CN110456815A - It is a kind of based on the heuristic intelligent unmanned plane cluster co-located method of army antenna - Google Patents
It is a kind of based on the heuristic intelligent unmanned plane cluster co-located method of army antenna Download PDFInfo
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Abstract
The invention discloses a kind of based on the heuristic intelligent unmanned plane cluster co-located method of army antenna: step 1: the optimizing mechanism of army antenna heuritic approach is obtained by army antenna foraging behavior specificity analysis；Step 2: the self-organizing behavior modeling based on army antenna foraging behavior characteristic；Step 3: distributed multiple no-manned plane collaboratively searching location tasks planning.The present invention has the advantages that 1) the method for the present invention positions target only in accordance with azimuth information, affected by environment small, operating distance is remote；2) distributed AC servo system decision is used, has many advantages, such as that high reliablity, calculation amount are few, the traffic is small；3) by using for reference the good self-organization of army antenna swarm intelligence, harmony in nature, strong robustness the features such as, so that unmanned plane is only under local sensing ability, complicated behavior pattern is realized by the interaction with other unmanned planes and environment, intelligence is shown in group's level, effectively improves unmanned plane cluster Self-organizing Science ability.
Description
Technical field
The unmanned plane cluster Self-organizing Science research method based on biocenose intelligence that the present invention relates to a kind of, especially one
Kind belongs to unmanned plane autonomous control field based on the heuristic intelligent unmanned plane cluster co-located method of army antenna.
Background technique
Unmanned aerial vehicle group is a kind of typical multi-agent system, can independently or remotely control, can hold without pilot
Row task.Compared with manned unmanned plane, unmanned plane has outstanding in terms of executing uninteresting, dirty, dangerous task
Advantage.Since limitation of the separate unit unmanned plane in the task of execution obtains higher work using the collaborative work of more unmanned planes
War efficiency is very important.U.S. Department of Defense has issued the 5th edition " integrated unmanned systems route map " (2017-2042),
It is proposed the division methods of unmanned plane capacity of will level, it is indicated that multi-platform cooperation is the important trend of unmanned air vehicle technique development.
The capacity of will of UAV system continues to develop, and will gradually melt from simple remote control, stored program control system to human-machine intelligence
The interactive controlling of conjunction, even full autonomous control direction are developed, and UAV system will be provided with the ability that cluster is performed in unison with task.With
A large amount of different types, the unmanned systems of different performance be devoted to battlefield and execute various tasks, be necessarily required to rationally efficient
Autonomous Coordination Decision and control means improve fighting efficiency to enhance its Mission Capability, final to realize unmanned systems battlefield
Situation control.
Multiple no-manned plane self-cooperation control structure is generally divided into two classes: centralized control architecture and distributed AC servo system system
Structure.Centralized control method is the main method in early stage research, has the advantage for obtaining globally optimal solution, but maximum disadvantage
It is once decision-making level fails, multiple no-manned plane system will be out of hand.With the raising of unmanned plane performance and capacity of will, having can
Distributed control method by the advantages that property is high, calculation amount is few, the traffic is small becomes research hotspot.
Most of ants, which can all find suitable area, nests and seeks cave, they can constantly be expanded with the increase of population quantity
Nest size.But army antenna is different, they will not establish permanent nest, and almost the moment, which is at, ceaselessly moves.March
Ants live in a clearly demarcated society, class, and wherein worker ant is a kind of ant that quantity is most in ant colony, they mostly by
The female ant composition of offspring can not be bred.Worker ant can show polymorphism according to the difference of the focus of work --- for example specially
Duty is larger in the worker ant head feeler of investigation food, and the ability that they issued and received information is stronger；It is for another example full-time to be eaten in carrying
The worker ant of object, they are strong, and movement speed is also fast, this is provided to improve the efficiency for carrying food.
Army antenna majority compound eye missing is degenerated, and is exchanged mainly by pheromones, therefore being capable of table during looking for food
Reveal fabulous interaction feature, army antennas carry out individual decision making by Local Interaction, finally make entire group from macroscopically
Emerge the sense of organization, harmony, stability and the adaptability to environment；The purpose of unmanned systems cluster fight is then to pass through distribution
Formula decision is realized can self-organizing, harmony be good, purposeful flight of strong robustness.
The present invention intends proposing on the basis of studying army antenna foraging behavior feature and internal mechanism and realizing that one kind is based on
The heuristic intelligent aerial cluster distributed cotasking self-organizing method of army antenna is appointed for solving multiple no-manned plane co-located
Business planning problem.
Passive location refers to positioning system by obtaining the electromagnetic signal of target itself radiation or the external spoke of target reflection
Source signal is penetrated, the presence of target is detected, obtains the information of target, and provides the space coordinate of target with certain precision.Due to
Positioning system itself non-radiating electromagnetic signal at work, and electromagnetic signal is only received, therefore referred to as passive location system.Have
Source positioning system be target is positioned according to distance and bearing information, and passive location system only in accordance with azimuth information to mesh
Mark is positioned.Passive location system is affected by environment small, and operating distance is remote.
Passive location obtains the signal of same radiation by being distributed in the station of space different location, measures its parameter,
Its geographical location is determined by positions calculations.The method of passive co-located mainly has: direction cross positioning, positioning using TDOA and
Direction finding-positioning using TDOA.What the present invention used is exactly the direction cross positioning method of passive co-located.
In conclusion the invention proposes a kind of based on the heuristic intelligent unmanned plane cluster co-located side of army antenna
Method effectively improves unmanned plane cluster Self-organizing Science ability to solve the problems, such as the passive co-located target of multiple no-manned plane.
Summary of the invention
The present invention is proposed a kind of based on heuristic intelligent nobody of army antenna by the research to army antenna foraging behavior
Machine cluster co-located method, to solve the problems, such as that the passive co-located target of multiple no-manned plane is filled under the task scene of scenario
Distribution waves that unmanned plane independent and flexible, steady reliable and viability be high, the advantages such as cheap, mesh of the realization to unknown spatial position
The positioning of target fast search, effectively improves unmanned plane cluster Self-organizing Science ability.
The present invention is directed to the passive co-located target problem of multiple no-manned plane, has carried out following research: army antenna group is from group
Knit behavior modeling and specificity analysis research, the self-organizing behavior modeling based on army antenna foraging behavior characteristic, distribution mostly nobody
The planning of machine collaboratively searching location tasks.Carrying out the analysis of army antenna group self-organizing behavioral trait and cotasking self-organizing method
On the basis of research, carry out modeling and simulation analysis in conjunction with multiple no-manned plane collaboratively searching location tasks feature, key step is as follows:
Step 1: the optimizing mechanism of army antenna heuritic approach is obtained by army antenna foraging behavior specificity analysis
Biologist has found that army antenna can show fabulous interaction feature during looking for food after study, march
The place that ant can pass through during looking for food leaves a kind of special secretion --- pheromones, finds food for other army antennas
Object or return ant main forces give a clue, and then entire ant colony of marching can be helped to find the shortest path between food and " legion "
Diameter.
It, can be with by the effects of pheromones although the selective power of single army antenna is limited during entirely looking for food
Make march ant colony that integrally there is high self-organization, exchange routing information between army antenna, eventually by march ant colony
Collective behavior finds optimal path.
As shown in Figure 1, N point is army antenna main forces, F point is food source, over time, due to the work of pheromones
With, army antenna will eventually will select completely the path with increasing probability selection shortest path, thus find by
" legion " arrives the shortest path of food source.
Army antenna determines investigation by release pheromone and resolution information element concentration, and therefore, pheromones design is
One of the key point of army antenna foraging behavior emulation.The Heuristic Intelligent Algorithm of army antenna forage behavior is simulated based on as follows
Basic assumption:
(1) it is communicated between army antenna by pheromones and environment.Every army antenna is according only to surrounding local ring
Border is made a response, and is also only had an impact to surrounding local environment；
(2) army antenna is determined the reaction of environment by its internal schema.Because army antenna is gene biological, the row of army antenna
For the adaptation sex expression for being actually its gene, i.e. army antenna is reactive adaptive agent；
(3) in individual level, every army antenna makes independent choice according only to environment；On population level, every row
The behavior of army ant has randomness, but ant colony of marching can form the group behavior of high-sequential by self-organizing process.
By above-mentioned hypothesis and analysis as it can be seen that the optimizing mechanism of the army antenna heuritic approach includes two root phases: suitable
Answer stage and the stage of cooperating.In the laundering period, each candidate solution (i.e. army antenna individual) constantly adjusts itself according to the information of accumulation
Structure, the army antenna passed through on path is more, and information content is bigger, then the path is easier is selected, and the time is longer, information content meeting
It is smaller；In the cooperation stage, by information exchange between candidate solution, performance is generated with expectation and is preferably solved.
Army antenna group carries out individual decision making by Local Interaction, and entire group is finally made to come from group from macroscopically emerging in large numbers
Knitting property, collaborative, stability and the adaptability to environment；The purpose of unmanned systems task is exactly to be realized by distributed decision making
It can self-organizing, harmony be good, purposeful flight of strong robustness.
Step 2: the self-organizing behavior modeling based on army antenna foraging behavior characteristic
On the basis of step 1, the self-organizing behavior modeling based on army antenna foraging behavior characteristic is realized.Based on march
Self-organization, harmony, the strong robust that the heuristic intelligent unmanned systems of ant pass through army antenna swarm intelligence in reference nature
Property the features such as so that single unmanned systems are only under local sensing ability, by with the mutual of other unmanned systems and environment
It acts on to realize complicated behavior pattern, shows intelligent phenomenon in group's level.
S21, the army antenna group structure model based on paralleling tactic
The Local Interaction that the army antenna process of looking for food is shown in nature be characterized in carrying out by pheromones field from group
Knit interbehavior.Self-organizing interactive structure model such as Fig. 2 institute based on paralleling tactic is obtained according to army antenna foraging behavior feature
Show.
As shown in Fig. 2, each army antenna unit is assigned an independent calculate node in paralleling tactic, for constructing
Their solution to problem, army antenna unit is corresponding with unmanned plane, is distributed on respective processing node, passes through link
Link is to constitute distributed frame.
Army antenna individual start search when, can substantially be estimated in itself sensing range other army antennas position and
Direction, and can be mobile according to node transition rule:
Wherein, (x (t), y (t)) represents the position of other army antennas, PSI*(t) direction of other army antennas, AC=are represented
{Ant1, Ant2..., AntNv, Nv is unmanned plane cluster scale (unmanned plane number).
In the distributed army antenna arrest mechanism for solving unmanned plane (UAV) cotasking self-organizing problem, with " ant
Ant agency " indicates the army antenna individual for representing UAV execution to the artificial scenario of the search location tasks of the target of mission area, i.e.,
Artificial army antenna, it is fixed to the search of target in mission area that foraging behavior of the ant agent in its spatial movement corresponds to UAV
Position behavior.Therefore, march ant colony scale should be identical as UAV scale, if UAV cluster is V={ V1, V2..., VNv}。
In distribution march ant colony arrest mechanism, ant agent have positioning as UAV platform, perception, consumption,
The ability of communication and self-renewing, while the constraint by platform property and space collision prevention.
Environmental information element structure is corresponding with ant agent, it is present in the node of each ant agent in march ant colony
On, it is mutually indepedent between the corresponding local information element structure of different ant agents.Due to being the distributed body of non-stop layer node
Architecture, so public pheromones structure is not present in entire group, the pheromone concentration of different zones has reacted the region
To the attraction degree of individual, each ant agent each walking is dynamic complete after, need the movement state information according to itself,
The mission area relevant information of acquisition and the information from other ant agents of receiving believe the home environment of self maintained
Breath element is updated.
The pheromones structure of t at any time, the maintenance of k-th of ant agent can be expressed as:
Wherein τ(k)(t) the pheromones structure of k-th of ant agent of t moment maintenance is indicated,Expression wherein corresponds to
Gridding space coordinate be (x, y) gridding information element concentration value, NwidthIndicate the mesh width divided, NheightIt indicates to draw
The grid height divided.
The Pheromone update model that S22, oriented mission are coordinated
S221, it avoids that the Pheromone update searched for is repeated several times
It, can be according to the distribution for the entire march ant colony that current time is grasped when the transfer of ant agent one next state of every completion
Situation, the region crossed to army antenna group hunting carry out Pheromone update, reduce its pheromone concentration, so that these areas for searching for
Domain reduces the attraction of other army antennas, avoids the region searched for before the excessive repeat search of ant colony, and then improve cluster
Search efficiency.Pheromone update mode is as follows:
Wherein,Indicate k-th of army antenna of subsequent time in the gridding information element concentration that coordinate is (x, y)
Value,It indicates in the gridding information element concentration decreasing value that coordinate is (x, y),Indicate j-th of army antenna in t
Carve the degree of certainty to current grid, l1To determine coefficient, RsearchFor search radius,Indicate the
Distance of the grid that j army antenna is passed by t moment to the grid that coordinate is (x, y).
S222, environmental uncertainty bring Pheromone update over time
A certain panel region is searched in t moment, but over time, which new food may also occurs
The uncertainty in source, the region enhances over time, is consequently increased to the attraction degree of army antenna, therefore each
Pheromone concentration in grid further increases.Update mode is as follows:
Wherein, (0,1) C ∈ is uncertain factor, and value is bigger to illustrate that dynamic is stronger, passage environment letter at any time
It is bigger to cease plain concentration, it is stronger to the attraction of army antenna；It indicates to increase in the gridding information element concentration of coordinate (x, y)
Value,The settable upper limit of value, avoid influence of the environmental uncertainty to pheromones excessively high.Environmental uncertainty with
Time elapses under bring Pheromone update mechanism, and the search range of march ant colony may make to cover entire mission area as much as possible
Domain.
S223, Pheromone update is convened for discovery target
When some army antenna finds some food source F, the pheromone concentration in the food source adjacent domain increases, to inhale
Draw more army antennas to help to confirm the presence of the food source and position；But when degree of certainty reaches certain value (or
When position positioning accuracy reaches requirement), then it no longer needs to attract more army antennas.Its Pheromone update mechanism is as follows:
Wherein,It indicates in the gridding information element concentration value added that coordinate is (x, y),Indicate gridding information
Plain concentration decline value, enhancing degree and attenuation degree are spread centered on food source to surrounding；σ is diffusion coefficient, characterize to
The range of external diffusion；αgAnd αdThe enhancing coefficient and attenuation coefficient for respectively convening Pheromone update, take between (0,1)；D ((x,
Y), (xF(t), yFIt (t)) is) distance of some food source F for finding in t moment to the grid that coordinate is (x, y).
S23, army antenna cluster group's behavior model based on limited distance information exchange mechanism
Pheromones include two kinds of information: catalysis pheromonesWith inhibition pheromonesConvening process
In, what is embodied first is positive feedback mechanism: food source information is passed to individual around by guidance individual, and guidance individual is according to food
The quantity for following individual is convened in the selection of material resource quality, and a certain food source quality is higher, and guidance individual passes the information of the food source
It passs and more follows the probability of individual higher；Simultaneously there is also negative feedback mechanisms: for a certain food source, with convene with
Quantity with individual is higher and higher, and looking for food for food source tends to be saturated, and a cognition inhibition for participating in looking for food at this time is convened more
Individual, to avoid the waste of labo r resources.
The pheromones to other UAVs sent when some UAV is in some grid generation pheromones or by communications reception
When information, pheromone concentration of the UAV at the grid be catalyzed pheromones with inhibition pheromones be superimposed and:
It is to carry out information transmitting by pheromones between army antenna individual.During convening, guidance individual is by releasing
Put pheromones, will carry the related information of food source pass to around follow individual at a distance of closer.In process of inhibition, follow
Individual follows individual by what pheromones passed to that other do not recruit.The transmitting of information is also a markov mistake in army antenna
Journey:
DA(t+1)=∑neighbour(A)DA(t) (7)
Wherein DA(t) the food source information that army antenna individual A is carried in t moment is indicated, neighbour (A) indicates march
The army antenna individual collections of the pheromones of A can be perceived around ant individual A.
Step 3: distributed multiple no-manned plane collaboratively searching location tasks planning
The mapping relations of S31, biocenose behavioural characteristic and task scene
Multiple no-manned plane cluster can be analogized to intelligent body army antenna ant colony, ant colony is by army antenna individual cell institute one by one
Composition can interact and cooperate by being based on information prime information between each army antenna unit and other army antenna units,
Suitable local action can be generated in this way to reach the result of whole global collaborative.Ant colony foraging behavior is cooperateed with to multiple no-manned plane
Search for mapping such as the following table 1 of position fixing process.
| Ant colony foraging behavior | Multiple no-manned plane collaboratively searching position fixing process |
| Ant individual cell | Single rack unmanned plane unit |
| Food source | The target of mission area |
| Path from ant nest to food source | Mission area can fly path |
| Food is carried | Task behavior to target |
| Ant sensing region | Unmanned plane investigative range |
| Perception information element between ant | Unmanned plane information interaction |
| Ant perceives environment | The unmanned plane indirect communication coordination system |
Table 1
It should be noted that actual march ant colony is to influence environment by this medium of pheromones, then influence it indirectly
He is individual, embodies indirect communication coordinative role, and the behaviour decision making of other individuals is influenced by pheromone concentration.And it is aerial
Cluster collaboratively searching location tasks are abstracted as biological information element due to not no this physical media of the pheromones of physical presence
.Each unmanned plane individual is owned by the pheromones field of oneself, and the state stored in memory headroom letter is shown as on physical layer
Breath, by the information exchange of topological structure and local communication progress biological information element field between individual, to realize from part
To global collaboration, and the building process of biological information element field is then referring to shown in the update mechanism of above- mentioned information element.
S32, the collaboratively searching location tasks frame based on the pheromones coordination system
During carrying out collaboratively searching positioning to target, unmanned plane node realizes that grouping, DPD are fixed by neighborhood interactive
The operation such as position and the update of information sketch map, then according to respective information sketch map as the subsequent movement of action guide progress.Due to adopting
With distributed structure, without central node, each unmanned plane can not carry out global information element perception, can only perceive part letter
Breath realizes that the indirect coordination of global information perceives by the Local Interaction of pheromones.Specifically, based on the pheromones coordination system
Collaboratively searching position fixing process includes the update for moving decision process and information sketch map；
Wherein movement decision process is as follows:
Step 321: the position of the highest point of pheromone concentration on the information sketch map of each unmanned plane node oneself is calculated
(if having multiple points, appoint and take a point)；
Step 322: calculating the highest point of pheromone concentration to the gravitation (unit direction vector) of the unmanned plane node, and
Determine the unmanned plane target to be positioned in next step；
Step 323: judge whether each unmanned plane nodal point separation is less than minimum safe distance when the distance of the target of prelocalization
From it is then to give one perpendicular to present node to the power in target point direction, allows to then carry out around target movement
Step 324；It is no, then directly carry out Step 324；
Step 324: each target for judging other nodes in each its neighborhood of unmanned plane nodal point separation and perceiving
Does is the distance of (other targets in addition to the target that the current unmanned plane is positioning) less than minimum safe distance it is, then
Its repulsion (unit direction vector) to current unmanned plane node is calculated, Step 325 is then carried out；It is no, then directly carry out Step
325；
Step 325: the power being calculated in the above process is added to obtain the power of guidance unmanned plane movement, in turn
The acceleration of unmanned plane joint movements, the position of speed and next step is calculated.
Wherein, the renewal process of information sketch map is as follows:
Step 326: each unmanned plane node calculates in neighborhood according to current latest position for which unmanned plane node,
And in this, as the communication range of the unmanned plane in this step-length；
Step 327: each unmanned plane node, which updates the path in information sketch map according to this step paths traversed, to be believed
Breath；
Step 328: new mesh is calculated by DPD algorithm according to the target currently to be positioned for each unmanned plane node
Mark probability density distribution；
Step 329: then carry out neighborhood in information exchange, each unmanned plane node obtain it is that oneself is perceived or
Most exact probability Density Distribution in the unmanned plane neighborhood for the target that other unmanned planes perceive, and contain in neighborhood it
The routing information in his node motion path；
The destination probability density of each unmanned plane node: being finally distributed and merge with routing information by Step 330, and
According to the Pheromone update model in technical scheme steps two, i.e. formula (1)-(7) are updated, and it is newest to obtain each node
Information sketch map.
The invention proposes a kind of based on the heuristic intelligent unmanned plane cluster co-located method of army antenna, the invention
Main advantage is mainly reflected in 3 aspects: 1) the direction cross positioning method of the passive co-located used is believed only in accordance with orientation
Breath positions target, and affected by environment small, operating distance is remote；2) distributed AC servo system decision is used, there is high reliablity, meter
The advantages that calculation amount is less, the traffic is small；3) the good, Shandong by the self-organization of army antenna swarm intelligence, harmony in reference nature
The features such as stick is strong, so that unmanned plane only under local sensing ability, passes through the phase interaction with other unmanned planes and environment
For realizing complicated behavior pattern, intelligence is shown in group's level, effectively improves unmanned plane cluster Self-organizing Science
Ability.
Detailed description of the invention
The schematic diagram of Fig. 1 army antenna foraging behavior simulation
The schematic diagram of self-organizing interactive structure model of the Fig. 2 based on paralleling tactic
Fig. 3 multiple no-manned plane collaboratively searching location tasks flow diagram
Direction cross positioning (DPD) schematic diagram in Fig. 4 plane
Fig. 5 simulation result diagram --- start search positioning
Fig. 6 simulation result diagram --- search position fixing process 1
Fig. 7 simulation result diagram --- search position fixing process 2
Fig. 8 simulation result diagram --- reach positioning accuracy, terminates search
Fig. 9 simulation result diagram --- original state target area probability density distribution
Figure 10 simulation result diagram --- target area probability density distribution when reaching positioning accuracy
The initial information element field of Figure 11 simulation result diagram --- certain node
Figure 12 simulation result diagram --- the pheromones field after the completion of certain node searching
Figure 13 simulation result diagram --- positioning accuracy is with number of nodes and the variation comparison diagram of simulation step length
Figure 14 simulation result diagram --- positioning accuracy is with communication distance and the variation comparison diagram of simulation step length
Figure label and symbol description are as follows:
The quantity of UAV-Num --- unmanned plane
The communication distance of UAV-Dc --- unmanned plane
Specific embodiment
Having for method proposed by the invention is verified below by a specific unmanned plane cluster co-located example
Effect property.Experimental calculation machine is configured to Intel Core i7-7700HQ processor, 2.8Ghz dominant frequency, 8G memory, and software is
MATLAB 2016a version.
It is a kind of based on the heuristic intelligent unmanned plane co-located method detailed process of army antenna referring to technical scheme steps
Task framework in three, as shown in Figure 3.Wherein initialization procedure be it is assumed that task scene in, initialize unmanned plane, mesh
The parameters of mark, information sketch map.The task scene settings of following verifying examples are as follows: in the search of known 100 × 100km^2
In mission area, to assign 50 unmanned plane nodes and 10 unknown objects are scanned for positioning, the communication distance of unmanned plane is 25km,
Positioning accuracy (i.e. error ellipse radius) is 1km (while including certain probability of combat damage).
Wherein DPD (Directional Positioning Determination) is a kind of direction cross positioning technology
(Directional Cosine Intersection Positioning Method) is utilized on two-dimensional surface and is measured
At least two directional diagram, i.e. 2 sector diagrams intersect the method for determining target position.Assuming that certain target is located at the C (x of planec, yc)
Point, 2 unmanned plane node A (xa, 0) and B (xb, 0) and direction finding is carried out to target.Assuming that the angle measurement error between unmanned plane is mutually solely
Vertical, and obey zero-mean gaussian distribution.Then direction cross positioning schematic diagram is as shown in Figure 4 on two-dimensional surface.
The position of two-dimentional Passive Bearing-Crossing Location Systems positioning accuracy and target, the position of unmanned plane and azimuthal measurement error
Etc. factors it is related.It takes DPD algorithm to position unknown object herein, passes through depositing for the available objective fuzzy area of the algorithm
In probability distribution, in this, as the main foundation that the method for unmanned plane information sketch map initialization, follow-up sketch map update, and
Condition is provided to judge whether to reach positioning accuracy.
The step of key link in Fig. 3, is as follows:
(1) decision process is moved
Step 1: calculate the highest point of pheromone concentration on the information sketch map of each unmanned plane node oneself position (if
There are multiple points, then appoint and take a point)；
Step 2: the highest point of pheromone concentration is calculated to the gravitation (unit direction vector) of the unmanned plane node, and really
Make the unmanned plane target to be positioned in next step；
Step 3: do you judge that each unmanned plane nodal point separation is less than minimum safe distance when the distance of the target of prelocalization
It is then to give one perpendicular to present node to the power in target point direction, allows to then carry out Step around target movement
4；It is no, then directly carry out Step 4；
Step 4: each target for judging other nodes in each its neighborhood of unmanned plane nodal point separation and perceiving
Does is the distance of (other targets in addition to the target that the current unmanned plane is positioning) less than minimum safe distance it is, then
Its repulsion (unit direction vector) to current unmanned plane node is calculated, Step 5 is then carried out；It is no, then directly carry out Step
5；
Step 5: the power being calculated in the above process is added to obtain the power of guidance unmanned plane movement, Jin Erji
Calculation obtains the acceleration of unmanned plane joint movements, the position of speed and next step.
(2) update of information sketch map
Step 1: each unmanned plane node calculates in neighborhood according to current latest position for which unmanned plane node, and
In this, as the communication range of the unmanned plane in this step-length；
Step 2: each unmanned plane node updates the routing information in information sketch map according to this step paths traversed；
Step 3: new target is calculated by DPD algorithm according to the target currently to be positioned for each unmanned plane node
Probability density distribution；
Step 4: then carry out neighborhood in information exchange, each unmanned plane node obtain it is that oneself is perceived or its
Most exact probability Density Distribution in the unmanned plane neighborhood for the target that his unmanned plane perceives, and contain in neighborhood other
The routing information in node motion path；
The destination probability density of each unmanned plane node: being finally distributed and merge with routing information by Step 5, and according to
According to the Pheromone update model in technical scheme steps two, i.e. formula (1)-(7) are updated, and obtain the newest letter of each node
Cease sketch map.
It can be seen from the renewal process of information sketch map when time step is considerable enough big, the information of each unmanned plane node
Sketch map will reach unanimity, and realize that distributed global information is shared indirectly.
Wherein emulation such as Fig. 5-8 of entire unmanned plane cluster co-located process, wherein five-pointed star characterizes target position,
" aircraft shape " individual represents unmanned plane node, and the individual of dashed line free connection represents the unmanned plane damaged, and dotted line represents nobody
The corresponding relationship of machine and the target when prelocalization, different size of oval circle characterization target region that may be present.
In simulation process, as unmanned plane node is by pheromones perception and local reciprocation, constantly intersected
Positioning, so that the oval circle of target area constantly reduces, the precision of characterization target detection positioning is continuously improved；It is finally reached
" positioning " precision, search terminate.
The variation of target area probability density distribution such as Fig. 9-10, the grid lines of xoy plane indicate map partitioning to be one
Each and every one grid, x, y-coordinate are position coordinates (unit: kilometer or longitude and latitude) of the current point on map, and z coordinate then indicates to work as
The target existing probability of preceding point.Target area probability density distribution figure is all targets that comprehensive all unmanned plane nodes obtain
Information, the information can't issue each unmanned plane node in simulations.Each unmanned plane node is not the case where knowing global information
Under pass through itself perception local information element field and field interactive mode carry out autokinetic movement decision.The pheromones field of certain node
Variation such as Figure 11-12, x, y-coordinate is position coordinates (unit: kilometer or longitude and latitude) of the current point on map, and z coordinate indicates
The target existing probability of current point.Due to having Local Interaction effect and pheromones increase, reduction etc. to update rule, unmanned plane is every
Secondary movement decision reaches behind new position that there may be carry out the interaction of networking again, therefore its pheromones from different unmanned plane nodes
Field variation is the process of a dynamic adjustment.
In order to verify the validity of proposition method, the present invention has also carried out corresponding comparative experiments, and positioning accuracy is with node
Several and simulation step length variation comparison is as shown in figure 13；Positioning accuracy compares such as Figure 14 with the variation of communication distance and simulation step length
It is shown.
By comparing above as can be seen that in the case that other conditions are constant, the reduction of communication distance and unmanned plane number
The reduction of amount can make search position convergent speed and slow down, but also can all complete in shorter finite time to all
The scouting of target positions, it is seen that proposed by the present invention based on the heuristic intelligent unmanned plane cluster co-located method tool of army antenna
There are the advantages such as high reliablity, required calculation amount and small, the good, strong robustness of harmony of the traffic, effectively improves unmanned plane cluster
Self-organizing Science ability.
Claims (3)
1. a kind of based on the heuristic intelligent unmanned plane cluster co-located method of army antenna, it is characterised in that: this method is main
Steps are as follows:
Step 1: the optimizing mechanism of army antenna heuritic approach is obtained by army antenna foraging behavior specificity analysis
The optimizing mechanism of army antenna heuritic approach includes two root phases: laundering period and the stage of cooperating；In the laundering period,
Each candidate solution, that is, army antenna individual constantly adjusts self structure according to the information of accumulation, and the army antenna passed through on path is more, letter
Breath amount is bigger, then the path is easier is selected, and the time is longer, and information content can be smaller；In the cooperation stage, pass through between candidate solution
Information exchange generates performance with expectation and preferably solves；
Army antenna group carries out individual decision making by Local Interaction, finally makes entire group from macroscopically emerging self-organizing
Property, collaborative, stability and the adaptability to environment；
Step 2: the self-organizing behavior modeling based on army antenna foraging behavior characteristic
S21, the army antenna group structure model based on paralleling tactic
Each army antenna unit is assigned an independent calculate node in paralleling tactic, and army antenna unit is opposite with unmanned plane
It answers, is distributed on respective processing node, distributed frame is constituted by the link of link；
Army antenna individual can substantially estimate the position and side of other army antennas when starting search in itself sensing range
To, and can be mobile according to node transition rule:
Wherein, (x (t), y (t)) represents the position of other army antennas, PSI*(t) direction of other army antennas, AC=are represented
{Ant5,Ant2,…,AntNv, Nv is unmanned plane cluster scale, that is, unmanned plane number；
For solving in the UAV i.e. distributed army antenna arrest mechanism of unmanned plane cotasking self-organizing problem, with " ant generation
Reason " indicates the army antenna individual for representing UAV execution to the artificial scenario of the search location tasks of the target of mission area, i.e., manually
Army antenna, foraging behavior of the ant agent in its spatial movement correspond to UAV in mission area to the search home row of target
For, so, march ant colony scale should be identical as UAV scale, if UAV cluster is V={ V1,V2,…,VNv}；
The pheromones structure of t at any time, the maintenance of k-th of ant agent can be expressed as:
Wherein τ(k)(t) the pheromones structure of k-th of ant agent of t moment maintenance is indicated,Indicate wherein corresponding net
Space coordinate of formatting is the gridding information element concentration value of (x, y), NwidthIndicate the mesh width divided, NheightIndicate division
Grid height；
The Pheromone update model that S22, oriented mission are coordinated
S221, it avoids that the Pheromone update searched for is repeated several times
Pheromone update mode is as follows:
Wherein,Indicate gridding information element concentration value of k-th of the army antenna of subsequent time in coordinate for (x, y),It indicates in the gridding information element concentration decreasing value that coordinate is (x, y),Indicate j-th of army antenna in t moment pair
The degree of certainty of current grid, l1To determine coefficient, RsearchFor search radius,It indicates j-th
Distance of the grid that army antenna is passed by t moment to the grid that coordinate is (x, y)；
S222, environmental uncertainty bring Pheromone update over time
This update mode is as follows:
Wherein, (0,1) C ∈ is uncertain factor, and value is bigger to illustrate that dynamic is stronger, passage environmental information element at any time
Concentration is bigger, stronger to the attraction of army antenna；Indicate the gridding information element concentration value added in coordinate (x, y)；
S223, Pheromone update is convened for discovery target
Its Pheromone update mechanism is as follows:
Wherein,It indicates in the gridding information element concentration value added that coordinate is (x, y),Indicate that gridding information element is dense
Pad value is spent, enhancing degree and attenuation degree are spread centered on food source to surrounding；σ is diffusion coefficient, is characterized to extending out
Scattered range；αgAnd αdThe enhancing coefficient and attenuation coefficient for respectively convening Pheromone update, take between (0,1)；d((x,y),
(xF(t),yFIt (t)) is) distance of some food source F for finding in t moment to the grid that coordinate is (x, y)；
S23, army antenna cluster group's behavior model based on limited distance information exchange mechanism
Pheromones include two kinds of information: catalysis pheromonesWith inhibition pheromones
The information prime information sent when some UAV is in some grid generation pheromones or by communications reception to other UAV
When, pheromone concentration of the UAV at the grid be catalyzed pheromones with inhibition pheromones be superimposed and:
It is that information transmitting is carried out by pheromones between army antenna individual, the transmitting of information is also a Ma Erke in army antenna
Husband's process:
DA(t+1)=∑neighbour(A)DA(t) (7)
Wherein DA(t) the food source information that army antenna individual A is carried in t moment is indicated, neightbour (A) indicates army antenna
The army antenna individual collections of the pheromones of A can be perceived around body A；
Step 3: distributed multiple no-manned plane collaboratively searching location tasks planning
The mapping relations of S31, biocenose behavioural characteristic and task scene
Multiple no-manned plane cluster is analogized into intelligent body army antenna ant colony, ant colony is made of army antenna individual cell one by one, often
It can be by interacting and cooperating based on information prime information between a army antenna unit and other army antenna units；
S32, the collaboratively searching location tasks frame based on the pheromones coordination system
To target carry out collaboratively searching positioning during, unmanned plane node by neighborhood interactive realize grouping, DPD position and
Information sketch map updates, then according to respective information sketch map as the subsequent movement of action guide progress；Due to using distributed
Structure, without central node, each unmanned plane can not carry out global information element perception, can only perceive partial information, pass through letter
The Local Interaction of breath element realizes the indirect coordination perception of global information；Specifically, the collaboratively searching based on the pheromones coordination system
Position fixing process includes the update for moving decision process and information sketch map.
2. it is according to claim 1 a kind of based on the heuristic intelligent unmanned plane cluster co-located method of army antenna,
It is characterized in that: it is as follows to move decision process in the step S32:
Step 321: the position of the highest point of pheromone concentration on the information sketch map of each unmanned plane node oneself is calculated；
Step 322: the highest point of pheromone concentration is calculated to the gravitation of the unmanned plane node, and is determined one under the unmanned plane
Walk the target to be positioned；
Step 323: do you judge that each unmanned plane nodal point separation is less than minimum safe distance when the distance of the target of prelocalization it is,
One is then given perpendicular to present node to the power in target point direction, allows to then carry out Step around target movement
324；It is no, then directly carry out Step 324；
Step 324: judge other nodes in each its neighborhood of unmanned plane nodal point separation and each target perceived away from
From whether less than minimum safe distance it is then to calculate its repulsion to current unmanned plane node, then carries out Step 325；It is no,
Then directly carry out Step 325；
Step 325: the power being calculated in the above process is added to obtain the power of guidance unmanned plane movement, and then is calculated
Obtain the acceleration of unmanned plane joint movements, the position of speed and next step.
3. it is according to claim 1 a kind of based on the heuristic intelligent unmanned plane cluster co-located method of army antenna,
Be characterized in that: the renewal process of information sketch map is as follows in the step S32:
Step 326: each unmanned plane node calculates in neighborhood according to current latest position for which unmanned plane node, and with
This communication range as the unmanned plane in this step-length；
Step 327: each unmanned plane node updates the routing information in information sketch map according to this step paths traversed；
Step 328: it is general that according to the target currently to be positioned new target is calculated by DPD algorithm in each unmanned plane node
Rate Density Distribution；
Step 329: then carry out neighborhood in information exchange, each unmanned plane node obtain it is that oneself is perceived or other
Most exact probability Density Distribution in the unmanned plane neighborhood for the target that unmanned plane perceives, and contain other sections in neighborhood
The routing information of point movement routine；
Step 330: finally the destination probability density of each unmanned plane node is distributed and is merged with routing information, and foundation
Pheromone update model, i.e. formula (1)-(7) are updated, and obtain the newest information sketch map of each node.
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