CN107831790A - A kind of Alliance Establishment method of the isomery unmanned plane collaboratively searching strike based on multi-objective genetic algorithm - Google Patents
A kind of Alliance Establishment method of the isomery unmanned plane collaboratively searching strike based on multi-objective genetic algorithm Download PDFInfo
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Abstract
The invention discloses a kind of Alliance Establishment method of the isomery unmanned plane collaboratively searching strike based on multi-objective genetic algorithm, including set up alliance；Establish alliance's Optimized model；The steps such as alliance's Optimized model are solved using multi-objective genetic algorithm.Setting up alliance includes：Unmanned plane enters after mission area to deploy to search for mission area, after unmanned plane finds target, obtains the positional information and resource information of target, and turn into alliance's leader；The positional information of target and resource information are sent to other unmanned planes by leader；Itself earliest arrival time and resource vector are returned to leader by the idle unmanned plane for being carrying out search mission；Leader sets up alliance according to the information of return, and arrival time alliance is sent into allied member；Allied member voluntarily plans that flight path completes target attack according to alliance's arrival time.The present invention can realize that isomery multiple no-manned plane collaboratively searching is hit, and while algorithm real-time is ensured, improve the completion efficiency of task.
Description
Technical field
The present invention relates to a kind of Alliance Establishment side of the isomery unmanned plane collaboratively searching strike based on multi-objective genetic algorithm
Method, belong to unmanned plane collaboration field.
Background technology
The advantages such as unmanned plane has zero injures and deaths, high maneuverability, Stealth Fighter is good, expense is low, are widely used in operation ring
Search strike task in border.But increasingly sophisticated modern battlefield environment is faced, the performance of single rack unmanned plane is by load etc.
Limitation, perform the combat duty of multiple target is had to multi rack isomery unmanned plane collaboration could complete.The unmanned function of multi rack isomery
It is enough effectively to perform that the premise of strike task is searched for multiple target is that carry out effective task distribution between multiple no-manned plane.
For Task Allocation Problem, many scholars have carried out studying and achieving a series of achievements, it is proposed that such as dynamic
Network flow optimization (DNFO), MILP (MILP), multidimensional multiple-choice knapsack problem (MMKP) and multiple no-manned plane association
Solves Task Allocation Problem with models such as multi-task planning problem (CMTAP), contract nets.Generally speaking, the above method is present
The problem of such：1) usually assume that unmanned plane is isomorphism and do not considered in existing unmanned plane Task Assignment Model and method
The consumption of resource, it is not inconsistent with actual operation；2) the method for solving real-time of Task Allocation Problem is not high；3) need to be known a priori by mesh
The information such as target quantity, position.It is unknown uncertain due to environment but multiple UAVs are when being performed in unison with searching for strike task
The information such as property, the quantity of target, position is unknown in advance for unmanned plane.Thus above mentioned problem is to traditional multiple no-manned plane
Method for allocating tasks proposes challenge.
The content of the invention
The present invention is hit the target search in circumstances not known for isomery multiple UAVs, and emphasis considers unmanned plane
Resource constraint, it is proposed that a kind of joint strike strategy for setting up alliance, it is fast to devise a kind of parallel NSGA-II of the high algorithm of real-time
Alliance is found in run-up.
The present invention adopts the following technical scheme that to solve its technical problem：
Printing method is looked into a kind of isomery unmanned plane collaboration based on multi-objective genetic algorithm to be included, and is set up alliance, is established and set up
Alliance's Optimized model, using multi-objective genetic algorithm solving-optimizing model.
The unmanned plane and target carry resource model and represent unmanned plane A with vectori(i=1,2 ..., N) its resource to
Amount is usedRepresent, whereinRepresent unmanned plane AiEntrained pth kind task money
The quantity in source.Target of attack target TjResource vector required for (j=1,2 ..., M) is usedRepresent,
WhereinExpression strikes target TjThe pth kind resource quantity needed.
Described establishment alliance flow, comprises the following steps
Step 1) unmanned plane finds Target Acquisition target location and resource information, and turns into alliance's leader；Leader believes mesh
Breath is sent to other idle unmanned planes；
The idle unmanned plane that step 2) is carrying out search mission returns to itself earliest arrival time and resource vector
Leader；
Step 3) leader sets up alliance according to the information of return, and arrival time alliance is sent into allied member；
Step 4) allied member voluntarily plans that flight path completes target attack according to alliance's arrival time, after the completion of attack
Step 1 is turned to, continues search for target.
Each frame unmanned plane in the alliance coordinates arrival time by Dubins paths, using long Dubins paths,
Dubins paths are made up of one section of straight line and radius for r circular arc, are in main track for two fixed radius r and path length
Sexual intercourse, it can increase or reduce path length by increasing reduction r, when unmanned plane during flying speed is identical, coordinating r makes each frame
The path length of unmanned plane is identical, so as to realize while reach.
Described establishment alliance Optimized model, including two targets, one constraints, note unmanned plane AiStrike target TjGroup
The model built isTarget and constraint are as follows：
(1) resource constraint
The strike to target is completed, for any one resource p, p ∈ { 1,2 ..., n }, the summation of allied member is big
Resource is i.e. needed for strike target：
(2) shortest time completes to strike target
In order to shorten the deadline of strike task, require to complete to attack target within the shortest time after finding target
Hit, each frame unmanned plane in alliance coordinates to reach the object time by adjusting the radius in Dubins paths, and realization reaches simultaneously
Target position, launches a offensive to target together.The arrival time of whole alliance is the unmanned plane by being reached the latest in alliance
Determined, rememberedFor with unmanned plane AiFor leader, objective of the attack TjThe alliance set up, λkFor unmanned plane AkReach target
Shortest time, then allianceArrival target TjTimeIt can be expressed as：
(3) alliance includes minimum frame target
The deadline of search strike task is made up of search time and Impulse time this two parts, if setting up connection every time
All make its scale as small as possible during alliance so that more unmanned planes participate in the search to zone of ignorance, are sent out within the shorter time
Existing target, so as to shorten the deadline of whole search strike task, useRepresent allianceThe frame number of middle unmanned plane.
Constraint and target to sum up is built alliance's Optimized model and can be expressed as：
Described Parallel multi-objective genetic solving-optimizing model, is to have used and is based on a kind of coarse grain parallelism technology
Genetic algorithm (NSGA-II) with elitism strategy non-dominated ranking, NSGA-II algorithms are a kind of for solving multiple-objection optimization
Genetic algorithm, noninferior solution is obtained using quick non-dominated ranking and crowding operator, is merged in generation by father and son and realize that elite retains,
Fig. 2 is the algorithm flow chart.The initial population generated at random is divided into some sub- populations first, each sub- population according to
NSGA-II algorithms are iterated optimizing, using synchronous migration strategy, often by certain iterative algebra when, to adjacent sub- population
Migration sends optimal solution set, and the optimal particle moved into adjacent populations substitutes the worst particle of book population, so as to introduce it
The outstanding gene of his sub- population, quickly enrich the diversity of each sub- population.After algorithm reaches stop condition, merge all sons
Population, non-bad sequence is carried out to whole population, obtains noninferior solution.
Alliance is set up with parallel NSGA-II selection unmanned planes, candidate's unmanned plane set Λ is carried out using binary coding
Coding, 1 represents that the unmanned plane coalizes, and 0 expression unmanned plane is added without alliance.Solved to obtain using NSGA-II algorithms
The Noninferior Solution Set of population, alliance promoter AiMost suitable solution is selected from Noninferior Solution Set as desired and is used as this alliance.
Beneficial effects of the present invention：The collaboration that the present invention solves isomery unmanned plane under circumstances not known is searched and spreads Percussion Problems,
Optimized model is established, and proposes a kind of good Optimized model derivation algorithm of real-time.The present invention can realize that isomery is more
Unmanned plane collaboratively searching is hit, and while algorithm real-time is ensured, improves the completion efficiency of task.
Brief description of the drawings
Fig. 1 is that alliance's leader of the present invention sets up alliance's flow chart；
Fig. 2 is the parallel NSGA-II algorithm flow charts of the present invention；
Fig. 3 is run time figure under the parallel NSGA-II different situations of the present invention；
For the present invention, the unmanned plane under a certain case performs operation flight path profile to Fig. 4.
Embodiment
To describe the technology contents of the present invention, the objects and the effects in detail, below in conjunction with embodiment and coordinate attached
Figure is explained
Unmanned plane enters after mission area and mission area is deployed to search for first, after unmanned plane finds target, obtains mesh
Target positional information and resource information, if the flow that strikes target is as shown in figure 1, owned enough resources are so individually completed
Target is hit, if own resource is insufficient, then as alliance's leader, target is led off an attack by setting up alliance.Alliance is set up
Target information is broadcast to other unmanned planes by Shi Shouxian leaders, after other unmanned planes receive broadcast message, if owned
Any one of required resource, just give a response and return to itself earliest arrival time and Current resource vector；Alliance's leader according to
Optimized model is established after receiving the feedback information of other unmanned planes, and alliance is set up using multi-objective genetic algorithm, if
Alliance group builds up work(, and alliance just is set up into result and arrival time is sent to other each frame unmanned planes, allied member is according to arrival
Time voluntarily path planning, failure otherwise is set up with regard to alliance and is newly broadcasted.
Each frame unmanned plane in the alliance coordinates arrival time by Dubins paths, is to use long Dubins roads
Footpath, Dubins paths are made up of one section of straight line and radius for r circular arc, are in path length for two fixed radius r
Positive linear relationships, it can increase or reduce path length by increasing reduction r, when unmanned plane during flying speed is identical, coordinating r makes
The path length of each frame unmanned plane is identical, and so as to realize while reach, Fig. 4 is unmanned plane during flying path in certain simulation process
Figure, unmanned plane is by adjusting Dubins paths while target target being led off an attack.
Described establishment alliance Optimized model, including two targets, one constraints, note unmanned plane AiStrike target TjGroup
The model built isTarget and constraint are as follows：
(1) resource constraint
The strike to target is completed, for any one resource p, p ∈ { 1,2 ..., n }, the summation of allied member is big
Resource is i.e. needed for strike target：
(2) shortest time completes to strike target
In order to shorten the deadline of strike task, require to complete to attack target within the shortest time after finding target
Hit.Each frame unmanned plane in alliance is coordinated the time for reaching target by adjusting the radius in Dubins paths, and realization is arrived simultaneously
Up to target position, target is launched a offensive together.The arrival time of whole alliance is nobody by being reached the latest in alliance
What machine was determined, noteFor with unmanned plane AiFor leader, objective of the attack TjThe alliance set up, λkFor unmanned plane AkReach target
Shortest time, then allianceArrival target TjTimeIt can be expressed as：
(3) alliance includes minimum frame target
The deadline of search strike task is made up of search time and Impulse time this two parts, if setting up connection every time
All make its scale as small as possible during alliance so that more unmanned planes participate in the search to zone of ignorance, are sent out within the shorter time
Existing target, so as to shorten the deadline of whole search strike task, useRepresent allianceThe frame number of middle unmanned plane, Λ tables
Show candidate's unmanned plane set.
Constraint and target to sum up is built alliance's Optimized model and can be expressed as：
Described Parallel multi-objective genetic solving-optimizing model, is to have used and is based on a kind of coarse grain parallelism technology
Genetic algorithm (NSGA-II) with elitism strategy non-dominated ranking, NSGA-II algorithms are a kind of for solving multiple-objection optimization
Genetic algorithm, noninferior solution is obtained using quick non-dominated ranking and crowding operator, is merged in generation by father and son and realize that elite retains,
Fig. 2 is the algorithm flow chart.Introduce concurrent technique and improve the ageing of algorithm, when Fig. 3 is the operation of algorithm under different situations
Between scheme, completing solution within 2s in algorithm ensure that real-time.The initial population generated at random is divided into several first
Sub- population, each sub- population are iterated optimizing according to NSGA-II algorithms, using synchronous migration strategy, often by certain iteration
During algebraically, optimal solution set is sent to adjacent sub- population migration, and the optimal particle moved into adjacent populations substitutes book population
Worst particle, so as to introduce the outstanding gene of other sub- populations, quickly enrich the diversity of each sub- population.Algorithm reaches stopping bar
After part, merge all sub- populations, non-bad sequence is carried out to whole population, obtains noninferior solution.
Alliance is set up with parallel NSGA-II selection unmanned planes, candidate's unmanned plane set Λ is carried out using binary coding
Coding, 1 represents that the unmanned plane coalizes, and 0 expression unmanned plane is added without alliance.Solved to obtain using NSGA-II algorithms
The Noninferior Solution Set of population, alliance promoter AiMost suitable solution is selected from Noninferior Solution Set as desired and is used as this alliance.
Those skilled in the art of the present technique are it is understood that unless otherwise defined, all terms used herein (including skill
Art term and scientific terminology) with the general understanding identical meaning with the those of ordinary skill in art of the present invention.Also
It should be understood that those terms defined in such as general dictionary should be understood that with the context of prior art
The consistent meaning of meaning, and unless defined as here, will not be explained with the implication of idealization or overly formal.
Above-described embodiment, the purpose of the present invention, technical scheme and beneficial effect are carried out further
Describe in detail, should be understood that the embodiment that the foregoing is only the present invention, be not limited to this hair
It is bright, within the spirit and principles of the invention, any modification, equivalent substitution and improvements done etc., it should be included in the present invention
Protection domain within.
Claims (5)
1. a kind of Alliance Establishment method of the isomery unmanned plane collaboratively searching strike based on multi-objective genetic algorithm, its feature exist
In comprising the following steps：
Step 1：Set up alliance；
Step 2：Establish alliance's Optimized model；
Step 3：Alliance's Optimized model is solved using multi-objective genetic algorithm.
A kind of 2. alliance of isomery unmanned plane collaboratively searching strike based on multi-objective genetic algorithm according to claim 1
Method for building up, it is characterised in that step 1, the establishment alliance comprises the following steps：
Step 1.1：Unmanned plane enters after mission area to deploy to search for mission area, after unmanned plane finds target, obtains mesh
Target positional information and resource information, and turn into alliance's leader；The positional information of target and resource information are sent to it by leader
His unmanned plane；
Step 1.2：Itself earliest arrival time and resource vector are returned to length by the idle unmanned plane for being carrying out search mission
Machine；
Step 1.3：Leader sets up alliance according to the information of return, and arrival time alliance is sent into allied member；It is described
Alliance's arrival time is the shortest reach time needed for the unmanned plane reached the latest in alliance；
Step 1.4：Allied member voluntarily plans that flight path completes target attack according to alliance's arrival time.
A kind of 3. alliance of isomery unmanned plane collaboratively searching strike based on multi-objective genetic algorithm according to claim 1
Method for building up, it is characterised in that step 2, described the step of establishing alliance's Optimized model is：
Step 2.1：Define unmanned plane and carry resource vector needed for resource vector and target of attack；
Unmanned plane AiThe resource vector of (i=1,2 ..., N) isWhereinTable
Show unmanned plane AiThe quantity of entrained pth kind task resource；Target of attack TjResource vector required for (j=1,2 ..., M) isWhereinRepresent target of attack TjThe pth kind resource quantity needed；
Step 2.2：Establish alliance's Optimized model；Alliance's Optimized model includes two targets, one constraints, and target 1 is
Target strike is completed in shortest time, target 2 is that alliance includes minimum frame unmanned plane, and constraint 1 is joined for any resource p
The resource summation of the every frame unmanned plane of alliance is more than the required resource that strikes target；
NoteFor with unmanned plane AiFor leader, objective of the attack TjThe alliance set up, λkFor unmanned plane AkReach target most in short-term
Between, then allianceArrival target TjTimeIt is expressed as：
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Then allianceOptimized model is：
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WhereinRepresent allianceThe frame number of middle unmanned plane, Λ represent candidate's unmanned plane set.
A kind of 4. alliance of isomery unmanned plane collaboratively searching strike based on multi-objective genetic algorithm according to claim 2
Method for building up, it is characterised in that step 1.4, allied member voluntarily plans flight path by Dubins paths.
A kind of 5. alliance of isomery unmanned plane collaboratively searching strike based on multi-objective genetic algorithm according to claim 1
Method for building up, it is characterised in that step 3, the multi-objective genetic algorithm is parallel NSGA-II algorithms.
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Cited By (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108490976A (en) * | 2018-04-28 | 2018-09-04 | 广州亿航智能技术有限公司 | A kind of scheduling system of unmanned plane cluster |
| CN109460059A (en) * | 2018-12-03 | 2019-03-12 | 中国航空工业集团公司洛阳电光设备研究所 | A kind of coordinated two-ship attack occupy-place optimal time bootstrap technique |
| CN111176321A (en) * | 2019-12-27 | 2020-05-19 | 西安羚控电子科技有限公司 | Heterogeneous swarm unmanned aerial vehicle cooperative attack system and method |
| CN111664852A (en) * | 2020-06-15 | 2020-09-15 | 天津大学 | Unmanned aerial vehicle path planning method and device |
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| CN112130586A (en) * | 2020-09-29 | 2020-12-25 | 南京航空航天大学 | Resource tree-based distributed heterogeneous unmanned aerial vehicle alliance forming method |
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| CN112230675A (en) * | 2020-10-19 | 2021-01-15 | 南京航空航天大学 | Unmanned aerial vehicle task allocation method considering operation environment and performance in collaborative search and rescue |
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| CN113175930A (en) * | 2021-03-22 | 2021-07-27 | 西安理工大学 | Multi-unmanned aerial vehicle collaborative track planning method based on MMEA |
| CN113325867A (en) * | 2021-05-21 | 2021-08-31 | 华中科技大学 | Path planning method and device for searching of unmanned aircraft and unmanned aircraft |
| CN115454125A (en) * | 2022-08-31 | 2022-12-09 | 北京瀚景锦河科技有限公司 | Unmanned aerial vehicle strike alliance building method based on iterative updating algorithm |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7693653B2 (en) * | 2004-03-24 | 2010-04-06 | Bbn Technologies Corp | Vehicle routing and path planning |
| CN104881043A (en) * | 2015-04-30 | 2015-09-02 | 南京航空航天大学 | Multi-unmanned-aerial-vehicle intelligent cooperation observe/act method for multiple dynamic targets |
| CN105278542A (en) * | 2015-09-23 | 2016-01-27 | 沈阳航空航天大学 | Counter-attack countermeasure optimal strategy method for multi-unmanned plane cooperative strike task |
| CN105302153A (en) * | 2015-10-19 | 2016-02-03 | 南京航空航天大学 | Heterogeneous multi-UAV (Unmanned Aerial Vehicle) cooperative scouting and striking task planning method |
-
2017
- 2017-09-21 CN CN201710857352.2A patent/CN107831790A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7693653B2 (en) * | 2004-03-24 | 2010-04-06 | Bbn Technologies Corp | Vehicle routing and path planning |
| CN104881043A (en) * | 2015-04-30 | 2015-09-02 | 南京航空航天大学 | Multi-unmanned-aerial-vehicle intelligent cooperation observe/act method for multiple dynamic targets |
| CN105278542A (en) * | 2015-09-23 | 2016-01-27 | 沈阳航空航天大学 | Counter-attack countermeasure optimal strategy method for multi-unmanned plane cooperative strike task |
| CN105302153A (en) * | 2015-10-19 | 2016-02-03 | 南京航空航天大学 | Heterogeneous multi-UAV (Unmanned Aerial Vehicle) cooperative scouting and striking task planning method |
Non-Patent Citations (2)
| Title |
|---|
| 刘重 等: "《未知环境下异构多无人机协同搜索打击中的联盟组建》", 《兵工学报》 * |
| 程聪: "《无人机协同作战任务分配与攻击效能评估技术》", 《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》 * |
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