CN108876086A - A kind of multiple no-manned plane method for allocating tasks based on particle swarm algorithm - Google Patents
A kind of multiple no-manned plane method for allocating tasks based on particle swarm algorithm Download PDFInfo
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Abstract
The invention belongs to unmanned plane task distribution domain variabilities to disclose a kind of multiple no-manned plane method for allocating tasks based on particle swarm algorithm, first, scale, Inertia Weight, the maximum allowable the number of iterations of Initialize installation microparticle population, then the task mating of random initial unmanned plane and target is generated for each particle, and calculates objective appraisal function；Then the optimal value of individual and group is found out again, and update each particle, according to particle swarm optimization algorithm, update traversal all particles, compare optimal X pbest and the optimal X gbest of group individual in all microparticle populations in group, if the current optimal X pbest of certain particle, enabling the current optimal X pbest of the particle is the whole optimal X gbest of group, and the current optimal X pbest for saving the particle is the whole optimal X gbest of group；The method of the present invention optimizing ability is strong, simple general-purpose, strong robustness, and compared with the prior art, it is realized and the method for operation is more simple, effectively increases the science of multiple no-manned plane task distribution.
Description
Technical field
The present invention relates to unmanned plane task allocation technique fields, and in particular to a kind of multiple no-manned plane based on particle swarm algorithm
Method for allocating tasks.
Background technique
Cotasking distribution is one of the key technology in the planning of multiple no-manned plane cotasking, can be according to obtained task
The relevant information for executing region, provides task-performance instructions sequence for multiple UAVs, corresponding unmanned plane is assigned accordingly to be appointed
Business.It is distributed by unmanned plane cotasking, preparatory offline task distribution can be carried out before task execution, can use and appoint
The global information in business region is provided for unmanned plane execution task and is preferably carried into execution a plan.
The model solution under simple target function is mainly concentrated on to the research of unmanned plane cotasking distribution at present, by
Multiple simple unmanned planes form group system and complete single unmanned plane and be unable to complete or be difficult to the work completed by cooperateing with, cooperating
Make, has become the widespread consensus of various countries researcher.Therefore, multiple no-manned plane systematic research is also received significant attention, it is more
Unmanned plane can realize a variety of combat duties, and basic working condition includes four kinds：1) dbjective state is searched for：In accordance with advance planning
Searching route flight, it is non-to look for ground target；2) dbjective state is identified：The true mesh of target type is judged using Target Recognition Algorithms
Mark, the target hit still temporarily can not definitely judge the target of type；3) target of attack state：For having been previously identified as
Genuine target, unmanned plane enter the intersection stage, implement to attack to it：4) evaluation stage is injured：Whether judge the target attacked
It is also equipped with fighting capacity.It, can be reasonably by unmanned plane with optimal task shape for the target that unmanned plane is found at a not moment
Can it be the key that play multiple no-manned plane collaborative work efficiency that state distributes to most suitable target, both at home and abroad to the research of the problem
Have become hot spot, generally use network optimal models method, multiple agent method, taboo search method at present or uses mixing
Integer linear rule and method carry out task allocation activities, but method in the prior art more or less exist it is computationally intensive, disappear
The disadvantage that time-consuming is more, robustness is poor.Therefore, how to improve a kind of optimizing ability it is strong, it is simple general-purpose, strong robustness based on
The multiple no-manned plane method for allocating tasks of particle swarm algorithm is those skilled in the art's technical issues that need to address.
Summary of the invention
The present invention, which is directed to, exists in the prior art the problem that computationally intensive, elapsed time is more, robustness is poor, and provides one
Multiple no-manned plane method for allocating tasks of the kind based on particle swarm algorithm.
The present invention using following technical scheme in order to solve the above technical problems, realized：
A kind of multiple no-manned plane method for allocating tasks based on particle swarm algorithm is designed, is included the following steps：
Step 1：Input unmanned plane quantity Nv, task object quantity Nt, unmanned plane speed V, task object coordinate position
Post=[post 1 ..., post Nt], unmanned plane initial coordinate position Posu=[posu 1 ..., posu Nv], unmanned plane
Scale P, the initialization Inertia Weight β and the maximum allowable the number of iterations maxstep of initialization of initial particle population；Wherein 1 post
The coordinate of first aim is represented, post Nt represents the coordinate of the Nt target, and posu 1 represents first unmanned plane and initially sits
Cursor position, posu Nv represent first unmanned plane initial coordinate position；
Step 2：For each microparticle population, random initial unmanned plane and the not task mating of table are generated, and calculate
Fitness function value calculates all non-domination solutions of current population, and constitutes external population NP, external population with all non-domination solutions
The scale of NP is N；
Step 3：According to formula
υki(t+1)=wpυki(t)+c1r1(pki(t)-Xki(t))+
c2r2(pgi(t)-Xgi(t)),
Xki(t+1)=Xki(t)+υki(t+1) calculates the new speed of each microparticle population and new position, and to each particle kind
The new speed of group carries out amplitude limiting processing；Wherein, wpFor linear decrease weight, c1, c2To learn constant, r1, r2For between 0 to 1
Random number；Pgi(t) optimal location occurred by all microparticle populations to current position；
Step 4：By in initial particle population w-th individual itself be used as its optimal solution Xpbest, w, w=1,2 ..., P,
The more closer more outstanding principle of distance according to non-domination solution away from origin, determines the optimal X gbest of group；
Step 5：Judge whether current iteration number step is equal to maximum number of iterations maxstep, if current iteration number
Step is equal to maximum number of iterations maxstep and then stops algorithm, exports the external population NP solution final as problem；If current change
Generation number step thens follow the steps six not equal to maximum number of iterations maxstep；
Step 6：According to particle swarm optimization algorithm, traversal all particles are updated；
Step 7：Compare optimal X pbest and the optimal X gbest of group individual in all microparticle populations in group, if
The current optimal X pbest of certain particle, then enabling the current optimal X pbest of the particle is the whole optimal X gbest of group, and is protected
The current optimal X pbest for depositing the particle is the whole optimal X gbest of group；
Step 8：If search the current optimal X pbest of particle X gbest optimal for whole groups in step 8,
It then stops search, and exporting X pbest is the whole optimal X gbest of group；Otherwise, range step 3 continues searching operation.
Preferably, the initialization population is generated using the double-deck coding rule：Firstly, coding code=
[x1 ..., xNv, y 1 ..., y Nt], wherein x represents prerequisite task layer coding, and x1 represents first, prerequisite task layer coding, preceding
Phase task layer encodes total Nv dimension, and is the integer in [0, Nt], represents if 0 without prerequisite task；Y represents assessment
Task layer, y1 represent task layer first coding of assessment, the total Nt dimension of assessment task layer, and are the real numbers in (1, Nv+1),
The integer-bit of real number is to execute the unmanned plane serial number of corresponding goal task, the decimal place size generation of the identical gene position real number of integer
Table executes sequence to target；Then, using Kent chaotic maps to initialization of population.
Preferably, the prerequisite task includes that unmanned plane executes detection, identification, interference and search mission to target.
Preferably, described in search, the position of microparticle population is limited by maximum position and minimum position, if certain particle
Population exceeds the maximum position or minimum position of the dimension in the position that certain is tieed up, then the position of the microparticle population is restricted to the dimension
Maximum position or minimum position, equally, microparticle population hasten also to be limited between maximum speed and minimum speed, specific public
Formula is as follows：Wherein：VmaxFor the maximum speed vector of setting, when particle rapidity mistake
It, can the appropriate velocity vector of correcting time when big.
Preferably, the cost function of unmanned plane task distribution is：
Wherein：xijI target is acted on for j unmanned plane；PiFor the identification probability of target, work as PiWhen falling into different identification confidence regions,
Be determined as respectively can not identification types target, true target, the target attacked；It is corresponding, by C (xij) domain be written as
(Rcost,Acost,Bcost), respectively identification mission cost, strike mission cost and injure assessment task cost again.
A kind of multiple no-manned plane method for allocating tasks based on particle swarm algorithm proposed by the present invention, beneficial effect are：
(1) the method for the present invention uses shaping linear programming method to construct optimal function first, using in gunz algorithm
Particle swarm algorithm finds out optimal solution, and the mutual abnormity of multidimensional variable is adapted to by the successive ignition to speed update and location updating
It is required that realize the Target Assignment of multiple no-manned plane；The method of the present invention clear concept, search speed be fast, is easily achieved/expends
Computing resource is few, can meet the operation requirement of single-chip microcontroller, can efficiently solve how man-machine task distribution using this algorithm and ask
Topic；
(2) the method for the present invention establishes multiple no-manned plane Task Assignment Model, by multiple no-manned plane Task Allocation Problem away from
From factor, angle factor and these three indexs of time factor respectively as the optimization object function of multiple target Task Allocation Problem,
Optimization while realizing these three indexs；
(3) the method for the present invention optimizing ability is strong, simple general-purpose, strong robustness, and compared with the prior art, it is realized and the method for operation
It is more simple, effectively increase the science of multiple no-manned plane task distribution.
Detailed description of the invention
The present invention is described in further detail for embodiment in reference to the accompanying drawing, but does not constitute to of the invention
Any restrictions.
Fig. 1 is the flow diagram of present invention method.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.
Refering to shown in attached drawing 1, a kind of multiple no-manned plane method for allocating tasks based on particle swarm algorithm of the invention, including such as
Lower step：
Step 1：Input unmanned plane quantity Nv, task object quantity Nt, unmanned plane speed V, task object coordinate position
Post=[post 1 ..., post Nt], unmanned plane initial coordinate position Posu=[posu 1 ..., posu Nv], unmanned plane
Scale P, the initialization Inertia Weight β and the maximum allowable the number of iterations maxstep of initialization of initial particle population；Wherein 1 post
The coordinate of first aim is represented, post Nt represents the coordinate of the Nt target, and posu 1 represents first unmanned plane and initially sits
Cursor position, posu Nv represent first unmanned plane initial coordinate position；
Step 2：For each microparticle population, random initial unmanned plane and the not task mating of table are generated, and calculate
Fitness function value calculates all non-domination solutions of current population, and constitutes external population NP, external population with all non-domination solutions
The scale of NP is N；
Step 3：According to formula
υki(t+1)=wpυki(t)+c1r1(pki(t)-Xki(t))+
c2r2(pgi(t)-Xgi(t)),
Xki(t+1)=Xki(t)+υki(t+1) calculates the new speed of each microparticle population and new position, and to each particle kind
The new speed of group carries out amplitude limiting processing；Wherein, wpFor linear decrease weight, c1, c2To learn constant, r1, r2For between 0 to 1
Random number；Pgi(t) optimal location occurred by all microparticle populations to current position；
Step 4：By in initial particle population w-th individual itself be used as its optimal solution Xpbest, w, w=1,2 ..., P,
The more closer more outstanding principle of distance according to non-domination solution away from origin, determines the optimal X gbest of group；
Step 5：Judge whether current iteration number step is equal to maximum number of iterations maxstep, if current iteration number
Step is equal to maximum number of iterations maxstep and then stops algorithm, exports the external population NP solution final as problem；If current change
Generation number step thens follow the steps six not equal to maximum number of iterations maxstep；
Step 6：According to particle swarm optimization algorithm, traversal all particles are updated；
Step 7：Compare optimal X pbest and the optimal X gbest of group individual in all microparticle populations in group, if
The current optimal X pbest of certain particle, then enabling the current optimal X pbest of the particle is the whole optimal X gbest of group, and is protected
The current optimal Xpbest for depositing the particle is the whole optimal X gbest of group；
Step 8：If search the current optimal X pbest of particle X gbest optimal for whole groups in step 8,
It then stops search, and exporting X pbest is the whole optimal Xgbest of group；Otherwise, range step 3 continues searching operation.
The initialization population is generated using the double-deck coding rule：Firstly, coding code=[x1 ..., xNv, y
1 ..., y Nt], wherein x represents prerequisite task layer coding, and x1 represents first, prerequisite task layer coding, prerequisite task layer coding
Total Nv dimension, and be the integer in [0, Nt], it represents if 0 without prerequisite task；Y represents assessment task layer, y1 generation
Table assesses task layer first coding, the total Nt dimension of assessment task layer, and is the real number in (1, Nv+1), the integer-bit of real number
It is the unmanned plane serial number for executing corresponding goal task, target is held in the decimal place size representative of the identical gene position real number of integer
Row sequence；Then, using Kent chaotic maps to initialization of population.The prerequisite task includes that unmanned plane executes spy to target
Survey, identification, interference and search mission.It is described that in search, the position of microparticle population is limited by maximum position and minimum position,
If certain microparticle population exceeds the maximum position or minimum position of the dimension in the position that certain is tieed up, the position of the microparticle population is limited
For the maximum position or minimum position of the dimension, equally, microparticle population hasten also to be limited with maximum speed and minimum speed it
Between, specific formula is as follows：Wherein：VmaxFor the maximum speed vector of setting, work as grain
When sub- speed is excessive, can correcting return appropriate velocity vector.The cost function of the described unmanned plane task distribution is：Wherein：xijI target is acted on for j unmanned plane；PiIt is general for the identification of target
Rate works as PiWhen falling into different identification confidence regions, be determined as respectively can not the targets of identification types, true target, attacked
Target；It is corresponding, by C (xij) domain be written as (Rcost,Acost,Bcost), respectively identification mission cost, attack are appointed again
Business and injures assessment task cost at cost.
The foregoing is only a preferred embodiment of the present invention, but scope of protection of the present invention is not limited thereto,
Anyone skilled in the art in the technical scope disclosed by the present invention, according to the technique and scheme of the present invention and its
Inventive concept is subject to equivalent substitution or change, should be covered by the protection scope of the present invention.
Claims (5)
1. a kind of multiple no-manned plane method for allocating tasks based on particle swarm algorithm, which is characterized in that include the following steps：
Step 1：Input unmanned plane quantity Nv, task object quantity Nt, unmanned plane speed V, task object coordinate position Post=
[post 1 ..., post Nt], unmanned plane initial coordinate position Posu=[posu 1 ..., posu Nv], unmanned plane are initially micro-
Scale P, the initialization Inertia Weight β and the maximum allowable the number of iterations maxstep of initialization of grain population；Wherein post 1 represents
The coordinate of one target, post Nt represent the coordinate of the Nt target, and posu 1 represents first unmanned plane initial coordinate position
It sets, posu Nv represents first unmanned plane initial coordinate position；
Step 2：For each microparticle population, random initial unmanned plane and the not task mating of table are generated, and calculate adaptation
Functional value calculates all non-domination solutions of current population, and constitutes external population NP with all non-domination solutions, external population NP's
Scale is N；
Step 3：According to formula
υki(t+1)=wpυki(t)+c1r1(pki(t)-Xki(t))+c2r2(pgi(t)-Xgi(t)),
Xki(t+1)=Xki(t)+υki(t+1).
The new speed of each microparticle population and new position are calculated, and amplitude limiting processing is carried out to the new speed of each microparticle population；Wherein,
wpFor linear decrease weight, c1, c2To learn constant, r1, r2For between 0 to 1 random number；Pgi(t) it is arrived for all microparticle populations
The optimal location that current position occurs；
Step 4：It regard w-th of individual itself in initial particle population as its optimal solution X pbest, w, w=1,2 ..., P, root
The more closer more outstanding principle of distance according to non-domination solution away from origin, determines the optimal X gbest of group；
Step 5：Judge whether current iteration number step is equal to maximum number of iterations maxstep, if current iteration number step
Then stop algorithm equal to maximum number of iterations maxstep, exports the external population NP solution final as problem；If current iteration time
Number step thens follow the steps six not equal to maximum number of iterations maxstep；
Step 6：According to particle swarm optimization algorithm, traversal all particles are updated；
Step 7：Compare optimal X pbest and the optimal X gbest of group individual in all microparticle populations in group, if certain is micro-
The current optimal X pbest of grain, then enabling the current optimal X pbest of the particle is the whole optimal X gbest of group, and saving should
The current optimal X pbest of particle is the whole optimal X gbest of group；
Step 8：If search the current optimal X pbest of particle X gbest optimal for whole groups in step 8, stop
It only searches for, and exporting X pbest is the whole optimal X gbest of group；Otherwise, range step 3 continues searching operation.
2. a kind of multiple no-manned plane method for allocating tasks based on particle swarm algorithm according to claim 1, which is characterized in that
The initialization population is generated using the double-deck coding rule：Firstly, coding code=[x1 ..., xNv, y 1 ..., y
Nt], wherein x represents prerequisite task layer coding, and x1 represents first, prerequisite task layer coding, and prerequisite task layer encodes total Nv dimension,
And be the integer in [0, Nt], it represents if 0 without prerequisite task；Y represents assessment task layer, and y1 represents assessment and appoints
First, layer coding of business, the total Nt dimension of assessment task layer, and be the real number in (1, Nv+1), the integer-bit of real number is execution pair
The unmanned plane serial number of goal task is answered, the decimal place size representative of the identical gene position real number of integer executes sequence to target；
Then, using Kent chaotic maps to initialization of population.
3. a kind of multiple no-manned plane method for allocating tasks based on particle swarm algorithm according to claim 1, which is characterized in that
The prerequisite task includes that unmanned plane executes detection, identification, interference and search mission to target.
4. a kind of multiple no-manned plane method for allocating tasks based on particle swarm algorithm according to claim 1, which is characterized in that
It is described in search, the position of microparticle population is limited by maximum position and minimum position, if certain microparticle population is in the position that certain is tieed up
Maximum position or minimum position beyond the dimension are set, then the position of the microparticle population is restricted to the maximum position or minimum of the dimension
Position, equally, microparticle population hasten also to be limited between maximum speed and minimum speed, and specific formula is as follows：Wherein：VmaxIt can when particle rapidity is excessive for the maximum speed vector of setting
Appropriate velocity vector is returned in correcting.
5. a kind of multiple no-manned plane method for allocating tasks based on particle swarm algorithm according to claim 1, which is characterized in that
The cost function of the described unmanned plane task distribution is：Wherein：xijFor j without
It is man-machine to act on i target；PiFor the identification probability of target, work as PiWhen falling into different identification confidence regions, being determined as respectively can not
The target of identification types, true target, the target attacked；It is corresponding, by C (xij) domain be written as (Rcost,Acost,
Bcost), respectively identification mission cost, strike mission cost and injure assessment task cost again.
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| CN109872009A (en) * | 2019-03-14 | 2019-06-11 | 西安电子科技大学 | A kind of interference increment method method for improving particle swarm algorithm |
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| CN110889625A (en) * | 2019-11-25 | 2020-03-17 | 航天时代飞鸿技术有限公司 | Task planning method for swarm unmanned aerial vehicle system |
| CN113325129A (en) * | 2021-04-20 | 2021-08-31 | 中国计量大学 | Atmospheric pollutant tracing algorithm based on dynamic population |
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| CN109872009A (en) * | 2019-03-14 | 2019-06-11 | 西安电子科技大学 | A kind of interference increment method method for improving particle swarm algorithm |
| CN109901616A (en) * | 2019-03-29 | 2019-06-18 | 北京航空航天大学 | A kind of isomery unmanned aerial vehicle group distributed task scheduling planing method |
| CN110083173A (en) * | 2019-04-08 | 2019-08-02 | 合肥工业大学 | The optimization method of unmanned plane formation patrol task distribution |
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| CN110336637A (en) * | 2019-07-15 | 2019-10-15 | 北京航空航天大学 | A kind of unmanned plane interference signal feature selection approach |
| CN110889625A (en) * | 2019-11-25 | 2020-03-17 | 航天时代飞鸿技术有限公司 | Task planning method for swarm unmanned aerial vehicle system |
| CN113325129A (en) * | 2021-04-20 | 2021-08-31 | 中国计量大学 | Atmospheric pollutant tracing algorithm based on dynamic population |
| CN116954239A (en) * | 2023-06-12 | 2023-10-27 | 成都丰千达科技有限公司 | Unmanned aerial vehicle track planning method and system based on improved particle swarm optimization |
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