true winged insect, there is cogency and the sense of reality, also adopt four kinds of different noise fields simulate winged insect group instability, the random behavior such as to turn to suddenly, combine finally by by all power, design the group's algorithm can simulating the common behavior of winged insect group major part, thus can the common behavior of efficiently and realistically simulating nature Jie Zhong winged insect colony.

Claims (6)

1., based on a winged insect group analogy method for biology rule, it is characterized in that, comprise the steps:

(1) continue to obtain individual interaction force, friction force and the intrinsic noise power be subject in winged insect group of each winged insect;

(2) according to the interaction force obtained in step (1), friction force and intrinsic noise power, the individual desired speed value in each moment of each winged insect is calculated;

(3) collision avoidance algorithms utilized corrects the desired speed value calculated in step (2), obtains the individual target speed value in each moment of each winged insect;

(4) according to the target speed value obtained in step (4), the up-to-date locus of all winged insects is upgraded frame by frame, simulation winged insect group.

2. as claimed in claim 1 based on the winged insect group analogy method of biology rule, it is characterized in that, in step (1), the computing formula of the interaction force that each winged insect individuality is subject in winged insect group is:

$F_{i,\mathrm{int}}={\mathrm{\Σ}}_k{F}_{i,k},F_{i,k}=\frac{{\mathrm{\χ}}_k}{N_k}{\mathrm{\Σ}}_{j=1}^{N_k}(g\left(r_{\mathrm{ji}}\right){\hat{r}}_{\mathrm{ji}}+(1-|g\left(r_{\mathrm{ji}}\right)\left|\right){\hat{v}}_{\mathrm{ji}});$

I, j represent that arbitrary different winged insect is individual;

K={rep, ali, att}, subscript rep represents repulsion force, and subscript dli represents Following effect power, and subscript att represents attraction force;

F _{i, int}represent total interaction force that the individual i of winged insect is subject to;

F _{i, k}represent the interaction force representated by subscript k, namely represent repulsion force F during k=rep _{i, rep}, during k=ali, represent Following effect power F _{i, ali}, during k=att, represent attraction force F _{i, att};

χ _k>=0, represent the weight coefficient of each acting force;

N _krepresent that other close on individual number in the region of the individual i radius of influence division of winged insect;

r _ji＝||r _j-r _i|| ₂， ${\hat{r}}_{\mathrm{ji}}=(r_j-r_i)/r_{\mathrm{ji}},{\hat{v}}_{\mathrm{ji}}=(v_j-v_i)/{\left|\right|v_j-v_i\left|\right|}_2,$

Wherein,

R _jirepresent the distance between the individual i of winged insect and the individual j of winged insect;

R _i, r _jrepresent the position of winged insect individual i, j;

represent that the vector of unit length of the individual j position of winged insect is pointed in the individual i position of winged insect;

V _i, v _jrepresent the speed of winged insect individual i, j;

represent the vector of unit length of the difference of the individual j of winged insect and the individual i velocity vector of winged insect;

G (r _ji) represent piecewise function, as 0≤r _ji≤ r _reptime, g (r _ji)=-1,

Work as r _rep≤ r _ji≤ r _alitime, g (r _ji)=0;

Work as r _ali≤ r _ji≤ r _atttime, g (r _ji)=1, wherein, r _reprepresent the radius of action of repulsion force, r _alirepresent the radius of action of Following effect power, r _attrepresent the radius of action of attraction force.

3. as claimed in claim 1 or 2 based on the winged insect group analogy method of biology rule, it is characterized in that, in step (1), the computing formula obtaining the friction force that winged insect individuality is subject in winged insect group is:

F _i，fric＝-γ|v _i|v _i；

Wherein,

I represents that arbitrary winged insect is individual;

F _{i, fric}represent the friction force that winged insect individuality is subject to;

γ represents friction factor;

| v _i| represent the size of the speed of winged insect individuality;

V _irepresent the speed of winged insect individuality.

4. as claimed in claim 3 based on the winged insect group analogy method of biology rule, it is characterized in that, in step (1), described intrinsic noise power is white noise, white Gaussian noise, three-dimensional Perlin noise or Curl noise, and the computing formula obtaining the intrinsic noise power that winged insect individuality is subject in winged insect group is:

White noise W is three-dimensional random noise, by three one-dimensional random noise w ₁, w ₂, w ₃composition, computing formula is as follows:

W＝(w ₁，w ₂，w ₃)；

w _k＝Random(-δ _k，δ _k)，k＝1,2,3；

Wherein, δ _kfor any real number, Random (-δ, δ) function returns the random number of a span between (-δ, δ);

The computing formula of white Gaussian noise G is as follows:

$G=\mathrm{\λ}\sqrt{-2\log W_1}\sin \left(2\mathrm{\π}{W}_2\right);$

Wherein,

I represents that arbitrary winged insect is individual;

λ represents strength factor;

W ₁, W ₂represent two different white noises;

Three-dimensional Perlin noise P (r _i) computing formula be:

$P\left(r_i\right)=(P_1\left(\frac{r_i}{\mathrm{scale}}\right),P_2\left(\frac{r_i}{\mathrm{scale}}\right),P_3\left(\frac{r_i}{\mathrm{scale}}\right))\·\mathrm{gain};$

Wherein,

P ₁, P ₂and P ₃represent the Perlin noise of one of them dimension respectively;

Scale and gain is the smoothness that two noise parameter: scale are used for indirect control noises, and gain is for adjusting the size of noise;

Curl noise C (r _i) computing formula as follows:

$C\left(r_i\right)=\▿\×P\left(r_i\right);$

Wherein,

represent and the curl of vector is operated.

5., as claimed in claim 4 based on the winged insect group analogy method of biology rule, it is characterized in that, in step (2), desired speed v _{i, pref}computing formula be:

${\stackrel{.}{v}}_{i,\mathrm{pref}}=a_i=F_{i,\mathrm{int}}+F_{i,\mathrm{pro}}+F_{i,\mathrm{\ξ}};$

Wherein,

I represents that arbitrary winged insect is individual;

represent desired speed v _{i, pref}derivative, represent the rate of change of speed, i.e. acceleration;

A _irepresent the acceleration of winged insect individuality;

F _{i, ξ}represent intrinsic noise power;

F _{i, pre}represent self drive, computing formula is:

F _i，pro＝F _i，fric+F _i，res；

Wherein, F _{i, res}represent when winged insect is individual perceive danger in environment or interested thing time the reagentia power that shows.

6., as claimed in claim 1 based on the winged insect group analogy method of biology rule, it is characterized in that, in step (3), target velocity v _{i, act}computing formula be:

v _i，act＝f _R(v _i，pref)；

Wherein,

I represents that arbitrary winged insect is individual;

F _rrepresent the collision avoidance algorithms based on RVOs;

V _{i, pref}represent desired speed.