CN103777640A - Method for distributed control of centralized clustering formation of unmanned-plane cluster - Google Patents
Method for distributed control of centralized clustering formation of unmanned-plane cluster Download PDFInfo
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Abstract
The invention provides a method for distributed control of centralized clustering formation of an unmanned-plane cluster. The method includes the following steps: 1. determining unmanned-plane-cluster task nature of each task in a task chain and determining a feasible unmanned-plane formation form according to a flying environment; 2. establishing a formation optimization priority set for the unmanned-plane cluster from the feasible formation form and modeling, and determining static or dynamic optimization priority sets of different tasks according to different task targets; 3. according to optimization indexes of the tasks, selecting an optimal task formation or a suboptimum task formation, which complies with performance indexes, from a formation method of the static optimization set or the dynamic optimization set. The method for the distributed control of the centralized clustering formation of the unmanned-plane cluster is capable of efficiently, intelligently and flexibly changing and selecting a coordinative formation of an unmanned-plane cluster and has the advantages of being safe, stable and reliable.
Description
Technical field
The present invention relates to formation is bunch centralized multitask unmanned aerial vehicle group evolution and a control field, especially relates to a kind of distributed control unmanned aerial vehicle group and concentrates sub-clustering formation method.
Background technology
Unmanned plane is operational weapon important in modern war, can replace within the specific limits and have man-machine execution Various Complex and dangerous task, but in future war, only depend on the autonomous operation of single frame unmanned plane cannot adapt to complicated battlefield surroundings.Form into columns and can better finish the work and possess effectively collaborative tactful unmanned plane.Because the unmanned aerial vehicle group that multiple UAVs forms can reduce overall flight resistance, making unmanned aerial vehicle group at pneumatic efficiency, strike effect, success ratio, reconnaissance range and evading probability all has lifting.From but unmanned aerial vehicle group can carry out complex task, multitask and complex environment, there is relative large scope of activities, task completes probability and protection.But when carrying out various different task, unmanned aerial vehicle group can exist formation to select and task index optimization problem.First,, when unmanned aerial vehicle group is carried out different task, different formations are selected not only to affect the implementation effect of this task, and can produce extra effect to next task in task chain.Particularly, in the time that the same area is carried out different tasks, evolution can make group of planes security improve timely, can also promote the execution efficiency of task.Secondly be, necessary, important sometimes for some bursts, extra event evolution.But the existence that the formation of each task is optimized and the optimization relation of each task optimization and task entirety; It is also current key issue that the formation control of forming into columns for distributed control, fixed sturcture along with whether the execution of task exists can be carried out dynamically adjusting constantly the preferential collection of optimization formation dynamic optimization.Therefore unmanned aerial vehicle group formation becomes one of key issue of can not ignore to the impact of various different tasks.
The control of task to unmanned plane flight pattern is only simply considered in early stage more research, groundwork is generally under the each task condition of supposition, a group of planes is take every unmanned plane as a node, or carry out formation control with lead aircraft, wing plane form, but planning in advance to known task target, thereby make unmanned aerial vehicle group in task, keep some index optimization, for example voyage maximizes etc.At present unmanned aerial vehicle group formation control, evolution mainly concentrates on the research direction that unit is considered separately, is that cluster point is fixed the division of task character or the routeing of semifixed formation from the viewpoint of single frame unmanned plane; Only need to finish the work and carry out the co-ordination of formation for length, wing plane double-click structure on the other hand, and unmanned aerial vehicle group is controlled every bunch in distribution, concentrates the feasibility and the optimization research that under this united state of forming into columns, realize less in bunch.
Summary of the invention
The present invention one of is intended to solve the problems of the technologies described above at least to a certain extent or a kind of useful building mode is at least provided.For this reason, the present invention proposes a kind of distributed control unmanned aerial vehicle group with security, reliability and good operability and concentrate sub-clustering formation method, make under the method unmanned aerial vehicle group can optimum or suboptimum finish the work.Its technical scheme is as described below:
A kind of distributed control unmanned aerial vehicle group is concentrated sub-clustering formation method, comprises the following steps:
1) initial parameter of setting unmanned aerial vehicle group, as group of planes quantity, unmanned plane performance etc.; Determine the task character of each subtask in the task chain of unmanned aerial vehicle group, and determine the permission formation form of unmanned aerial vehicle group in each subtask according to flight environment of vehicle, tentatively form static optimization and preferentially collect;
2) to described unmanned aerial vehicle group, the permission formation form in each subtask is carried out modeling, determine the corresponding network topology structure of each formation form, draw in described task chain the configuration of the preplanned mission of every bunch of unmanned plane and topological model under the environment of each subtask, and therefrom set up formation and change mathematical form, different can the having a significant impact selection mode and the conversion regime of forming into columns of task character of every bunch then taked optimum or suboptimum formation control strategy to the formation form of unmanned aerial vehicle group from entirety to part;
3) in the time there is emergency case in certain subtask, the preferential collection of static optimization is reconstructed, is expanded or shrinks, and select other effective formation forms, preferentially collect thereby form dynamic optimization.
In step 1), the preferential collection of described static optimization comprises that project has: bunch point of the definite unmanned aerial vehicle group of task character, the concrete function of every bunch and movability, rapidity, a group of planes, connect topology, topological transformation strategy.
Further, every bunch of unmanned aerial vehicle group to be the single submanifold of task character or many character composition blend together bunch, and wherein the concrete function of every bunch comprises: I, target detecting; II, fire cover; III, motor-driven for subsequent use; IV, interference and counter-jamming; V, formation coordination and control; VI, bunch point shift; In VII, task, the formation of emergency case is emergent distributes.
The formation control of described unmanned aerial vehicle group is coordinated to control by every bunch, and the wherein communication of control signal, formation strategy can be subject to the restriction of formation topological sum formation sequence; For the emergent strategy of adjusting of the formation that has emergency case, each bunch of non-central unmanned plane also can participate in controlling and coordinating, and the concrete formation of every bunch can be limited by the disaggregation of static priority; When structure dynamic optimization preferentially collects, between some node, without directly informational linkage, therefore can only comprise and form static some formations of preferentially concentrating, the preferential collection of dynamic optimization is generally the subset of the preferential collection of static optimization; In every bunch bunch, adopt centralized formation method, bunch center bunch in shift by bunch in each unmanned plane according to distribute specific tasks jointly participate in coordinate determine.
The formation control coodination modes of described unmanned aerial vehicle group comprises: I, same task function are coordinated; II, motor-driven coordination for subsequent use; Between III, different task, function conversion is coordinated;
For every bunch of formation mode that comprises multiple-task character, first by bunch in unmanned plane by functional stratification be: I, center key-course; II, goal task enforcement bunch, can choose target strike bunch as target striking type task is the second layer; III, fire cover bunch; IV, interference and counter-jamming bunch; V, target detecting; VI, motor-driven for subsequent use; Secondly, center key-course preferentially bunch in the formation of carrying out task coordinate and processing the preferential collection of static optimization distribute; Again, the unmanned plane of center key-course and high preferential layer can call motor-driven for subsequent use until center key-course is optimized preferential collection dynamically thereby process fast from low to high, reaches the reliable conversion of function and formation; Finally, except center key-course emergency processing emergency case, shift at the function of not bypassing the immediate leadership conversion and control He Cu center;
Every bunch only comprises the formation mode of single task role character, specific tasks are distributed to every bunch according to task character after by each bunch of coordination, center machine in every bunch bunch can be specified flexibly, and adopts layer-management, also can adopt parallel coordination mode process bunch in each unmanned plane task object;
Task character is single each bunch, bunch between coordinate preferentially in same nature each bunch carry out; Task character blend together bunch bunch between Task Switching coordinated to control by formation and each bunch and determine.
The method that forms the preferential collection of dynamic optimization in step 3) is as described below: in the time that the each subtask of task chain can be independent, optimize and carry out according to each subtask, only task is carried out to static optimization; In the time of life period requirement or formation requirement, according to the predistribution mode planning dynamic priority collection of every bunch; Need to have each Cu Hegecu center joint coordination of direct correlation to the formation processing of emergency condition, be that the order that preferentially converts, has direct link bunch point to change between similarity degree maximum before and after formation changes, the shortest, each bunch of congeniality task of each bunch of displacement is carried out according to formation change in topology minimum.
According to method shown in the present, can make unmanned aerial vehicle group under the method to reach concrete optimization index or to answer suboptimum form to finish the work with corresponding formation.
Part of the present invention is optimized form can guarantee that some task index of task obviously improves, and from concrete example of the present invention or practice thus, can confirm mutually.
Accompanying drawing explanation
Fig. 1 is the process flow diagram that distributed control unmanned aerial vehicle group of the present invention concentrates sub-clustering formation to build;
Fig. 2 is task chain formation control flow chart of the present invention;
Fig. 3 is that the group of planes that in the present invention, unmanned aerial vehicle group sub-clustering is formed into columns preferentially collects structure process flow diagram;
Fig. 4 is unmanned aerial vehicle group evolution process flow diagram in the present invention;
Fig. 5 a is the topological structure conversion embodiment 1 of evolution in the present invention;
Fig. 5 b is the topological structure conversion embodiment 2 of evolution in the present invention.
Embodiment
Describe concrete illustrated embodiment of the present invention below in detail, respective flow chart and structure all show in the accompanying drawings.Wherein identical or similar label represents identical or similar ingredient or has the assembly of identical or similar functions from start to finish.Below, by describing the example of accompanying drawing representative, give an example as just the referential of the method, only for explaining the present invention, and can not be interpreted as limitation of the present invention.
Below openly provide a kind of specific concrete example to be described method of the present invention, be only used to more in detail significantly of the present invention open, but this kind of concrete example of simplifying is as just example, can not be limited to this example.The disclosed method of the present invention can provide many different examples and example to be used for realizing the corresponding structure of the present invention, model, assembly wherein and description is set; And corresponding popularization object does not lie in restriction the present invention.In addition, example in the present invention can be extended to numeral corresponding in all the other different examples or alphabetical, this kind take optional network specific digit or letter representation only as simplifying and knowing object, can as restriction or itself not indicate discussed example essence or arrange between relation.Various special processes in example involved in the present invention and material are those of ordinary skills' technological means that commonly use, common.
In description of the invention, except special annotation, its term is broad understanding, as: the connection and instruction information interaction in network topology, can be various possible contacts or connected mode, can direct communication or by means communications such as ground, satellites, understand the concrete meaning of above-mentioned term in example depending on concrete condition.
Corresponding aspect that can more clear and intuitive understanding concrete example of the present invention with reference to accompanying drawing and following description and explanation.In concrete example, only use some concrete, specific embodiment shows principle and the feasibility of this invention, but these should not serve as limitation of the present invention, on the contrary, in concrete example of the present invention, derive, comprise all conversion, modification and equivalent within the scope of spirit and the intension that falls into additional claims.
The distributed control unmanned aerial vehicle group that detailed below with reference to accompanying drawings description shows according to the specific embodiment of the invention is concentrated sub-clustering formation modeling and construction method.
The distributed control unmanned aerial vehicle group of one as shown in Figure 1 concentrates sub-clustering formation construction method to comprise the steps:
Step e 101, judges the permission formation form of unmanned aerial vehicle group according to task character and unmanned aerial vehicle group flight environment of vehicle.
In other document examples, the Collaborative Control that unmanned aerial vehicle group is formed into columns only considers that the different task of the same area carries out disposable formation control, and be confined to the path planning aspect of target detecting, example group of planes unmanned plane negligible amounts wherein, target strike behavior is independent action, as document: the unmanned plane formation control research based on multitask; Or only according to the same task of zones of different, be generally the path plannings such as target strike or target detecting, the highest path optimization of middle index security etc. cruises.But unmanned aerial vehicle group sub-clustering is formed into columns in every bunch containing the formation conversion of multiple UAVs, particularly have the task formation study on the transformation of formation sequence restriction less.In this paper example, formation is collaborative includes but not limited in the centralized sub-clustering pattern of unmanned aerial vehicle group that with conversion project every bunch is single task role character, also can every bunch the unmanned plane hybrid system that is multiple task characters, between task, can independently also can be associated; Or conversion or local transitions are carried out the different task, the pop-up mission etc. that are associated in these two kinds of structures.Here mainly unmanned aerial vehicle group sub-clustering formation mode is studied and built.Comprise the function setting of task to every cluster, the feasibility in each task of task chain of forming into columns, a group of planes corresponding topological structure of forming into columns.Further concrete bunch of formation form of unmanned aerial vehicle group mainly contains: fixing and free formation mode; Control structure adopts fixing formation and hierachical structure in the present invention.Bunch function setting comprises: Task Assigned Policy, adjust strategy, function switching strategy, formation transduction pathway selection strategy, formation optimisation strategy etc.Collaborative Control part is that entirety is controlled formation adjustment aspect, preferentially collects mainly for static formation optimization; Specifically comprise: the restriction of unmanned aerial vehicle group task landform formation, group of planes formation topological structure build and task overall arrangement.
Step e 102, from the each subtask of task chain, list all feasible formation forms according to optimizing collection, and thus unmanned aerial vehicle group is formed into columns and carried out modeling, determine the network topology structure that each formation mode is corresponding, thereby can draw preplanned mission configuration and the topological model of every bunch of unmanned plane under described task environment, and therefrom set up formation conversion mathematical form.(routeing gordian technique research in other documents, based on the Path Planning for Unmanned Aircraft Vehicle of space decomposition network, the tight flight pattern of unmanned plane forms to be controlled, the investigation routeing of unmanned plane, Path Planning for Unmanned Aircraft Vehicle, Path Planning for Unmanned Aircraft Vehicle problem Primary Study), multiple UAVs formation form is mainly to single task role, and as the shortest in voyage is the path planning of index; Only document " unmanned plane formation control research " based on multitask is mentioned multitask, but its example is only for the formation optimization of reconnaissance phase, and the formation of single frame unmanned plane composition is only discussed, and attack part is subsequently that each unmanned plane independently executed the task.First the model that the present invention builds is to be based upon on sub-clustering formation mode, wherein ignores concrete control system, and in multitask, each task is all carried out formation control, and carries out the overall and local optimum of task chain.
Step e 103, set up corresponding formation optimisation strategy according to every bunch of unmanned plane of task setting, and according to limiting factors such as formation sequences between task chain, formation optimization collection is screened, thereby further can from entirety to part, take optimum or suboptimum formation control strategy according to preplanned mission variant in task chain.A group of planes specifically can take the control of overall distribution formula, bunch in centralized control (main You Cu center realize) and be subject to the evolution control model of formation sequence restriction overall formation is coordinated to control.Can from entirety to part, adopt different reasonable control modes for different task chains, as the overall tasks of single task role character such as detect, in its task chain, each subtask is all detecting character, therefore optimization aim can be that voyage minimizes routeing from set overall, now application distribution control just can finish the work require formation disposition.But different for task character, when containing many character tasks such as detectings, early warning, strike, support, need entire and part, be centralized and the optimization of distributed dual control flight pattern, distributed coordination function is the optimization of unmanned aerial vehicle group task chain, formation and adjustment mode that when task character is complicated, task entirety limits; Centralized control functions is that each bunch of task character set adjustment; Formation sequence be limited in the each task evolution of task chain time provide, alternative condition can limit formation adjust strategy.
With reference to Fig. 2, a kind of distributed control unmanned aerial vehicle group concentrates sub-clustering formation control structure to comprise:
Task chain and formation sequence limiting module, for the planning of unmanned aerial vehicle group overall task and the judgement of formation limiting factor;
Task chain integrated planning is optional formation under set task, as when region, the each subtask of task chain apart from each other, even if there is other local task optimization formations, but under certain bullet-loading capacity or fuel oil prerequisite, can only preferentially select fuel-saving type formation for guaranteeing; Formation sequence restriction planning comprises when certain two or more task in task chain, there are communication delay or the transmission limiting factor such as interferences and cannot normally carry out formation adjustment, needing that target is in advance carried out to feasible formation mode and add the restriction of formation sequence to plan.
Space planning and task character module, for the feasible formation judgement of unmanned aerial vehicle group.Task character can preset or provide in advance, as with detecting, detect+cruise, detect+every bunch of task of a given group of planes and feasible formation when the mission modes such as firepower attack.Space planning comprises the environmental restraint in tasks carrying region, whether has high mountain or executes the task in valley etc., and these all limit to some extent to the selection of formation.When task is carry out detection task or Strike task in valley time, loose rank formation must be not attainable formation mode, and therefore task character and space boundary also exist impact to forming into columns.
Control section, preferentially collects for forming formation from feasible formation, thus the formation mode of selection entirety or local optimum, suboptimum.
Wherein, static preferential collection is used for forming into columns and sets up preferential collection, it comprises space planning and task character planning, final static state corresponding to each subtask that forms preferentially collects chain, and initial setting task cluster is distributed and when evolution bunch between conversion, formation sequence limiting module is for switching the emergency processing of planning and emergency case under task formation restrictive condition between task chain, and feed back actual formation to static preferential collection, thereby filter out the front-to-back effect of each formation in task chain, finally form dynamic priority collection.
First by task object in advance, task chain is carried out to preliminary planning and arrangement, can draw geography and the weather condition in the each tasks carrying of task chain area from known information and other data.Can draw like this feasibility formation under environmental restraint; Secondly,, according to each task character, as detection task, hit the task setting that task dispatching carries out bunch each task, two are carried out formation static programming thus, form a static optimization and preferentially collect.
The concrete function of every bunch comprises: I, target detecting; II, fire cover; III, motor-driven for subsequent use; IV, interference and counter-jamming; V, formation coordination and control; VI, bunch point shift; In VII, task, the formation of emergency case is emergent distributes.
The group of planes unmanned plane quantity that appointment is executed the task, provides the preferential execution time of concentrating topological structure, voyage, the maximum of each formation to take the unmanned plane technical parameter indexs such as bullet amount, the maximum angle of climb and some task.Task for the fixing execution time also should be included static programming part in.Now task module, through space planning and task character module, can provide a static optimization by each bunch of coordination of distributed control model and preferentially collect.
The formation control of described unmanned aerial vehicle group is coordinated to control by every bunch, the concrete formation of every bunch is limited by the disaggregation of static optimization priority, in every bunch bunch, adopt centralized formation method, bunch center bunch in shift by bunch in each unmanned plane according to distribute specific tasks jointly participate in coordinate determine.
Because dynamically formation variation and pop-up mission, task chain node task character change, static preferential collection have direct relation.Therefore needing to contact directly each bunch of Dian Yucunei center unmanned plane carries out order adjustment and upgrades static optimization collection constantly.Be that empty this class situation can be concentrated and select optimization formation in dynamic priority for the static preferential collection formation common factor of last task formation selection or front and back task in time series, then judge whether this formation also meets the preferential collection of front and back static state or formation sequence restriction demand; Adopt suboptimum formation and generally can only concentrate in dynamic priority for emergency case, because now optimum formation is difficult to meet emergency case mission requirements.
In an embodiment of the present invention, as the method for building up of the preferential collection of Fig. 3 unmanned aerial vehicle group comprises the steps:
A1) determine task environment and task character;
A2) set up unmanned plane state, comprise in bullet-loading capacity, the maximum angle of climb, ultimate run, minimum flying height, minimum flying distance, maximum angle of turn, flight environment of vehicle that task object enemy threatens;
A3) set static formation optimization collection by task character, space environment and unmanned plane state;
A4) arrange static optimization collection by each task index;
A5) add formation sequence to limit expansion, adjustment or the selection to static optimization collection;
A6) carrying out the each subtask of task global optimization or task chain optimizes;
A7) flight formation is carried out to corresponding formation control and reach correct formation form.
Wherein, steps A 5) in formation sequence limiting factor add static preferential collection, make it be converted into dynamic priority collection.The objective optimization of the final formation of unmanned aerial vehicle group is just concentrated and is selected optimum or suboptimum formation from dynamic priority.Some technical indicators that objective optimization is generally task starts front appointment, unmanned plane minimum number in as the shortest in air route a, group of planes, bullet-loading capacity is minimum or safety coefficient is the most high, therefore optimizes the unique and bounded of collection.
As shown in Figure 4, steps A 5) can also turn in detail following flow process:
A51) start containing the overall task of formation sequence limiting factor;
A52) judge between the task chain of overall task and affected by formation sequence; If so, enter described steps A 53); If not, enter steps A 6);
A53) judge that limiting factor preferentially collects impact to static state; If had, enter described steps A 54), if not, enter steps A 6);
A54) judge that dynamic priority integrates whether as empty set; If so, keep former formation, enter described steps A 7); If not, enter steps A 6);
Various limiting factors described in the present invention can not make the dynamic priority of all tasks integrate as empty set.
Modeling part is for the concrete model determining objective optimization collection.
This segment set has suffered the optional modeling process of formation conversion, comprises static preferential collection model and the dynamic optimization collection model optimized.The collaborative formation model of concrete unmanned plane can be selected according to actual conditions.To discuss in detail static path planning model take optimal-fuel as target and have mentality of designing and the modelling application example of the dynamic task allocation model of formation sequence restriction below.
Static path planning modeling and algorithm are selected:
Consider 5 node topologies as shown in Figure 5 a, each node represents one bunch., establish its motor pattern and be as a set depending on every cluster: r
i, wherein
Represent the position of i bunch, establishing true origin is the center bunch (No. 3 nodes) of forming into columns, and v is every bunch of unmanned plane speed, θ,
for every bunch of relative center course heading, θ is course angle,
for the angle of pitch.So
if air formation is in same plane inner conversion formation, the static preferential collection path planning of structure.Can concentrate each bunch of unmanned plane while finding out evolution to change the shortest path L of distance at static optimization
min,
wherein l
ijfor the distance of bunch exchange, s is the topological node that needs conversion.If cost J is optimized in air route, the topological matrix that the each task of task chain is possible is κ
n={ S
ij, i, j=1,2 ..., 5; For there being m subtask, the minimum value of cost is optimized in its air route
ω
nthe evolution feasibility of expression task; Work as ω
n, represent that the each subtask of n does not exist the formation that can convert at=0 o'clock; Work as ω
nrepresent that this kind of formation is the optimum disposable formation in the each subtask of n at=1 o'clock.
Fly to from the outset first task object and generally can adopt any formation, but consider that fuel oil is economized and first task most in-flight, should select to meet
and
initial formation.
If the task of cruising that first subtask is GENERAL TYPE, economizes according to fuel oil most, start a group of planes and can adopt V font formation; In topology, represent 0 ° and 90 ° of adjacent cluster with " ± 1 ", as each in column bunch is 90 °, and ± 30 ° of " ± 2 " expressions are adjacent, and " ± 3 " represent ± 45 °, and " ± 4 " represent ± 60 °, and " ± 5 " represent that all the other are not more than 90 ° of angles; ?
In the time that second task is detecting, search, I: adopt rank without landform restriction on a large scale; II: have landform restriction to adopt column; If limit without landform in this example, due to
can adopt rank,
When the 3rd task is the offensive task of target, I: general attack adopts trapezoidal formation; II: attack over the ground and adopt wedge shape or column; III: point, line target adopt diamond formation; If offensive mission is generality while attacking over the ground in example of the present invention, can from I, II, select, consider
with the formation of making a return voyage, thus line of wedge should be adopted,
ω in above-mentioned task
n=1, n=1 ..., 3, thus cost function
The conventional algorithm of present stage path planning is divided into traditional classical algorithm and modern algorithm, mainly contains derivative correlation method, method in optimal control, optimizing search, genetic algorithm, artificial neural network etc., and all visual condition is used.Here can adopt based on Voronoi graph search method, path planning is divided with plane domain in task chain tasks carrying region.
5 node topologies shown in Fig. 5 b are discussed, because task exists the restriction of formation sequence under this condition, therefore
not necessarily there is bunch the shortest evolution of displacement, cost function in task
ω
nthere will be ω
n=0 situation, as: task, landform restriction can produce restriction to rank, circular formation in mountain valley etc., or formation conversion has requirement to convert column to rank just to form restriction.In this topology, establishing between second and third task has the conversion of formation sequence to limit, the 3rd task is for there being landform restriction task, task two is detection task, task three is ground task of bombing, now there is not the optimum formation conversion of task chain, consider the suboptimum formation of the task of taking into account two, three, while taking off, still adopt V font to form into columns,
While carrying out first task, adopt line of wedge
Then be the reaching of assurance task, consider two, three task character and restrictive conditions, adopt column can adapt to multiple-task form and comprise and in ground attack, detecting and cloud, returning etc.,
While making a return voyage, still continue to use line of wedge.Cost function corresponding to model thus
it is the suboptimal solution under condition restriction.
Algorithm is the same with upper example still can be used based on Voronoi graph search method, but the region in figure is divided and need to be met restrictive condition.
According to the method for the embodiment of the present invention, the security of unmanned aerial vehicle group can be preferentially improved, and task index can be optimized, there is efficient, stable and reliable advantage.
Should be appreciated that each several part of the present invention can realize with hardware, software or their combination.Those skilled in the art are appreciated that realizing all or part of step that above-described embodiment method carries is can carry out the hardware that instruction is relevant by program to complete, described program can be stored in a kind of computer-readable recording medium, this program, in the time carrying out, comprises step of embodiment of the method one or a combination set of.Storage medium can be ROM (read-only memory), disk or CD etc.
Although illustrated and described embodiments of the invention, for the ordinary skill in the art, be appreciated that without departing from the principles and spirit of the present invention and can carry out multiple variation, modification, replacement and modification to these embodiment, scope of the present invention is by claims and be equal to and limit.
Claims (6)
1. distributed control unmanned aerial vehicle group is concentrated a sub-clustering formation method, comprises the following steps:
1) set the initial parameter of unmanned aerial vehicle group, determine the task character of each subtask in the task chain of unmanned aerial vehicle group, and determine the permission formation form of unmanned aerial vehicle group in each subtask according to flight environment of vehicle, tentatively form static optimization and preferentially collect;
2) to described unmanned aerial vehicle group, the permission formation form in each subtask is carried out modeling, determine the corresponding network topology structure of each formation form, draw in described task chain the configuration of the preplanned mission of every bunch of unmanned plane and topological model under the environment of each subtask, and therefrom set up formation conversion mathematical form, then from entirety to part, the formation form of unmanned aerial vehicle group is taked to optimum or suboptimum formation control strategy;
3) in the time there is emergency case in certain subtask, the preferential collection of static optimization is reconstructed, is expanded or shrinks, and select other effective formation forms, preferentially collect thereby form dynamic optimization.
2. distributed control unmanned aerial vehicle group according to claim 1 is concentrated sub-clustering formation method, it is characterized in that: in step 1), the preferential collection of described static optimization comprises that project has: bunch point of the definite unmanned aerial vehicle group of task character, the concrete function of every bunch and movability, rapidity, a group of planes, connect topology, topological transformation strategy.
3. distributed control unmanned aerial vehicle group according to claim 2 is concentrated sub-clustering formation method, it is characterized in that: every bunch of unmanned aerial vehicle group to be the single submanifold of task character or many character composition blend together bunch, and wherein the concrete function of every bunch comprises: I, target detecting; II, fire cover; III, motor-driven for subsequent use; IV, interference and counter-jamming; V, formation coordination and control; VI, bunch point shift; In VII, task, the formation of emergency case is emergent distributes.
4. distributed control unmanned aerial vehicle group according to claim 1 is concentrated sub-clustering formation method, it is characterized in that: the formation control of described unmanned aerial vehicle group is coordinated to control by every bunch, the concrete formation of every bunch is limited by the disaggregation of static optimization priority, in every bunch bunch, adopt centralized formation method, bunch center bunch in shift by bunch in each unmanned plane according to distribute specific tasks jointly participate in coordinate determine.
5. distributed control unmanned aerial vehicle group according to claim 4 is concentrated sub-clustering formation method, it is characterized in that: the formation control coodination modes of described unmanned aerial vehicle group comprises: I, same task function are coordinated; II, motor-driven coordination for subsequent use; Between III, different task, function conversion is coordinated;
For every bunch of formation mode that comprises multiple-task character, first by bunch in unmanned plane by functional stratification be: I, center key-course; II, goal task enforcement bunch, can choose target strike bunch as target striking type task is the second layer; III, fire cover bunch; IV, interference and counter-jamming bunch; V, target detecting; VI, motor-driven for subsequent use; Secondly, center key-course preferentially bunch in the formation of carrying out task coordinate and processing the preferential collection of static optimization distribute; Again, the unmanned plane of center key-course and high preferential layer can call motor-driven for subsequent use until center key-course is optimized preferential collection dynamically thereby process fast from low to high, reaches the reliable conversion of function and formation; Finally, except center key-course emergency processing emergency case, shift at the function of not bypassing the immediate leadership conversion and control He Cu center;
Every bunch only comprises the formation mode of single task role character, specific tasks are distributed to every bunch according to task character after by each bunch of coordination, center machine in every bunch bunch can be specified flexibly, and adopts layer-management, also can adopt parallel coordination mode process bunch in each unmanned plane task object;
Task character is single each bunch, bunch between coordinate preferentially in same nature each bunch carry out; Task character blend together bunch bunch between Task Switching coordinated to control by formation and each bunch and determine.
6. distributed control unmanned aerial vehicle group according to claim 1 is concentrated sub-clustering formation method, it is characterized in that: the method that forms the preferential collection of dynamic optimization in step 3) is as described below: in the time that the each subtask of task chain can be independent, optimize and carry out according to each subtask, only task is carried out to static optimization; In the time of life period requirement or formation requirement, according to the predistribution mode planning dynamic priority collection of every bunch; Need to have each Cu Hegecu center joint coordination of direct correlation to the formation processing of emergency condition, be that the order that preferentially converts, has direct link bunch point to change between similarity degree maximum before and after formation changes, the shortest, each bunch of congeniality task of each bunch of displacement is carried out according to formation change in topology minimum.
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| CN116449865A (en) * | 2023-03-15 | 2023-07-18 | 中国人民解放军国防科技大学 | Cluster task decomposition method and system for clustered unmanned aerial vehicle based on state awareness |
| CN116449865B (en) * | 2023-03-15 | 2024-03-12 | 中国人民解放军国防科技大学 | Cluster task decomposition method and system for clustered unmanned aerial vehicle based on state awareness |
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