CN110031004A - Unmanned plane static state and dynamic path planning method based on numerical map - Google Patents
Unmanned plane static state and dynamic path planning method based on numerical map Download PDFInfo
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Abstract
The present invention provides a kind of unmanned plane static state and dynamic path planning method based on numerical map, is related to unmanned plane Path Planning Technique field.This method comprises: building unmanned plane during flying Three-dimensional Numeric Map；Starting, the final position coordinate of unmanned plane during flying are set, unmanned plane path planning constraint condition is initialized；According to the speed of hybrid particle swarm simulated annealing more new particle and position；Draw the global static optimal flight paths of unmanned plane；Judge whether unmanned plane encounters pop-up threats, if so, planning static flight path again according to hybrid particle swarm simulated annealing；Draw unmanned plane overall situation dynamic optimal flight path.This method under quiescent conditions, can quickly cook up accurate global ship trajectory；Pop-up threats are encountered in flight course, can quickly evade threat, are effectively carried out path replanning and are ensured unmanned plane during flying safety.
Description
Technical field
The present invention relates to unmanned plane Path Planning Technique fields, and in particular to a kind of unmanned plane based on numerical map is static
And dynamic path planning method.
Background technique
Unmanned plane (Unmanned Aerial Vehicle, UAV) full name is UAV, it is wireless by receiving
Electricity instruction is flown by process control itself.In 20th century, first unmanned plane is succeeded in developing, then in the form of target drone
Unmanned plane succeed in developing, over time, the Drones for surveillance of multiple instructions process control and Various Complex control shape
The unmanned plane of formula researches and develops success in succession, plays respective effect in different fields.Today's society science and technology is fast-developing,
Military equipment is unmanned especially under modern battlefield environment has become apparent trend.It is continuous complete with aviation flight technology
Kind, the military value of the unmanned plane with autonomous control function afield is more and more obvious, therefore on the following complicated battlefield,
Unmanned plane will become the important component of various countries' military affairs fight strength.
Unmanned plane will complete autonomous sortie, and path planning is one of the key technology firstly the need of solution, previous logical
It is more coarse to cross the route artificially planned by hand, cannot achieve effective anti-effect of dashing forward, it is therefore necessary to by computational
Algorithm that can be excellent realizes that unmanned plane path is finely planned.Unmanned plane path planning is the optimal road found between start-stop position
Diameter mainly needs to consider during path planning following aspects: flight path length, landform, natural weather environment, prestige
Side of body factor etc.[6], rational routes are cooked up under the premise of these comprehensive factors guarantees that its safety during execution task is suitable
Benefit.There are many uncertain factors for the flight environment of vehicle of unmanned plane under normal conditions, so as to cause that can not obtain in initial phase
Exact overall situation flight environment of vehicle information, therefore traditional unmanned plane static state global path planning is unable to reach the reality of current unmanned plane
When war fighting requirement.It is usually at present when encountering threat about unmanned plane active path planning, then selection bypasses pop-up threats
It returns on original static programming route, this processing mode not only will increase the path replanning time, can also increase path
Cost, and then increase oil consumption, mobility is poor.
The research emphasis of path planning was focused primarily on based on geometric unmanned plane path planning searching algorithm in recent years,
It is broadly divided into random search type algorithm and deterministic type searching algorithm, wherein deterministic type searching algorithm includes dynamic programming algorithm, D*
Algorithm, A* algorithm and dijkstra's algorithm, such algorithm have predictable foundation requirement for model to route searching
It is relatively easy, convenient for the realization of Solve problems, but since the path planning of unmanned plane is finally three-dimensional space to be risen to, data
Calculation amount is bigger, will appear multiple shot array situation using deterministic type algorithm, is unfavorable for solution path planning problem, and searches at random
Rope type algorithm has preferable global optimization performance in complicated problem space, and its robustness and concurrency are compared to true
Searching algorithm of being formed has apparent advantage.But flight environment of vehicle becomes increasingly complex at present, requires also to get over to the flight of unmanned plane
Come higher, therefore, further research realizes that more outstanding unmanned plane path planning algorithm completes the unmanned plane road of fast accurate
Diameter planning is still a project with challenge.
Summary of the invention
In view of the problems of the existing technology, the present invention provides a kind of unmanned plane static state based on numerical map and dynamic road
Diameter planing method can quickly cook up accurate global ship trajectory under quiescent conditions；Burst is encountered in flight course
It threatens, can quickly evade threat, effectively carry out path replanning and ensure unmanned plane during flying safety.
To achieve the goals above, a kind of unmanned plane static state and dynamic path planning method based on numerical map, specifically
Steps are as follows:
Step 1: unmanned plane during flying Three-dimensional Numeric Map, detailed process are constructed using numerical map technology are as follows:
Step 1.1: high according to any point landform corresponding to the projection coordinate (x, y) of horizontal plane in 3-dimensional digital landform
Journey value constructs benchmark topographic map；
The formula of the building benchmark landform is as follows:
Wherein, z1(x, y) is any point landform corresponding to the projection coordinate (x, y) of horizontal plane in 3-dimensional digital landform
Height value, a1~a7It is constant coefficient, for controlling the undulating state of benchmark landform；
Step 1.2: according to the height value at numerical map midpoint (x, y), constructing mountain peak topographic map；
The formula of the building mountain peak landform is as follows:
Wherein, z2(x, y) is the height value at numerical map midpoint (x, y), and n is the number on mountain peak in numerical map, hmFor
The height on m-th of simulation mountain peak, (xm, ym) it is m-th of centre coordinate for simulating mountain peak, xsmIt is m-th of simulation mountain peak in x-axis side
To attenuation, ysmMountain peak is simulated in the attenuation in y-axis direction for m-th；
Step 1.3: radar service area domain being equivalent to by the processing of hemisphere landform using the thought of information fusion, establishes radar
Threat modeling；
The formula for establishing threat radar model is as follows:
Wherein, z3(x, y) is the equivalent height value of threat radar at threat radar model midpoint (x, y), (x3, y3, z3) be
The position coordinates of any point, r in 3-dimensional digital landform3For the radius of threat radar range；
Step 1.4: using cube simcity building, establish urban environmental models；
The formula of the urban environmental models is as follows:
Wherein, z4(x, y) is the height value at urban architecture model midpoint (x, y), (x4, y4) it is in urban environmental models
Heart coordinate, length are the lateral length of building in urban environmental models, and width is the vertical of building in urban environmental models
To width, h is the height of building in urban environmental models；
Step 1.5: being threatened using cylindrical body simulation weather, establish weather threat modeling；
The formula of the weather threat modeling is as follows:
Wherein, z5(x, y) is the height value at weather threat modeling midpoint (x, y), r5For the radius of weather threat range,
(x5, y5) it is meteorological threat centre coordinate, hwFor the height of weather threat modeling；
Step 1.6: by the benchmark topographic map of foundation, mountain peak topographic map, threat radar model, urban environmental models and weather
Threat modeling is converted into the data that computer is capable of handling, and the principle of combining information fusion is merged, with forming integrated digital
Shape information equivalence Three-dimensional Numeric Map；
The expression formula of the equivalent Three-dimensional Numeric Map of integrated digital terrain information is as follows:
Z (x, y)=max [z1(x, y)+z2(x, y)+z3(x, y)+z4(x, y)+z5(x, y)]；
Wherein, z (x, y) is the height value at the equivalent Three-dimensional Numeric Map midpoint (x, y) of integrated digital terrain information；
Step 2: importing equivalent Three-dimensional Numeric Map, starting, the final position coordinate of unmanned plane during flying are set, initialize nothing
Man-machine path planning constraint condition carries out global static path planning；
The unmanned plane path planning constraint condition includes minimum flight step-length lmin；Maximum turning angle θ, the maximum angle of climb
Spend β, minimum flight altitude Hmin；
Step 3: initialization hybrid particle swarm simulated annealing parameter, and to the position x of i-th of particleiWith speed viInto
Row random initializtion updates the speed v of i-th of particleiWith position xi；
Step 3.1: initialization hybrid particle swarm simulated annealing parameter, including maximum number of iterations tmax1, population rule
Mould N, higher initial temperature T, attenuation factor, Studying factors c1And c2, and to the position x of i-th of particleiWith speed viIt carries out random first
Beginningization；
Step 3.2: the fitness value f (x of i-th of particle is calculated according to unmanned plane integration schedules evaluation functioni), and pass through
Particle minimum fitness value determines particle individual extreme value pbestWith global extremum gbest；
The formula of the unmanned plane integration schedules evaluation function is as follows:
Wherein, C is path planning objective function, and l is path length, and ∑ δ w (s) is unmanned plane during flying integrate-cost, described
The formula of unmanned plane during flying integrate-cost ∑ δ w (s) is as follows:
∑ δ w (s)=δOwO(s)+δTwT(s)+δRwR(s)+δDwD(s)；
Wherein, wOIt (s) is the oil consumption cost of unmanned plane, wT(s) cost, w are threatened for flight range natural weatherRIt (s) is nothing
The man-machine threat radar cost being subject to, wD(s) cost, δ are threatened for landformOFor oil consumption cost weight coefficient, δTGeneration is threatened for weather
Valence weight coefficient, δRFor threat radar cost weight coefficient, δDCost weight coefficient, and δ are threatened for landformO+δT+δR+δD=1；
Step 3.3: to the speed v of i-th of particle by the way of with compressibility factoriWith position xiIt is updated, obtains more
The speed v of i-th of particle after newi' and position xi′；
The speed v of updated i-th of particlei' and position xi' formula it is as follows:
vi'=χ [vi+c1r1(pbest-xi)+c2r2(gbest-xi)]；
xi'=xi+vi′；
Wherein, r1And r2It is the random number on [0,1],
Step 3.4: according to the speed v of updated i-th of particlei' and position xi', it is updated to calculate i-th of particle
Fitness value f (xi'), and calculate the variable Δ f for updating i-th of the particle fitness value in front and back；
Step 3.5: judge whether the variable Δ f of i-th of particle fitness value is greater than 0, if so, continue step 3.6, if
It is no, then receive the speed v of updated i-th of particlei' and position xi', continue step 4；
Step 3.6: judging whether exp (- Δ f/T) is greater than random number rand (0,1), if so, receiving updated i-th
The speed v of a particlei' and position xi', and according to the updated fitness value f (x of i-th of particlei') more new particle individual extreme value
pbestWith global extremum gbest, continue step 4, if it is not, not updating the speed v of i-th of particle theniWith position xi, continue step 4；
Step 4: executing annealing operation, enable temperature T=α T, wherein α is decay factor；
Step 5: judging whether the current iteration number of hybrid particle swarm simulated annealing is greater than maximum number of iterations
tmax1, if so, the global static optimal flight paths of unmanned plane are drawn after handling by Spline interpolation, if it is not, then exporting
Particle individual extreme value pbestWith global extremum gbest, return step 3.2；
Step 6: initialization hybrid particle swarm artificial fish-swarm algorithm parameter, the minimum value including Linear recurring series
ωminWith maximum value ωmax, particle populations N, particle visual field visual, state change step-length step, population crowding factor delta,
Random repeated attempt number try_number, maximum number of iterations tmax2；
Step 7: judging whether unmanned plane encounters pop-up threats if so, by billboard more new information to next-generation particle
It is updated, plans unmanned plane during flying path again, if it is not, then according to the global static optimal flight paths flight of unmanned plane；
Step 8: judging whether the current iteration number of hybrid particle swarm artificial fish-swarm algorithm is greater than maximum number of iterations
tmax2, unmanned plane overall situation dynamic optimal flight path is drawn if so, handling by Spline interpolation；If it is not, then returning to step
Rapid 7.
Further, the step of being updated by billboard more new information to next-generation particle in the step 7 is as follows:
Step S1: to the speed v of i-th of particleiWith position xiIt is updated, and calculates the adaptation of i-th of particle after update
Angle value f (xi'), take fitness smallest particles state and its value to be given to billboard；
Step S2: respectively looking for food to particle, knocked into the back, being bunched and random behavior；
Step S2.1: setting i-th of particle current state beIts within sweep of the eye random search arrive it is next
A position isThe optimal center situation of population isOptimal particle state is
Step S2.2: judge i-th of particle t for the integration schedules evaluation function value of current stateWhether it is greater than
Its integration schedules evaluation function of random search to next position within sweep of the eyeIf so, according at i-th
Next state that the random search within sweep of the eye in sub- t generation arrivesWith the global extreme point within t generationIt updates
I-th of particle obtains the speed V in i-th of particle t+1 generationt+1 iDWith state Xt+1 iD, continue step S3, if it is not, not updating then
I-th of particle continues step S2.3；
Next position that within sweep of the eye random search of the basis i-th of particle t generation arrivesWith in t
Global extreme point in generationI-th of particle is updated, the speed in i-th of particle t+1 generation is obtainedAnd state
Formula it is as follows:
Wherein,For the speed in i-th of particle t+1 generation under foraging behavior, t is current iteration time
Number, tmax2For maximum number of iterations, r1And r2It is the random number on [0,1],For under foraging behavior
The current state in i-th of particle t+1 generation,For random behavior；
Step S2.3: judge whether be greater than random repetition in the number of random search within sweep of the eye in i-th of particle t generation
Number of attempt try_number, if so, particle is in its field range random flight, if it is not, then i-th particle t generation
The next position of random search within sweep of the eye, return step S2.2；
Step S2.4: judging whether Population status meets crowed condition, if so, return step S2.2, if it is not, then basis
In the optimal center of the population searched within sweep of the eye in i-th of particle t generationWith the global extremum within t generation
PointI-th of particle is updated, the speed in i-th of particle t+1 generation is obtainedAnd stateContinue step S3；
The basis is in the optimal center of the population searched within sweep of the eye in i-th of particle t generationWith
Global extreme point in t generationIt is updated the speed in i-th of particle t+1 generationAnd stateFormula such as
Under:
Wherein,For the speed in i-th of particle t+1 generation under behavior of bunching,
For the current state in i-th of particle t+1 generation under behavior of bunching, nfFor remaining people within sweep of the eye in i-th of particle t generation
The shoal of fish number of work fish；
Step S2.5: judge the integration schedules evaluation function value of i-th of particle current stateWhether optimal grain is less than
The integration schedules evaluation function value of sub- stateIf so, return step S2.2, if it is not, then optimal according to searching
Particle stateWith the global extreme point under current iteration stateI-th of particle is updated, i-th of particle is obtained
The speed V in t+1 generationt+1 iDWith state Xt+1 iD, continue step S3；
The optimal particle state that the basis searchesWith the global extreme point under current iteration state
Update the speed V in i-th of particle t+1 generationt+1 iDWith state Xt+1 iDFormula it is as follows:
Wherein,For the speed in i-th of particle t+1 generation under the behavior of knocking into the back,
For the current state in i-th of particle t+1 generation under the behavior of knocking into the back；
Step S3: in i-th particle t+1 generation, is calculated from the renewal process in t generation to t+1 generation according to i-th of particle
Fitness chooses the speed V in fitness minimum value corresponding i-th of particle t+1 generation in individual behaviort+1 iDWith state Xt+1 iD
The current state of i-th of particle is updated；
Step S4: judging whether the fitness of i-th of particle after updating is less than the fitness of particle in billboard, if so,
The particle state information then updated in billboard carries out, if it is not, not updating the particle state information in billboard then.
Beneficial effects of the present invention:
The present invention proposes a kind of unmanned plane static state and dynamic path planning method based on numerical map, in quiescent conditions
Under, using hybrid particle swarm simulated annealing (PSO-SA algorithm), the algorithm is using simulated annealing kick probability strategy to grain
Swarm optimization optimizes, and can overcome the problems, such as its precocious (local extremum), and can quickly cook up accurate effective nothing
Man-machine route.In the dynamic case, it using the strategy for threatening dynamic route weight-normality to draw is hidden, is advised from threatening point to carry out again path
It draws, in order to improve computational efficiency of the algorithm under three-dimensional emergency case, using hybrid particle swarm artificial fish-swarm algorithm (PSO-AFSA
Algorithm), the strategies such as look for food, knock into the back, bunching which introduces artificial fish-swarm make particle more preferably to select, the algorithm with compared with
Good global convergence performance, can avoid falling into local extremum, quickly hides threat, carry out accurate path replanning, reduce nobody
The total cost of machine flight.
Detailed description of the invention
Fig. 1 is the process of unmanned plane static state and dynamic path planning method based on numerical map in the embodiment of the present invention
Figure；
Fig. 2 is numerical map benchmark topographic map in the embodiment of the present invention；
Fig. 3 is numerical map mountain peak topographic map in the embodiment of the present invention；
Fig. 4 is numerical map radar mockup figure in the embodiment of the present invention；
Fig. 5 is numerical map city model schematic diagram in the embodiment of the present invention；
Fig. 6 is numerical map weather threat modeling schematic diagram in the embodiment of the present invention；
Fig. 7 is the equivalent Three-dimensional Numeric Map of integrated digital terrain information in the embodiment of the present invention；
Fig. 8 is the path planning figure for using the method for the present invention in the embodiment of the present invention under Three-dimensional Numeric Map；
Fig. 9 is in the embodiment of the present invention under the same Three-dimensional Numeric Map environment, and the path planning of algorithms of different is overlooked
Figure；
Wherein, (a) is the path planning top view that the present invention proposes algorithm；(b) it is overlooked for the path planning of AFSA algorithm
Figure；It (c) is the path planning top view of PSO algorithm.
Specific embodiment
It is right in the following with reference to the drawings and specific embodiments in order to be more clear the purpose of the present invention, technical solution and advantage
The present invention is described in further details.Described herein specific examples are only used to explain the present invention, is not used to limit this
Invention.
Based on Three-dimensional Numeric Map, unmanned plane static state and the dynamic route rule that the present embodiment provides a kind of based on numerical map
The method of drawing constructs unmanned plane during flying three-dimensional environment by numerical map technology first under quiescent conditions, sets flight start bit
It sets, then carries out path planning using by the PSO-SA algorithm for introducing SA kick probability strategy, constrained by unmanned plane during flying
Condition determines flight cost, and unmanned plane global static flight path is drawn on the basis of meeting algorithm termination condition；Exist later
Dynamic threats are set on numerical map, using PSO-AFSA algorithm from threatening point position to carry out accurate path replanning, reduce nothing
The total cost of man-machine flight.Detailed process is as shown in Figure 1, include the following steps:
Step 1: unmanned plane during flying Three-dimensional Numeric Map, detailed process are constructed using numerical map technology are as follows:
Step 1.1: high according to any point landform corresponding to the projection coordinate (x, y) of horizontal plane in 3-dimensional digital landform
Journey value constructs benchmark topographic map.
Shown in the formula such as formula (1) of the building benchmark landform:
Wherein, z1(x, y) is any point landform corresponding to the projection coordinate (x, y) of horizontal plane in 3-dimensional digital landform
Height value, a1~a7It is constant coefficient, for controlling the undulating state of benchmark landform.
In the present embodiment, the benchmark shape of building works as a as shown in Fig. 2, showing in Fig. 21To a7Value is followed successively by 10,
0.2,0.1,0.6,1,0.1,0.1 when benchmark topographic features.
Step 1.2: according to the height value at numerical map midpoint (x, y), constructing mountain peak topographic map.
Shown in the formula such as formula (2) of the building mountain peak landform:
Wherein, z2(x, y) is the height value at numerical map midpoint (x, y), and n is the number on mountain peak in numerical map, hmFor
The height on m-th of simulation mountain peak, (xm, ym) it is m-th of centre coordinate for simulating mountain peak, xsmIt is m-th of simulation mountain peak in x-axis side
To attenuation, ysmMountain peak is simulated in the attenuation in y-axis direction for m-th.
In the present embodiment, x can be passed throughsmAnd ysmTo change the gradient on mountain peak surface.The mountain peak of building shaped like Fig. 3 institute
Show, is shown in Fig. 3 as n=4, h=[20,35,25,20], xm=[20,70,20,80], ym=[30,20,70,80],
xsm=[10,15,10,15]；ysmMountain peak model when=[10,15,10,10].
Step 1.3: radar service area domain being equivalent to by the processing of hemisphere landform using the thought of information fusion, establishes radar
Threat modeling.
Shown in the formula for establishing threat radar model such as formula (3):
Wherein, z3(x, y) is the equivalent height value of threat radar at threat radar model midpoint (x, y), (x3, y3, z3) be
The position coordinates of any point, r in 3-dimensional digital landform3For the radius of threat radar range.
In the present embodiment, the threat radar model of foundation is as shown in Figure 4.
Step 1.4: using cube simcity building, establish urban environmental models.
Shown in the formula of the urban environmental models such as formula (4):
Wherein, z4(x, y) is the height value at urban architecture model midpoint (x, y), (x4, y4) it is in urban environmental models
Heart coordinate, length are the lateral length of building in urban environmental models, and width is the vertical of building in urban environmental models
To width, h is the height of building in urban environmental models.
In the present embodiment, the urban environmental models of foundation are as shown in figure 5, unmanned plane can choose according to position in-flight
Laterally bypass or longitudinally across urban environmental models.
Step 1.5: being threatened using cylindrical body simulation weather, establish weather threat modeling.
Shown in the formula such as formula (5) of the weather threat modeling:
Wherein, z5(x, y) is the height value at weather threat modeling midpoint (x, y), r5For the radius of weather threat range,
That is distance of the unmanned plane apart from weather threat center, (x5, y5) it is meteorological threat centre coordinate, hwFor the height of weather threat modeling
Degree.
In the present embodiment, weather threat refers to bad weather phenomenon existing for nature, such as cyclone, sandstorm,
The weather threat modeling of foundation is as shown in fig. 6, unmanned plane generally can all select to threaten mould around weather according to position in-flight
Type.
Step 1.6: by the benchmark topographic map of foundation, mountain peak topographic map, threat radar model, urban environmental models and weather
Threat modeling is converted into the data that computer is capable of handling, and the principle of combining information fusion is merged, with forming integrated digital
Shape information equivalence Three-dimensional Numeric Map.
Shown in the expression formula such as formula (6) of the equivalent Three-dimensional Numeric Map of integrated digital terrain information:
Z (x, y)=max [z1(x, y)+z2(x, y)+z3(x, y)+z4(x, y)+z5(x, y)] (6)
Wherein, z (x, y) is the height value at the equivalent Three-dimensional Numeric Map midpoint (x, y) of integrated digital terrain information.
In the present embodiment, the equivalent Three-dimensional Numeric Map of integrated digital terrain information of formation is as shown in Figure 7, wherein mountain peak
Number n=8, mountain peak centre coordinate be respectively (5,85), (20,180), (25,25), (45,165), (50,85), (65,65),
(85,145), (130,50)；Radar number 1, radar center coordinate is (180,140), radius 40；City model 1, city
City's model center coordinate is (60,60)；Weather threat modeling 1, model center coordinate is (120,30), radius 30；Task
Planning region is 200km × 200km × 0.4km, and planning level region is converted to 200 × 200 seat with 1000: 1 ratio
Mark region, wherein vertical direction uses 1: 1 ratio, i.e. vertical direction maximum coordinate value among indicates 400m.
Step 2: importing equivalent Three-dimensional Numeric Map, starting, the final position coordinate of unmanned plane during flying are set, initialize nothing
Man-machine path planning constraint condition carries out global static path planning.
The unmanned plane path planning constraint condition includes minimum flight step-length lmin；Maximum turning angle θ, the maximum angle of climb
Spend β, minimum flight altitude Hmin。
In the present embodiment, unmanned plane path planning constraint condition: minimum flight step-length l is initializedmin=1, maximum turning angle
θ=45 °, maximum are climbed angle beta=30 °, minimum flight altitude Hmin=1.
Step 3: initialization hybrid particle swarm simulated annealing parameter, and to the position x of i-th of particleiWith speed viInto
Row random initializtion updates the speed v of i-th of particleiWith position xi。
Step 3.1: initialization hybrid particle swarm simulated annealing parameter, including maximum number of iterations tmax1, population rule
Mould N, higher initial temperature T, attenuation factor, Studying factors c1And c2, and to the position x of i-th of particleiWith speed viIt carries out random first
Beginningization.
In the present embodiment, initialization PSO-SA algorithm population size is 100, and annealing parameter is α=0.98, initial temperature
10000, Studying factors are set as c1=c2=[1.8,2.2]；Maximum number of iterations tmax1=100.
Step 3.2: the fitness value f (x of i-th of particle is calculated according to unmanned plane integration schedules evaluation functioni), and pass through
Particle minimum fitness value determines particle individual extreme value pbestWith global extremum gbest；
Shown in the formula such as formula (7) of the unmanned plane integration schedules evaluation function:
Wherein, C is path planning objective function, and l is path length, and ∑ δ w (s) is unmanned plane during flying integrate-cost, described
The formula of unmanned plane during flying integrate-cost ∑ δ w (s) is as follows:
∑ δ w (s)=δOwO(s)+δTwT(s)+δRwR(s)+δDwD(s)；
Wherein, wOIt (s) is the oil consumption cost of unmanned plane, wT(s) cost, w are threatened for flight range natural weatherRIt (s) is nothing
The man-machine threat radar cost being subject to, wD(s) cost, δ are threatened for landformOFor oil consumption cost weight coefficient, δTGeneration is threatened for weather
Valence weight coefficient, δRFor threat radar cost weight coefficient, δDCost weight coefficient, and δ are threatened for landformO+δT+δR+δD=1.
Oil consumption cost w in the present embodiment, during unmanned plane during flyingO(s) it depends primarily on flying distance and flies
Scanning frequency degree, if sharing n node on unmanned plane during flying path, then shown in unmanned plane during flying distance such as formula (8):
Wherein, liIndicate the distance between i-th of unmanned plane path node and i+1 node, it is assumed that unmanned plane during flying
Speed is kept constant, then shown in the fuel cost of unmanned plane such as formula (9):
Wherein, o indicates unit length oil consumption proportionality coefficient.
In the present embodiment, route segment l is calculated for convenienceiThreat cost, this route segment is divided into three parts, respectively
I1, i2, i3, wherein α*Represent the α of certain threat*A threat, such as αTRepresent αTA natural weather threatens point, αRRepresent
αRA threat radar, αDRepresent αDA landform threatens point,Indicate that unmanned plane distance threatens the distance of point.
It will be Thunderstorm Weather with the cylindrical body equivalent process of certain radius and height, it is assumed that dTmaxIndicate the thunderstorm zone of influence
The maximum radius in domain, dTminIndicate that unmanned plane crash probability is 1 area, if it is d that unmanned plane, which threatens the distance at center away from thunderstorm,T,
Then Thunderstorm Weather probability of damage p caused by unmanned planeT(dT) as shown in formula (10):
Natural weather threatens point α known to formula (10)*For route segment liThreat cost such as formula (11) shown in:
Assuming that for route segment liThere are nTA natural weather threatens point, then the weather that this section is subject to always threatensSuch as formula
(12) shown in:
The natural weather in so whole unmanned plane path threatens shown in cost such as formula (13):
It originally is that the landform that unmanned plane is subject in flight course threatens cost w in embodimentDIt (s) mainly include some mountains
The threat cost of the generations such as peak and highland carries out approximate representation using three-dimensional circular cone, and unmanned plane is corresponding in flight course
The mountain peak cross section of height is a circumference, if the radius of corresponding circumference is RD, unmanned plane terrain clearance is h, the maximum on mountain peak
Height is H, and the gradient is θ, and distance of the unmanned plane apart from mountain peak symmetry axis is dD, as h > H, threatened without the concern for landform.
If dDminTo allow minimum distance of the unmanned plane apart from mountain peak landform, when unmanned plane is less than d apart from mountain peak distanceDminWhen, it is believed that nothing
The man-machine probability smashed is 1, correspondingly, when unmanned plane is greater than d apart from mountain peakDmaxWhen, unmanned plane collisionless threatens.Landform is faced upward
Shown in angle (gradient) θ calculation formula such as formula (13):
Unmanned plane is corresponded in flight course shown in the radius calculation formula such as formula (14) of the corresponding circumference in mountain peak cross section:
RD(h)=(H-h)/tan θ (14)
It can be obtained by analyzing above, shown in the collision probability of unmanned plane such as formula (15):
α is threatened according to formula (15) landformTFor route segment liThreat cost such as formula (16) shown in:
Assuming that in route segment liOn there are nDA landform threatens point, then the landform of this section threatens total costSuch as formula (17)
It is shown:
The ground in so whole unmanned plane path is shaped like shown in formula (18):
Radar is that relatively common detecting under actual environment threatens, and has long-range detection, identification and tracking target
Function, by receive own transmission electromagnetic wave echo analyzed, to obtain the position and side in relation to detected target
To etc. information, therefore, in the present embodiment consider the threat radar cost being subject to during unmanned plane during flying.Radar is antiaircraft neck
Domain heavyweight equipment, electromagnetic wave signal meet following signal-to-noise ratio formula (19):
Wherein, PtFor radar transmission power, GrFor receiving antenna gain, GtFor transmitter antenna gain (dBi), σ is that effectively detection is cut
Face area, λ are signal operation wavelength, and K is Boltzmann constant, LmFor the energy loss factor, BnFor filter match bandwidth, Ts
For kelvin rating, dRLength for unmanned plane apart from radar center, under normal conditions Gr=Gt.In order to facilitate analysis, by formula
(19) formula (20) can be obtained by arranging:
Wherein, σ and R be variable, then by remaining system parameter numerical value bring into formula (20) arrange can obtain formula (21):
Under actual environment, for give unmanned plane scattering section area be it is fixed, i.e. σ be a constant value, then
Formula (21) can be further simplified as formula (22):
Wherein, krFor a constant value.In the present embodiment, it is assumed that the scanning range of radar is 360 °, and working range can be with
It is equivalent to a hemisphere, if dRmaxFor the detectable maximum region radius of radar, dRminFor the place that radar will necessarily detect,
Working as unmanned plane apart from radar center O distance according to formula (22) is dRWhen, detection threat probabilities P that unmanned plane is subject toR(dR) can
It is approximately formula (23):
Then particle individual extreme value p is determined by particle minimum fitness value againbestWith global extremum gbest。
Step 3.3: to the speed v of i-th of particle by the way of with compressibility factoriWith position xiIt is updated, obtains more
The speed v of i-th of particle after newi' and position xi′。
The speed v of updated i-th of particlei' and position xi' formula such as formula (24) and formula (25) shown in:
vi'=χ [vi+c1r1(pbest-xi)+c2r2(gbest-xi)] (24)
xi'=xi+vi′ (25)
Wherein, r1And r2It is the random number on [0,1],
Step 3.4: according to the speed v of updated i-th of particlei' and position xi', it is updated to calculate i-th of particle
Fitness value f (xi'), and calculate the variable Δ f for updating i-th of the particle fitness value in front and back.
Step 3.5: judge whether the variable Δ f of i-th of particle fitness value is greater than 0, if so, continue step 3.6, if
It is no, then receive the speed v of updated i-th of particlei' and position xi', continue step 4.
Step 3.6: judging whether exp (- Δ f/T) is greater than random number rand (0,1), if so, receiving updated i-th
The speed v of a particlei' and position xi', and according to the updated fitness value f (x of i-th of particlei') more new particle individual extreme value
pbestWith global extremum gbest, continue step 4, if it is not, not updating the speed v of i-th of particle theniWith position xi, continue step 4.
Step 4: executing annealing operation, enable temperature T=α T, wherein α is decay factor.
Step 5: judging whether the current iteration number of hybrid particle swarm simulated annealing is greater than maximum number of iterations
tmax1, if so, the global static optimal flight paths of unmanned plane are drawn after handling by Spline interpolation, if it is not, then exporting
Particle individual extreme value pbestWith global extremum gbest, return step 3.2.
Step 6: initialization hybrid particle swarm artificial fish-swarm algorithm parameter, the minimum value including Linear recurring series
ωminWith maximum value ωmax, particle populations N, particle visual field visual, state change step-length step, population crowding factor delta,
Random repeated attempt number try_number, maximum number of iterations tmax2。
In the present embodiment, PSO-AFSA algorithm parameter is initialized, algorithm population size is 100, maximum number of iterations
tmax2100, c1=c2=2, ω=[0.4,0.9], try_number=50, visual=20, δ=0.618, step=5.
Step 7: judging whether unmanned plane encounters pop-up threats, if so, by billboard more new information to next-generation grain
Son is updated, and plans unmanned plane during flying path again, if it is not, the then global static optimal flight paths flight of unmanned plane.
In the present embodiment, dynamic burst threatens path replanning, and pop-up threats, setting burst prestige are arranged on the digital map
The side of body threatens for meteorology, and centre coordinate is (160,130,0), this threat is unmanned plane spy when flight is arrived near the position
It measures.
Described the step of being updated by billboard more new information to next-generation particle, is as follows:
Step S1: to the speed v of i-th of particleiWith position xiIt is updated, and calculates the adaptation of i-th of particle after update
Angle value f (xi'), take fitness smallest particles state and its value to be given to billboard.
Step S2: respectively looking for food to particle, knocked into the back, being bunched and random behavior.
Step S2.1: setting i-th of particle current state beIts within sweep of the eye random search arrive it is next
A position isThe optimal center situation of population isOptimal particle state is
Step S2.2: judge i-th of particle t for the integration schedules evaluation function value of current stateWhether it is greater than
Its integration schedules evaluation function of random search to next position within sweep of the eyeIf so, according at i-th
Next state that the random search within sweep of the eye in sub- t generation arrivesWith the global extreme point within t generationIt updates
I-th of particle obtains the speed V in i-th of particle t+1 generationt+1 iDWith state Xt+1 iD, continue step S3, if it is not, not updating then
I-th of particle continues step S2.3.
Next position that within sweep of the eye random search of the basis i-th of particle t generation arrivesWith in t
Global extreme point in generationUpdate the speed V in i-th of particle t+1 generationt+1 iDWith state Xt+1 iDFormula such as formula (26)
With shown in formula (27):
Wherein,For the speed in i-th of particle t+1 generation under foraging behavior, t is current iteration time
Number, tmax2For maximum number of iterations, r1And r2It is the random number on [0,1],For under foraging behavior
The current state in i-th of particle t+1 generation,For random behavior.
Step S2.3: judge whether be greater than random repetition in the number of random search within sweep of the eye in i-th of particle t generation
Number of attempt try_number, if so, particle is in its field range random flight, if it is not, then i-th particle t generation
The next position of random search within sweep of the eye, return step S2.2；
Step S2.4: judging whether Population status meets crowed condition, if so, return step S2.2, if it is not, then basis
In the optimal center of the population searched within sweep of the eye in i-th of particle t generationWith the global extremum within t generation
PointI-th of particle is updated, the speed V in i-th of particle t+1 generation is obtainedt+1 iDWith state X+1t iD, continue step S3.
It is described to judge whether Population status meets shown in the formula such as formula (28) of crowed condition:
Wherein, nfFor the shoal of fish number of remaining Artificial Fish within sweep of the eye in i-th of particle t generation, if formula (28) is set up,
Then illustrate that Population status meets crowed condition, is otherwise unsatisfactory for.
The basis is in the optimal center of the population searched within sweep of the eye in i-th of particle t generationWith
Global extreme point in t generationIt is updated the speed V in i-th of particle t+1 generationt+1 iDWith state X+1t iDFormula such as formula
(29) and shown in formula (30):
Wherein,For the speed in i-th of particle t+1 generation under behavior of bunching,
For the current state in i-th of particle t+1 generation under behavior of bunching.
Step S2.5: judge the integration schedules evaluation function value of i-th of particle current stateWhether optimal grain is less than
The integration schedules evaluation function value of sub- stateIf so, return step S2.2, if it is not, then optimal according to searching
Particle stateWith the global extreme point under current iteration stateI-th of particle is updated, i-th of particle is obtained
The speed V in t+1 generationt+1 iDWith state Xt+1 iD, continue step S3.
The optimal particle state that the basis searchesWith the global extreme point under current iteration state
Update the speed V in i-th of particle t+1 generationt+1 iDWith state Xt+1 iDFormula such as formula (31) and formula (32) shown in:
Wherein,For the speed in i-th of particle t+1 generation under the behavior of knocking into the back,
For the current state in i-th of particle t+1 generation under the behavior of knocking into the back.
Step S3: in i-th particle t+1 generation, is calculated from the renewal process in t generation to t+1 generation according to i-th of particle
Fitness chooses the speed V in fitness minimum value corresponding i-th of particle t+1 generation in individual behaviort+1 iDWith state Xt+1 iD
The current state of i-th of particle is updated.
Step S4: judging whether the fitness of i-th of particle after updating is less than the fitness of particle in billboard, if so,
The particle state information in billboard is then updated, if it is not, not updating the particle state information in billboard then.
Step 8: judging whether the current iteration number of hybrid particle swarm artificial fish-swarm algorithm is greater than maximum number of iterations
tmax2, unmanned plane overall situation dynamic optimal flight path is drawn if so, handling by Spline interpolation；If it is not, then returning to step
Rapid 7.
In the present embodiment, schematic three dimensional views such as Fig. 8 of path planning is carried out by using PSO-SA and PSO-AFSA algorithm
It is shown.The unmanned plane of algorithm is used using particle swarm algorithm (PSO algorithm), artificial fish-swarm algorithm (AFSA algorithm) and the present invention
Path planning top view is as shown in figure 9, it is apparent that the method for the present invention can not only complete path planning times from Fig. 9
Business, and the route of precisely brief safety can be cooked up, planning effect is substantially better than PSO and AFSA algorithm.
Finally, it should be noted that the above embodiments are merely illustrative of the technical solutions of the present invention, rather than its limitations；Although
Present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that；It still may be used
To modify to technical solution documented by previous embodiment, or some or all of the technical features are equal
Replacement；Thus these are modified or replaceed, defined by the claims in the present invention that it does not separate the essence of the corresponding technical solution
Range.
Claims (6)
1. a kind of unmanned plane static state and dynamic path planning method based on numerical map, which comprises the following steps:
Step 1: unmanned plane during flying Three-dimensional Numeric Map is constructed using numerical map technology；
Step 2: importing equivalent Three-dimensional Numeric Map, starting, the final position coordinate of unmanned plane during flying are set, initialize unmanned plane
Path planning constraint condition carries out global static path planning；
The unmanned plane path planning constraint condition includes minimum flight step-length lmin, maximum turning angle θ, maximum climb angle beta,
Minimum flight altitude Hmin；
Step 3: initialization hybrid particle swarm simulated annealing parameter, and to the position x of i-th of particleiWith speed viCarry out with
Machine initialization, updates the speed v of i-th of particleiWith position xi；
Step 4: executing annealing operation, enable temperature T=α T, wherein α is decay factor；
Step 5: judging whether the current iteration number of hybrid particle swarm simulated annealing is greater than maximum number of iterations tmax1If
It is that the global static optimal flight paths of unmanned plane are drawn after then handling by Spline interpolation, if it is not, then exporting particle individual
Extreme value pbestWith global extremum gbest, return step 3, the speed v of i-th of particle of updateiWith position xi；
Step 6: initialization hybrid particle swarm artificial fish-swarm algorithm parameter, the minimum value ω including Linear recurring seriesminWith
Maximum value ωmax, particle populations N, particle visual field visual, state change step-length step, population crowding factor delta, random weight
Multiple number of attempt try_number, maximum number of iterations tmax2；
Step 7: judge whether unmanned plane encounters pop-up threats, if so, by billboard more new information to next-generation particle into
Row updates, and plans unmanned plane during flying path again, if it is not, then according to the global static optimal flight paths flight of unmanned plane；
Step 8: judging whether the current iteration number of hybrid particle swarm artificial fish-swarm algorithm is greater than maximum number of iterations tmax2If
It is then to be handled by Spline interpolation and draw unmanned plane overall situation dynamic optimal flight path；If it is not, then return step 7.
2. the unmanned plane according to claim 1 based on numerical map is static and dynamic path planning method, feature exist
In, the step 1 the following steps are included:
Step 1.1: according to any point landform altitude corresponding to the projection coordinate (x, y) of horizontal plane in 3-dimensional digital landform
Value constructs benchmark topographic map；
The formula of the building benchmark landform is as follows:
Wherein, z1(x, y) is any point landform altitude corresponding to the projection coordinate (x, y) of horizontal plane in 3-dimensional digital landform
Value, a1~a7It is constant coefficient, for controlling the undulating state of benchmark landform；
Step 1.2: according to the height value at numerical map midpoint (x, y), constructing mountain peak topographic map；
The formula of the building mountain peak landform is as follows:
Wherein, z2(x, y) is the height value at numerical map midpoint (x, y), and n is the number on mountain peak in numerical map, hmFor m
The height on a simulation mountain peak, (xm, ym) it is m-th of centre coordinate for simulating mountain peak, xsmIt is m-th of simulation mountain peak in x-axis direction
Attenuation, ysmMountain peak is simulated in the attenuation in y-axis direction for m-th；
Step 1.3: radar service area domain being equivalent to by the processing of hemisphere landform using the thought of information fusion, establishes threat radar
Model；
The formula for establishing threat radar model is as follows:
Wherein, z3(x, y) is the equivalent height value of threat radar at threat radar model midpoint (x, y), (x3, y3, z3) it is three-dimensional
The position coordinates of any point, r on digital terrain3For the radius of threat radar range；
Step 1.4: using cube simcity building, establish urban environmental models；
The formula of the urban environmental models is as follows:
Wherein, z4(x, y) is the height value at urban architecture model midpoint (x, y), (x4, y4) it is that urban environmental models center is sat
Mark, length are the lateral length of building in urban environmental models, and width is the longitudinal wide of building in urban environmental models
Degree, h are the height of building in urban environmental models；
Step 1.5: being threatened using cylindrical body simulation weather, establish weather threat modeling；
The formula of the weather threat modeling is as follows:
Wherein, z5(x, y) is the height value at weather threat modeling midpoint (x, y), r5For the radius of weather threat range, (x5,
y5) it is meteorological threat centre coordinate, hwFor the height of weather threat modeling；
Step 1.6: the benchmark topographic map of foundation, mountain peak topographic map, threat radar model, urban environmental models and weather are threatened
Model conversation is the data that computer is capable of handling, and the principle of combining information fusion is merged, and forms integrated digital landform letter
Cease equivalent Three-dimensional Numeric Map；
The expression formula of the equivalent Three-dimensional Numeric Map of integrated digital terrain information is as follows:
Z (x, y)=max [z1(x, y)+z2(x, y)+z3(x, y)+z4(x, y)+z5(x, y)]；
Wherein, z (x, y) is the height value at the equivalent Three-dimensional Numeric Map midpoint (x, y) of integrated digital terrain information.
3. the unmanned plane according to claim 1 based on numerical map is static and dynamic path planning method, feature exist
In, the step 3 the following steps are included:
Step 3.1: initialization hybrid particle swarm simulated annealing parameter, including maximum number of iterations tmax1, population scale N, compared with
High initial temperature T, attenuation factor, Studying factors c1And c2, and to the position x of i-th of particleiWith speed viCarry out random initializtion；
Step 3.2: the fitness value f (x of i-th of particle is calculated according to unmanned plane integration schedules evaluation functioni), and pass through particle
Minimum fitness value determines particle individual extreme value pbestWith global extremum gbest；
Step 3.3: to the speed v of i-th of particle by the way of with compressibility factoriWith position xiIt is updated, after obtaining update
I-th of particle speed vi' and position xi′；
Step 3.4: according to the speed v of updated i-th of particlei' and position xi', calculate the updated adaptation of i-th of particle
Angle value f (xi'), and calculate the variable Δ f for updating i-th of the particle fitness value in front and back；
Step 3.5: judge whether the variable Δ f of i-th of particle fitness value is greater than 0, if so, continue step 3.6, if it is not,
Then receive the speed v of updated i-th of particlei' and position xi', continue step 4；
Step 3.6: judging whether exp (- Δ f/T) is greater than random number rand (0,1), if so, receiving updated i-th
The speed v of soni' and position xi', and according to the updated fitness value f (x of i-th of particlei') more new particle individual extreme value pbest
With global extremum gbest, continue step 4, if it is not, not updating the speed v of i-th of particle theniWith position xi, continue step 4.
4. the unmanned plane according to claim 1 based on numerical map is static and dynamic path planning method, feature exist
In the step of being updated by billboard more new information to next-generation particle in the step 7 is as follows:
Step S1: to the speed v of i-th of particleiWith position xiIt is updated, and calculates the fitness value of i-th of particle after update
f(xi'), take fitness smallest particles state and its value to be given to billboard；
Step S2: respectively looking for food to particle, knocked into the back, being bunched and random behavior；
Step S2.1: the current state of i-th of particle of setting is Xt iD, in its next position that random search arrives within sweep of the eye
It is set toThe optimal center situation of population is Xt cD, optimal particle state be Xt maxD；
Step S2.2: judge i-th of particle t for the integration schedules evaluation function value Z (X of current statet iD) whether it is greater than its view
Integration schedules evaluation function of the random search to next position in wild rangeIf so, according in i-th of particle t
Next state that the random search within sweep of the eye in generation arrivesWith the global extreme point X within t generationt gbestDIt updates i-th
Particle obtains the speed V in i-th of particle t+1 generationt+1 iDWith state Xt+1 iD, continue step S3, if it is not, not updating i-th then
Particle continues step S2.3；
Next position that within sweep of the eye random search of the basis i-th of particle t generation arrivesWith t generation in
Global extreme point Xt gbestDI-th of particle is updated, the speed V in i-th of particle t+1 generation is obtained+1t iDWith state X+1t iDPublic affairs
Formula is as follows:
Wherein,For the speed in i-th of particle t+1 generation under foraging behavior, t is current iteration number,
tmax2For maximum number of iterations, r1And r2It is the random number on [0,1],For i-th under foraging behavior
The current state in a particle t+1 generation,For random behavior；
Step S2.3: judge whether be greater than random repeated attempt in the number of random search within sweep of the eye in i-th of particle t generation
Number try_number, if so, particle is in its field range random flight, if it is not, then in the visual field in i-th of particle t generation
The next position of random search in range, return step S2.2；
Step S2.4: judging whether Population status meets crowed condition, if so, return step S2.2, if it is not, then according to the
The optimal center X of the population searched within sweep of the eye in i particle t generationt cDWith the global extreme point within t generation
Xt gbestDI-th of particle is updated, the speed V in i-th of particle t+1 generation is obtainedt+1 iDWith state Xt+1 iD, continue step S3；
The population that within sweep of the eye searches optimal center X of the basis i-th of particle t generationt cDWith in t generation
Interior global extreme point Xt gbestDIt is updated the speed V in i-th of particle t+1 generation+1t iDWith state Xt+1 iDFormula it is as follows:
Wherein,For the speed in i-th of particle t+1 generation under behavior of bunching,For
Bunch the current state in i-th of particle t+1 generation under behavior, nfFor remaining Artificial Fish within sweep of the eye in i-th of particle t generation
Shoal of fish number；
Step S2.5: judge the integration schedules evaluation function value Z (X of i-th of particle current statet iD) whether it is less than optimal particle
The integration schedules evaluation function value Z (X of statet maxD), if so, return step S2.2, if it is not, then according to the optimal grain searched
Sub- state Xt maxDWith the global extreme point X under current iteration statet gbestDI-th of particle is updated, i-th of particle t+ is obtained
The speed V in 1 generationt+1 iDWith state Xt+1 iD, continue step S3；
The optimal particle state X that the basis searchest maxDWith the global extreme point X under current iteration statet gbestDIt updates
The speed V in i-th of particle t+1 generationt+1 iDWith state Xt+1 iDFormula it is as follows:
Wherein,For the speed in i-th of particle t+1 generation under the behavior of knocking into the back,For
Knock into the back the current state in i-th of particle t+1 generation under behavior；
Step S3: the adaptation in i-th of particle t+1 generation is calculated from the renewal process in t generation to t+1 generation according to i-th of particle
Degree chooses the speed V in fitness minimum value corresponding i-th of particle t+1 generation in individual behaviort+1 iDWith state Xt+1 iDTo i-th
The current state of a particle is updated；
Step S4: judging whether the fitness of i-th of particle after updating is less than the fitness of particle in billboard, if so, more
Particle state information in new billboard carries out, if it is not, not updating the particle state information in billboard then.
5. the unmanned plane according to claim 3 based on numerical map is static and dynamic path planning method, feature exist
In the formula of unmanned plane integration schedules evaluation function is as follows in the step 3.2:
Wherein, C is path planning objective function, and l is path length, and ∑ δ w (s) is unmanned plane during flying integrate-cost, it is described nobody
The formula of machine integrated flight cost ∑ δ w (s) is as follows:
∑ δ w (s)=δOwO(s)+δTwT(s)+δRwR(s)+δDwD(s)；
Wherein, wOIt (s) is the oil consumption cost of unmanned plane, wT(s) cost, w are threatened for flight range natural weatherRIt (s) is unmanned plane
The threat radar cost being subject to, wD(s) cost, δ are threatened for landformOFor oil consumption cost weight coefficient, δTCost power is threatened for weather
Weight coefficient, δRFor threat radar cost weight coefficient, δDCost weight coefficient, and δ are threatened for landformO+δT+δR+δD=1.
6. the unmanned plane according to claim 3 based on numerical map is static and dynamic path planning method, feature exist
In the speed v of updated i-th of particle in the step 3.3i' and position xi' formula it is as follows:
vi'=χ [vi+c1r1(pbest-xi)+c2r2(gbest-xi)]；
xi'=xi+vi′；
Wherein, r1And r2It is the random number on [0,1],
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