CN106406346B - A kind of multiple no-manned plane collaboration rapid Cover search path planning method - Google Patents
A kind of multiple no-manned plane collaboration rapid Cover search path planning method Download PDFInfo
- Publication number
- CN106406346B CN106406346B CN201610936002.0A CN201610936002A CN106406346B CN 106406346 B CN106406346 B CN 106406346B CN 201610936002 A CN201610936002 A CN 201610936002A CN 106406346 B CN106406346 B CN 106406346B
- Authority
- CN
- China
- Prior art keywords
- target
- search
- unmanned plane
- covering
- track
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 49
- 230000009977 dual effect Effects 0.000 claims abstract description 24
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 21
- 230000002068 genetic effect Effects 0.000 claims abstract description 13
- 230000000977 initiatory effect Effects 0.000 claims abstract description 3
- 210000000349 chromosome Anatomy 0.000 claims description 77
- 230000000007 visual effect Effects 0.000 claims description 22
- 238000001514 detection method Methods 0.000 claims description 16
- 238000005457 optimization Methods 0.000 claims description 14
- 108090000623 proteins and genes Proteins 0.000 claims description 12
- 230000015572 biosynthetic process Effects 0.000 claims description 6
- 244000025254 Cannabis sativa Species 0.000 claims description 4
- 230000035772 mutation Effects 0.000 claims description 4
- 230000009471 action Effects 0.000 claims description 3
- 238000004364 calculation method Methods 0.000 claims description 3
- 239000002245 particle Substances 0.000 claims description 3
- 229920001228 polyisocyanate Polymers 0.000 claims description 3
- 239000005056 polyisocyanate Substances 0.000 claims description 3
- 230000007115 recruitment Effects 0.000 claims description 3
- 230000006978 adaptation Effects 0.000 claims 1
- 238000006243 chemical reaction Methods 0.000 claims 1
- 238000010586 diagram Methods 0.000 description 7
- 230000008901 benefit Effects 0.000 description 4
- 230000006870 function Effects 0.000 description 4
- 238000012360 testing method Methods 0.000 description 4
- 238000004043 dyeing Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000010353 genetic engineering Methods 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 238000005316 response function Methods 0.000 description 2
- 238000004088 simulation Methods 0.000 description 2
- 239000007787 solid Substances 0.000 description 2
- 206010034719 Personality change Diseases 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 230000000052 comparative effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000003384 imaging method Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 238000010606 normalization Methods 0.000 description 1
- 239000003016 pheromone Substances 0.000 description 1
- 238000011897 real-time detection Methods 0.000 description 1
- 230000001172 regenerating effect Effects 0.000 description 1
- 238000010845 search algorithm Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/10—Simultaneous control of position or course in three dimensions
- G05D1/101—Simultaneous control of position or course in three dimensions specially adapted for aircraft
- G05D1/104—Simultaneous control of position or course in three dimensions specially adapted for aircraft involving a plurality of aircrafts, e.g. formation flying
Landscapes
- Engineering & Computer Science (AREA)
- Aviation & Aerospace Engineering (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)
Abstract
A kind of multiple no-manned plane collaboration rapid Cover disclosed by the invention searches for path planning method, belongs to unmanned aerial vehicle flight path planning field.The present invention is first according to the geometrical characteristic of the prior information of battlefield surroundings and target domain of the existence, gray area is extracted as three kinds of point, line and Area Objects focussing search targets, each focussing search target access sequence is then determined using dual coding countermeasure integer genetic algorithm；Finally it is directed to the allocation order of focussing search target and unmanned plane, unmanned plane is obtained using the path Dubins and greedy strategy and reaches the most short connection track in part that next search target coverage is searched between track initiation point from current goal covering search track end point, the covering search track of next search target is obtained, to obtain multiple no-manned plane collaboration covering search track.The present invention can cook up collaboration rapid Cover search track of the multiple no-manned plane in special gray area, and the cover time is short, and algorithm robustness is high.The present invention effectively can replace region all standing to search for track.
Description
Technical field
The present invention relates to a kind of multiple no-manned plane collaboration rapid Covers to search for path planning method, more particularly to a kind of for tool
There is the multiple no-manned plane collaboration rapid Cover search path planning method of the special gray area of dotted line Area Objects, belongs to unmanned aerial vehicle flight path
Planning field.
Background technique
Unmanned plane (Unmanned Aerial Vehicle, UAV), mobility strong small in size with its, it is cheap, take off
Flexibly, the advantages that no one was injured, air duty guarantee is simple is injured, becomes and executes uninteresting, severe and hot mission optimal selection,
It is widely used in the fields such as battle reconnaissance, air-to-ground attack, mapping and maritime search and rescue, and has played significant role.By
Target search is carried out in the theater of war and intelligence reconnaissance has become the important means of current battle field information acquisition, and in information
Change in battlefield and the scouting before strike is heavily dependent on to the precision strike of target, therefore is able to carry out in unmanned plane
Various tasks in, scout and search mission be the most important combat duty of UAV system.
It is deepened constantly with the development of science and technology with informatization and network war, task constantly aggravates, single unmanned plane
The fighting efficiency that can be played is extremely limited, and can effectively improve search efficiency, contracting by the way of multiple no-manned plane collaboratively searching
Short search time makes up the defect of single unmanned plane.Compared to single rack unmanned plane, multiple no-manned plane is performed in unison with task with many excellent
Gesture.When unmanned plane breaks down or damages, task can be redistributed to avoid mission failure and bungled the chance of winning a battle even broken
Bad entire operation plan；Multiple no-manned plane can also be observed target area from multiple and different orientation, largely keep away
Exempt to omit or lose target；Multiple no-manned plane collaboratively searching and the range of scouting will increase, can be improved according to plan with require to justify
Completely complete the probability of preplanned mission.
For unmanned plane Area Coverage Searching trajectory planning problem, relatively broad research is carried out both at home and abroad at present, has passed
System method is based on search theory, to maximize the angle of Methods for Target Detection Probability, the scouting course of design covering mission area.Separately
It is outer to use the method based on figure, such as probability graph, pheromones view, this method be all one is constructed using certain mechanism can
To reflect the two-dimensional discrete map of target and environmental information, with the progress that unmanned plane is searched for, search graph is constantly updated, this side
Method can efficiently use real-time detection information, be suitble to News Search process.For the totally unknown gray area of information, mainly adopt
With the mode of grid stroke, i.e. along rectilinear flight, in region of search, boundary is turned flies to opposite direction along parallel lines unmanned plane
Row, is so searched for repeatedly, covers entire region to be searched.This way of search unmanned plane number of turns is few, flight straight line
Distance, imaging field pattern distortion feature caused by flight attitude changes when can greatly reduce unmanned plane turning.
Currently, the covering for not having also scholar's research that there is dotted, linear and planar highest priority gray area both at home and abroad
Search for path planning method.
Summary of the invention
Trajectory planning problem is searched for for the multiple no-manned plane collaboration rapid Cover with the special gray area of dotted line Area Objects,
A kind of multiple no-manned plane collaboration rapid Cover disclosed by the invention searches for path planning method, and technical problems to be solved are to provide one
Kind can cook up the rapid Cover search path planning method for meeting flight constraints and particular characteristic for multiple no-manned plane, described
Path planning method has the advantages that the cover time is short, algorithm robustness is high, and the covering search track cooked up can have
Effect replaces region all standing to search for track.
The purpose of the present invention is what is be achieved through the following technical solutions.
A kind of multiple no-manned plane collaboration rapid Cover disclosed by the invention searches for path planning method, first according to battlefield surroundings
Prior information and target domain of the existence geometrical characteristic, gray area is extracted as point target, line target and three kinds of Area Objects
Then the focussing search target of type determines that the access of each focussing search target is suitable using dual coding countermeasure integer genetic algorithm
Sequence.Finally it is directed to the allocation order of focussing search target and unmanned plane, it is contemplated that the constraint of unmanned plane turning radius and solution time
Next search is reached it is required that obtaining unmanned plane using the path Dubins and greedy strategy and covering search track end point from current goal
Rope target coverage searches for the most short connection track in part between track initiation point, obtains the covering search boat of next search target
Mark, to obtain multiple no-manned plane collaboration covering search track, i.e. completion multiple no-manned plane collaboration rapid Cover searches for trajectory planning.
The covering search track of the focussing search target of the three types is divided into the covering search boat of point target type
Track is searched in the covering of mark, line target type, track is searched in the covering of Area Objects type.
Track is searched in the covering of point target type.
For directionless constraint point target, it is only necessary to which unmanned plane field of view center passes through point target and obtains directionless obligatory point
Track is searched in the covering of target；For there is direction to constrain point target, unmanned plane field of view center passes through point target i.e. along specific direction
Track is searched in covering to there is direction to constrain point target.
Track is searched in the covering of line target type.
Since line target length is far longer than unmanned plane visual field width, and line target width is less than unmanned aerial vehicle vision field width
Degree, therefore, the covering search track of line target is the geometry line segment of line target, unmanned plane from any endpoint of line target along
Target geometry line segment direction is searched for another endpoint of line target and completes line target covering search.
Track is searched in the covering of Area Objects type.
Area Objects are all far longer than unmanned plane visual field width on length and width direction, and unmanned plane needs back and forth cover back and forth
Lid search could complete the search to Area Objects.Using the vertex of Area Objects as the covering initial search point of Area Objects, Area Objects
Length or width direction as covering search prime direction, unmanned plane is from starting point and prime direction using reciprocal covering back and forth
Way of search generates different covering search tracks and searches for track to get to the covering of Area Objects.
A kind of multiple no-manned plane collaboration rapid Cover disclosed by the invention searches for path planning method, includes the following steps:
Track is searched in the covering of step 1, point-line-surface special objective.
Unmanned plane target seeker ground visual field is influenced by its flying height, attitude angle and target seeker established angle, by nobody
Machine is considered as particle, and assumes target seeker ground visual field for circle and be located at immediately below unmanned plane, and unmanned plane flies in fixed height
It goes and its ground visual field is not influenced by attitude angle and hypsography.Guiding of the unmanned plane to the detection probability and unmanned plane of target
Head is related to the detection time of target.According to the geometrical characteristic of the prior information of battlefield surroundings and target domain of the existence, by grey
Extracted region is the focussing search target of point target, line target and Area Objects three types.For point target, when unmanned plane visual field
When center passes through target, detection time longest of the unmanned plane to target, detection probability maximum.Covering search track is according to special mesh
Mark type is divided into the covering search track of the covering search track of point target, the covering search track of line target, Area Objects.
Step 1.1: track is searched in the covering of point target.
For directionless constraint point target, it is only necessary to which unmanned plane field of view center passes through point target and obtains directionless obligatory point
Track is searched in the covering of target；For there is direction to constrain point target, unmanned plane field of view center passes through point target i.e. along specific direction
Track is searched in covering to there is direction to constrain point target.
Step 1.2: track is searched in the covering of line target.
Since line target length is far longer than unmanned plane visual field width, and line target width is less than unmanned aerial vehicle vision field width
Degree, therefore, the covering search track of line target is the geometry line segment of line target, unmanned plane from any endpoint of line target along
Target geometry line segment direction is searched for another endpoint of line target and completes line target covering search.
Step 1.3: track is searched in the covering of Area Objects.
Area Objects are all far longer than unmanned plane visual field width on length and width direction, and unmanned plane needs back and forth cover back and forth
Lid search could complete the search to Area Objects.Using the vertex of Area Objects as the covering initial search point of Area Objects, Area Objects
Length or width direction as covering search prime direction, unmanned plane is from starting point and prime direction using reciprocal covering back and forth
Way of search generates different covering search tracks and searches for track to get to the covering of Area Objects.
For Area Objects, there are commonly Z-shaped search and spiral search for the covering search reciprocal back and forth.
Since there are drain sweeps under there are minimum turning radius constraint for simple spiral search pattern, and it is based on Z-shaped search
Mode can without drain sweep complete the covering search of Area Objects, and preferably Z-shaped search, Z-shaped search has different titles, such as grass mower
Formula search, the search of seeder formula, the search of mill ice formula, raster pattern search, scanning line search etc..
Step 2, most short connection track.
The Dynamic Constraints of unmanned plane are reduced to kinematic geometry by its Dynamic Constraints, using Dubins model by unmanned plane
Constraint is learned, and assumes that unmanned plane is flown to avoid collision with constant speed in different height, establishes the kinematics mould of unmanned plane
Type such as formula (1).
Wherein, v is the flying speed of unmanned plane, rminIt is the minimum turning radius of unmanned plane, c is control amount input, if
C=1 represents unmanned plane and turns to the left, and c=-1 represents unmanned plane and bends to right.
Consider unmanned plane turning angle, unmanned plane is from arbitrary initial state (xinitial,yinitial,θinitial) reach any end
End state (xfinal,yfinal,θfinal) track be with minimum turning radius rminFor the combination of the circular arc and straightway of radius.Root
It is constrained according to the SOT state of termination, most short connection track is divided into the path Dubins for having the constraint of terminal direction and endless constraint.For having
The path Dubins of terminal direction constraint, R indicate that target is turned circular arc clockwise, and L indicates that turning circular arc counterclockwise, S indicate straight
Line segment, then the shortest path Dubins is one of D={ RSL, LSR, RSR, LSL, RLR, LRL }.For endless extreme direction
The path Dubins of constraint, most it is short connection track be one section of circular arc or be one section of circular arc and straightway combination, shortest path
Collection is combined into D={ LS, RL, RS, L }.
Step 3 establishes the special gray area covering search Target Distribution Model with dotted line Area Objects.
Covering search Target Assignment is combinatorial optimization problem, and the spy with dotted line Area Objects is established based on combinatorial optimization problem
Different gray area covering search Target Distribution Model, optimization aim are that unmanned plane formation obtains maximum scouting search efficiency, are made
Obtaining unmanned plane formation can use consumption as small as possible to complete covering search mission.The maximum scouting search efficiency embodies
At two aspect of task time-consuming and all unmanned planes time-consuming.
Step three concrete methods of realizing are as follows: assuming that NUFrame unmanned planeNTA packet
Target collection containing point-line-surface three typesThese polyisocyanate structure Target Assignments are formed into columns to unmanned plane
Collaboratively searching, according to formula (2) calculating target function.
Wherein α, β ∈ [0,1] are the weight factors of corresponding sub-goal, and alpha+beta=1.Formula (3) calculates corresponding unmanned plane
Complete time t required for respective search missionu。tuEqual to the detection range of unmanned plane divided by its constant cruising speed vu。
Wherein NV=NU+NTIndicate the node state that unmanned plane is in, w (qi,qj) indicate two node states of unmanned plane it
Between track length, which includes that unmanned plane to the coverings of respective objects searches for track length.
It is binary decision variable, if (qi,qj) represent unmanned plane Uu∈ U is from node state qiReach qj, decision variableDeng
In 1, otherwise it is equal to 0.Formula (4), (5), which limit each target, only needs frame unmanned plane search.
Step 4, single unmanned plane covering search track length computation.
Assuming that certain unmanned plane is assigned m target sequence { T of search1,T2,…,Tm, search track total length includes four
Part, Dubins path length of the first part between starting point and first aim, second part are remaining adjacent target
Between the sum of the path most short Dubins, Part III is that all target coverages search for the sum of track length, and Part IV is
The path length of unmanned plane return starting point.Single unmanned plane covering search track length be aforementioned four part paths length it
With.
Single unmanned plane covering search track length computation concrete methods of realizing is to utilize formula in the step four
(6) it solves unmanned plane and covers searching route length.
Wherein (x0,y0) it is unmanned plane initial position, θ0For the starting velocity direction of unmanned plane, (xj,yj) it is j-th of target
Position, θjIt is unmanned plane in (xj,yj) at direction.d((xj,yj,θj),(xj+1,yj+1,θj+1) it is two node state (xj,yj,
θj) and (xj+1,yj+1,θj+1) between the path Dubins.ljIt is the covering search track length of target j.d((xm,ym,θm),
(x0,y0)) be the last one target correcting action of unmanned plane distance.d((xj,yj,θj),(xj+1,yj+1,θj+1) according to having
Direction constrains point target, directionless constraint point target, line target, the corresponding four kinds of calculation methods of four kinds of target types of Area Objects.By
Four alternative tracks are indicated in appearance, according to greedy strategy, therefrom select most short track as connection track.
Step 5 customizes dual coding chromosome.
The coding mode that the chromosome of genetic algorithm generallys use is binary coding, however in target assignment problem,
Binary coding mode can not intuitively indicate UAV targets' matching relationship, and decimal coded mode on the one hand can be straight
Seeing indicates UAV targets' matching relationship, and another aspect decimal coded mode can satisfy chromosome when carrying out genetic manipulation
Constraint between coding.
The step five customizes the preferred decimal system dual coding chromosome of dual coding chromosome, customization decimal system dual coding dye
Colour solid concrete methods of realizing is that chromosome 1 is used to indicate that the access order of target, chromosome 2 indicate search order spaced points, this
A little spaced points divide chromosome 1 for multiple gene sections, and each gene section will indicate the search target sequences of each unmanned plane.Dyeing
The value range of body 1 is 1,2 ..., NT, gene number is NT, the gene number of chromosome 2 is NU-1.Assuming that there is 10 targets
With 4 frame unmanned planes, chromosome 1 is (1,2,3,4,5,6,7,8,9,10), includes according to the different target assignment problem of chromosome 2
Several typical distribution cases below, the results are shown in Table 1 for the Target Assignment of each unmanned plane under different cases.
Example 1:
Chromosome 1:(1,2,3,4,5,6,7,8,9,10)
Chromosome 2:(3,5,8)
Example 2:
Chromosome 1:(1,2,3,4,5,6,7,8,9,10)
Chromosome 2:(0,5,8)
Example 3:
Chromosome 1:(1,2,3,4,5,6,7,8,9,10)
Chromosome 2:(4,7,10)
Example 4:
Chromosome 1:(1,2,3,4,5,6,7,8,9,10)
Chromosome 2:(4,4,7)
Example 5:
Chromosome 1:(1,2,3,4,5,6,7,8,9,10)
Chromosome 2:(4,4,4)
The Target Assignment result of each unmanned plane of table 1
| Example | UAV1 | UAV2 | UAV3 | UAV4 |
| 1 | (1,2,3) | (4,5) | (6,7,8) | (9,10) |
| 2 | () | (1,2,3,4,5) | (6,7,8) | (9,10) |
| 3 | (1,2,3,4) | (5,6,7) | (8,9,10) | () |
| 4 | (1,2,3,4) | () | (5,6,7) | (8,9,10) |
| 5 | (1,2,3,4) | () | () | (5,6,7,8,9,10) |
Step 6 sets the diversity that changeable exclusive-OR operator increases hereditary variation.Since the chromosome customized in step 5 is
Dual coding chromosome, monotropic exclusive-OR operator can only act on single chromosome, while to increase the diversity of variation, multi-Vari being calculated
Sub-portfolio carries out mutation operation to chromosome, with the multiple no-manned plane Target Distribution Model established in this solution procedure four.Multi-Vari
Operator includes operator, commutating operator and sliding operator.
Step 7, Optimization Solution have the special gray area covering search track of dotted line Area Objects.
The special grey area of dotted line Area Objects is solved using the countermeasure genetic algorithm of dual coding chromosome and changeable exclusive-OR operator
Domain covering search track, includes the following steps:
Step 7.1: initialization of population；
Step 7.2: fitness function calculates.It is suitable that individual in population is calculated using the Target Distribution Model established in step 3
Response function；
Step 7.3: roulette selection parent individuality；
Step 7.4: intersecting；
Step 7.5: variation.It is made a variation using the changeable exclusive-OR operator in step 6 to the dual coding chromosome in step 5
Operation；
Step 7.6: countermeasure calculates；
Step 7.7: population recruitment；
Step 7.8: Evolution of Population, circulation step 7.2 to step 7.7.
Step 7.9: iteration ends seek the special gray area with dotted line Area Objects according to optimal objective allocation result
Covering search track.
The utility model has the advantages that
1, a kind of multiple no-manned plane collaboration rapid Cover disclosed by the invention searches for path planning method, is able to solve with point
The multiple no-manned plane collaboration covering search trajectory planning problem of the special gray area of line Area Objects.
2, a kind of multiple no-manned plane collaboration rapid Cover disclosed by the invention searches for path planning method, can be according to target spy
Property and prior information extract focussing search region, range searching range can be reduced, and cook up covering search
Track effectively can replace region all standing to search for track, to reduce the region overlay time.
3, a kind of multiple no-manned plane collaboration rapid Cover disclosed by the invention searches for path planning method, by changeable exclusive-OR operator and
Countermeasure calculating is dissolved into genetic algorithm, and the changeable exclusive-OR operator of use can increase the diversity of population, and countermeasure calculating can increase
The ability of searching optimum of strong genetic algorithm, to improve this method robustness.
Detailed description of the invention
Fig. 1 is unmanned aerial vehicle vision field model schematic diagram；
Fig. 2 is line target covering search schematic diagram；
Fig. 3 is Area Objects covering search schematic diagram；
Fig. 4 a is the path the Dubins RSL of terminal direction constraint；
Fig. 4 b is the path the Dubins LSR of terminal direction constraint；
Fig. 4 c is the path the Dubins RSR of terminal direction constraint；
Fig. 4 d is the path the Dubins LSL of terminal direction constraint；
Fig. 4 e is the path the Dubins RLR of terminal direction constraint；
Fig. 4 f is the path the Dubins LRL of terminal direction constraint；
Fig. 5 a is the unconfined path the Dubins LS in terminal direction；
Fig. 5 b is the unconfined path the Dubins RL in terminal direction；
Fig. 5 c is the unconfined path the Dubins L in terminal direction；
Fig. 5 d is the unconfined path the Dubins LS or RS in terminal direction；
Fig. 6 a is the Target Assignment of UAV1；
Fig. 6 b is the Target Assignment of UAV2；
Fig. 6 c is the UAV1 and UAV2 covering search corresponding track length of target；
Fig. 7 is the connection track schematic diagram of terminal direction obligatory point target；
Fig. 8 is connection track schematic diagram of the terminal direction without constraint point target；
Fig. 9 is the connection track schematic diagram of line target；
Figure 10 is the connection track schematic diagram of Area Objects；
Figure 11 is operator operation chart；
Figure 12 is commutating operator operation chart；
Figure 13 is sliding operator operation chart；
Figure 14 is the countermeasure operatings of genetic algorithm step of dual coding chromosome and changeable exclusive-OR operator；
Figure 15 a is the lower covering search trajectory planning result of scene 1；
Figure 15 b is the lower covering search trajectory planning result of scene 2；
Figure 15 c is the lower covering search trajectory planning result of scene 3；
Figure 15 d is the lower covering search trajectory planning result of scene 4；
Figure 16 is that search trajectory planning result box traction substation is covered with dotted line Area Objects special area.
Specific embodiment
Purpose and advantage in order to better illustrate the present invention, below by simulation calculation comparative test, in conjunction with table, attached
The present invention will be further described for figure, and by testing comprehensive performance of the invention compared with traditional optimization result
Card analysis.
For the validity for verifying proposed method, the present invention is respectively adopted and proposes algorithm (OGA-DEMMO), traditional genetic algorithm
(being abbreviated as GA), random algorithm (Random Search) solve multiple no-manned plane target assignment problem.Simulation hardware is Intel (R)
Core (TM) 2Duo CPU E7500 2.93GHz, 4G memory, simulated environment MATLAB.All data use in embodiment
Normalization mode, and assume that mission area size is [0,100] × [0,100], the scale of population takes 50, greatest iteration time
Number takes 50, crossover probability Pc=0.9, mutation probability Pm=0.05, reversed probability Po=0.9, weight coefficient α and β are 0.5, nothing
Man-machine turning radius is all 4.
Embodiment one
Illustrate a kind of multiple no-manned plane collaboration rapid Cover search boat disclosed in the present embodiment by four kinds of different scenes
The validity of mark planing method feasibility and algorithm.Scene one type of target and quantity into scene four gradually increase, tool
Body number of types is as shown in table 2.
The description of 2 four kinds of scenes of table
Track is searched in the covering of step 1, point-line-surface special objective.
Unmanned plane target seeker ground visual field is influenced by its flying height, attitude angle and target seeker established angle, by nobody
Machine is considered as particle, and assumes target seeker ground visual field for circle and be located at immediately below unmanned plane, and unmanned plane flies in fixed height
It goes and its ground visual field is not influenced by attitude angle and hypsography.It is as shown in Figure 1 that unmanned aerial vehicle vision field model is established accordingly.Nobody
Machine is related to detection time of its target seeker to target to the detection probability of target.According to the prior information and target of battlefield surroundings
Gray area is extracted as the focussing search mesh of point target, line target and Area Objects three types by the geometrical characteristic of domain of the existence
Mark.For point target, when its field of view center passes through target, detection time longest of the unmanned plane to target, detection maximum.Covering
Track, Area Objects are searched in the covering of covering search track, line target that search track is divided into point target according to special objective type
Covering search for track.
Step 1.1: track is searched in the covering of point target.
For directionless constraint point target, it is only necessary to which unmanned plane field of view center passes through point target and obtains directionless obligatory point
Track is searched in the covering of target；For there is direction to constrain point target, unmanned plane field of view center passes through point target i.e. along specific direction
Track is searched in covering to there is direction to constrain point target.
Step 1.2: track is searched in the covering of line target.
Since line target length is far longer than unmanned plane visual field width, and line target width is less than unmanned aerial vehicle vision field width
Degree, therefore, the covering search track of line target are the geometry line segment of line target, any endpoint (L of the unmanned plane from line target1
Or L2) search for along target geometry line segment direction to another endpoint (L of line target2Or L1) line target covering search is completed, such as scheme
Shown in 2.
Step 1.3: track is searched in the covering of Area Objects.
Area Objects are all far longer than unmanned plane visual field width on length and width direction, and unmanned plane needs back and forth cover back and forth
Lid search could complete the search to Area Objects.Using the vertex of Area Objects as the covering initial search point of Area Objects, Area Objects
Length direction as covering search prime direction, unmanned plane is from starting point (Pentry) and prime direction use reciprocal covering back and forth
Way of search generates different covering search tracks and searches for track to get to the covering of Area Objects.
For Area Objects, there are commonly Z-shaped search and spiral search for the covering search reciprocal back and forth.
Since there are drain sweeps under there are minimum turning radius constraint for simple spiral search pattern, and it is based on Z-shaped search
Mode can without drain sweep complete the covering search of Area Objects, and preferably Z-shaped search, Z-shaped search has different titles, such as grass mower
Formula search, the search of seeder formula, the search of mill ice formula, raster pattern search, scanning line search etc., as shown in Figure 3.
Step 2 solves most short connection track.Unmanned plane is by its Dynamic Constraints, using Dubins model by unmanned plane
Dynamic Constraints are reduced to geometry of motion constraint, and assume that unmanned plane is flown in different height with constant speed to avoid touching
It hits, the kinematics model for establishing unmanned plane is as follows
Wherein, v is the flying speed of unmanned plane, rminIt is the minimum turning radius of unmanned plane, c is control amount input, if
C=1 represents unmanned plane and turns to the left, and c=-1 represents unmanned plane and bends to right.
Consider unmanned plane turning angle, unmanned plane is from arbitrary initial state (xinitial,yinitial,θinitial) reach any end
End state (xfinal,yfinal,θfinal) track be with minimum turning radius rminFor the combination of the circular arc and straightway of radius.Root
It is constrained according to the SOT state of termination, most short connection track is divided into the path Dubins for having the constraint of terminal direction and endless constraint.For having
The path Dubins of terminal direction constraint, R indicate that target is turned circular arc clockwise, and L indicates that turning circular arc counterclockwise, S indicate straight
Line segment, then the shortest path Dubins is one of D={ RSL, LSR, RSR, LSL, RLR, LRL }, as shown in Figure 4.For
The path Dubins of endless extreme direction constraint, most short connection track be one section of circular arc or be one section of circular arc and straightway group
It closes, set of minimal paths is D={ LS, RL, RS, L }, as shown in Figure 5.
Step 3 establishes the special gray area covering search Target Distribution Model with dotted line Area Objects.
Covering search Target Assignment is combinatorial optimization problem, and the spy with dotted line Area Objects is established based on combinatorial optimization problem
Different gray area covering search Target Distribution Model, optimization aim are that unmanned plane formation obtains maximum scouting search efficiency, are made
Obtaining unmanned plane formation can use consumption as small as possible to complete covering search mission.The maximum scouting search efficiency embodies
At two aspect of task time-consuming and all unmanned planes time-consuming.
Step three concrete methods of realizing are as follows: assuming that NUFrame unmanned planeNTA packet
Target collection containing point-line-surface three typesThese polyisocyanate structure Target Assignments are formed into columns to unmanned plane
Collaboratively searching, according to formula (8) calculating target function.
Wherein α, β ∈ [0,1] are the weight factors of corresponding sub-goal, and alpha+beta=1.Formula (9) calculates corresponding unmanned plane
Complete time t required for respective search missionu。tuEqual to the detection range of unmanned plane divided by its constant cruising speed vu。
Wherein NV=NU+NTIndicate the node state that unmanned plane is in, w (qi,qj) indicate two node states of unmanned plane it
Between track length, which includes that unmanned plane to the coverings of respective objects searches for track length.
It is binary decision variable, if (qi,qj) represent unmanned plane Uu∈ U is from node state qiReach qj, decision variableDeng
In 1, otherwise it is equal to 0.Such as 2 frame unmanned planes and 4 targets to be searched, a kind of possible decision variable is indicated such as Fig. 6 institute
Show.Fig. 6 (a) indicates that the covering search target sequence of unmanned plane 1 is { T1,T3, Fig. 6 (b) indicates that covering is searched for target by unmanned plane 2
Sequence is { T2,T4, Fig. 6 (c) indicates corresponding path length.Arranging each target only needs a frame unmanned plane to search for, because
This, constraint condition is expressed as follows:
Step 4, single unmanned plane covering search track length computation.
Assuming that certain unmanned plane is assigned m target sequence { T of search1,T2,…,Tm, search track total length includes four
Part, Dubins path length of the first part between starting point and first aim, second part are remaining adjacent target
Between the sum of the path most short Dubins, Part III is that all target coverages search for the sum of track length, and Part IV is
The path length of unmanned plane return starting point.Single unmanned plane covering search track length be aforementioned four part paths length it
With as shown in formula (12).
Wherein (x0,y0) it is unmanned plane initial position, θ0For the starting velocity direction of unmanned plane, (xj,yj) it is j-th of target
Position, θjIt is unmanned plane in (xj,yj) at direction.d((xj,yj,θj),(xj+1,yj+1,θj+1) it is two node state (xj,yj,
θj) and (xj+1,yj+1,θj+1) between the path Dubins.ljIt is the covering search track length of target j.d((xm,ym,θm),
(x0,y0)) be the last one target correcting action of unmanned plane distance.
d((xj,yj,θj),(xj+1,yj+1,θj+1) following four calculating method mode had according to the difference of target: (1) it is directionless
It is as shown in Figure 7 to constrain point target connection track；(2) there is constraint point target connection track in direction as shown in Figure 8；(3) line target
Connection track is as shown in figure 9, track shorter in two tracks is chosen as connection track；(4) the connection track of Area Objects is as schemed
Shown in 10, four alternative tracks are shared, according to greedy strategy, therefrom select most short track as connection track.
Step 5 customizes dual coding chromosome.
The coding mode that the chromosome of genetic algorithm generallys use is binary coding, however in target assignment problem,
Binary coding mode can not intuitively indicate UAV targets' matching relationship, and decimal coded mode on the one hand can be straight
Seeing indicates UAV targets' matching relationship, and another aspect decimal coded mode can satisfy chromosome when carrying out genetic manipulation
Constraint between coding.
The step five customizes the preferred decimal system dual coding chromosome of dual coding chromosome, customization decimal system dual coding dye
Colour solid concrete methods of realizing is that chromosome 1 is used to indicate that the access order of target, chromosome 2 indicate search order spaced points, this
A little spaced points divide chromosome 1 for multiple gene sections, and each gene section will indicate the search target sequences of each unmanned plane.Dyeing
The value range of body 1 is 1,2 ..., NT, gene number is NT, the gene number of chromosome 2 is NU-1。
Step 6 sets the diversity that changeable exclusive-OR operator increases hereditary variation.Since the chromosome customized in step 5 is
Dual coding chromosome, monotropic exclusive-OR operator can only act on single chromosome, while to increase the diversity of variation, multi-Vari being calculated
Sub-portfolio carries out mutation operation to chromosome, with the multiple no-manned plane Target Distribution Model established in this solution procedure four.Multi-Vari
Operator includes: (1) operator, as shown in figure 11；(2) commutating operator, as shown in figure 12；(3) operator is slided, such as Figure 13 institute
Show.
Assuming that chromosome 1 is (1,2,3,4,5,6), chromosome 2 is (2,4), and two positions being randomly generated are 2 and 5, and
And chromosome 2 is become (3,5) by the way of regenerating, the child chromosome of eight kinds of combinations is as shown in table 3.
3 eight kinds of child chromosomes of table
| Case | chromosome 1 | chromosome 2 |
| 1 | (1,2,3,4,5,6) | (2,4) |
| 2 | (1,5,4,3,2,6) | (2,4) |
| 3 | (1,5,3,4,2,6) | (2,4) |
| 4 | (1,3,4,5,2,6) | (2,4) |
| 5 | (1,2,3,4,5,6) | (3,5) |
| 6 | (1,5,4,3,2,6) | (3,5) |
| 7 | (1,5,3,4,2,6) | (3,5) |
| 8 | (1,3,4,5,2,6) | (3,5) |
Step 7, Optimization Solution have the special gray area covering search track of dotted line Area Objects.
The special grey area of dotted line Area Objects is solved using the countermeasure genetic algorithm of dual coding chromosome and changeable exclusive-OR operator
Domain covering search track, includes the following steps:
Step 7.1: initialization of population；
Step 7.2: fitness function calculates.It is suitable that individual in population is calculated using the Target Distribution Model established in step 3
Response function；
Step 7.3: roulette selection parent individuality；
Step 7.4: intersecting；
Step 7.5: variation.It is made a variation using the changeable exclusive-OR operator in step 6 to the dual coding chromosome in step 5
Operation；
Step 7.6: countermeasure calculates；
Step 7.7: population recruitment；
Step 7.8: Evolution of Population, circulation step 7.2 to step 7.7.
Step 7.9: iteration ends seek the special gray area with dotted line Area Objects according to optimal objective allocation result
Covering search track.Its solution process is as shown in figure 14, and solving result is as shown in figure 15, and program results statistics is as shown in table 4,
Box traction substation is as shown in figure 16.
4 Optimization Solution data statistics result of table
Data and Figure 16 show that compared with GA and Random Search, the present invention in most cases has more in table 4
Good performance.For the scene 1 and scene 2 of small-scale problem, the solving result of three kinds of algorithms is substantially suitable.And for scene
1,2 and 3, three kinds of algorithms can find same current optimal solution.However for scene 4, in 100 tests, only originally
Invention can find it is more excellent as a result, and solving result maximum value and average value will better than GA and Random Search,
Show that inventive algorithm effect is more preferable.In addition, the number of current optimal solution can be found by comparing in 100 tests, it can
To show that the method for the present invention is better than genetic algorithm and random search algorithm, illustrate that the present invention can more effectively solve special section
Domain covering search trajectory planning problem, has better robustness.
Above-described specific descriptions have carried out further specifically the purpose of invention, technical scheme and beneficial effects
It is bright, it should be understood that above is only a specific embodiment of the present invention, being used to explain the present invention, it is not used to limit this
The protection scope of invention, all within the spirits and principles of the present invention, any modification, equivalent substitution, improvement and etc. done should all
It is included within protection scope of the present invention.
Claims (10)
1. a kind of multiple no-manned plane collaboration rapid Cover searches for path planning method, characterized by the following steps:
Track is searched in the covering of step 1, point-line-surface special objective；
Unmanned plane target seeker ground visual field is influenced by its flying height, attitude angle and target seeker established angle, by unmanned aerial vehicle vision
For particle, and assume target seeker ground visual field be it is round and be located at immediately below unmanned plane, unmanned plane fly in fixed height and
Its ground visual field is not influenced by attitude angle and hypsography；Unmanned plane is to the detection probability of target and the target seeker pair of unmanned plane
The detection time of target is related；According to the geometrical characteristic of the prior information of battlefield surroundings and target domain of the existence, by gray area
It is extracted as the focussing search target of point target, line target and Area Objects three types；For point target, when unmanned plane field of view center
When across target, detection time longest of the unmanned plane to target, detection probability maximum；Covering search track is according to special objective class
Type is divided into the covering search track of the covering search track of point target, the covering search track of line target, Area Objects；
Step 2, most short connection track；
The Dynamic Constraints of unmanned plane are reduced to geometry of motion about by its Dynamic Constraints, using Dubins model by unmanned plane
Beam, and assume that unmanned plane is flown to avoid collision with constant speed in different height, establish the kinematics model of unmanned plane such as
Formula (1)；
Wherein, v is the flying speed of unmanned plane, rminIt is the minimum turning radius of unmanned plane, c is control amount input, if c=1
It represents unmanned plane to turn to the left, c=-1 represents unmanned plane and bends to right；
Consider unmanned plane turning angle, unmanned plane is from arbitrary initial state (xinitial,yinitial,θinitial) reach any terminal shape
State (xfinal,yfinal,θfinal) track be with minimum turning radius rminFor the combination of the circular arc and straightway of radius；According to end
State constraint is held, most short connection track is divided into the path Dubins for having the constraint of terminal direction and endless constraint；For there is terminal
The path Dubins of direction constraint, R indicate that target is turned circular arc clockwise, and L indicates that turning circular arc counterclockwise, S indicate straightway,
Then the shortest path Dubins is one of D={ RSL, LSR, RSR, LSL, RLR, LRL }；For the constraint of endless extreme direction
The path Dubins, most it is short connection track be one section of circular arc or be one section of circular arc and straightway combination, set of minimal paths D
={ LS, RL, RS, L }；
Step 3 establishes the special gray area covering search Target Distribution Model with dotted line Area Objects；
Covering search Target Assignment is combinatorial optimization problem, and the special ash with dotted line Area Objects is established based on combinatorial optimization problem
Color Area Coverage Searching Target Distribution Model, optimization aim is that unmanned plane formation obtains maximum scouting search efficiency, so that nothing
Man-machine formation can complete covering search mission with consumption as small as possible；The maximum scouting search efficiency embodies in office
Time-consuming and all two aspect of unmanned plane time-consuming of business；
Step 4, single unmanned plane covering search track length computation；
Assuming that certain unmanned plane is assigned m target sequence { T of search1,T2,…,Tm, search track total length includes four parts,
Dubins path length of the first part between starting point and first aim, second part are between remaining adjacent target
The sum of most short path Dubins, Part III are that all target coverages search for the sum of track length, and Part IV is unmanned plane
Return to the path length of starting point；Single unmanned plane covering search track length is the sum of aforementioned four part paths length；
Step 5 customizes dual coding chromosome；
Step 6 sets the diversity that changeable exclusive-OR operator increases hereditary variation；Since the chromosome customized in step 5 is double compile
Code chromosome, monotropic exclusive-OR operator can only act on single chromosome, while to increase the diversity of variation, by multi-Vari operator tuple
It closes and mutation operation is carried out to chromosome, with the Target Distribution Model established in this solution procedure three；Changeable exclusive-OR operator includes conversion
Operator, commutating operator and sliding operator；
Step 7, Optimization Solution have the special gray area covering search track of dotted line Area Objects；
It is covered using the special gray area that the countermeasure genetic algorithm of dual coding chromosome and changeable exclusive-OR operator solves dotted line Area Objects
Lid search track.
2. a kind of multiple no-manned plane collaboration rapid Cover as described in claim 1 searches for path planning method, it is characterised in that: institute
Seven concrete methods of realizing of the step of stating includes the following steps:
Step 7.1: initialization of population；
Step 7.2: fitness function calculates；Individual adaptation degree in population is calculated using the Target Distribution Model established in step 3
Function；
Step 7.3: roulette selection parent individuality；
Step 7.4: intersecting；
Step 7.5: variation；Variation behaviour is carried out to the dual coding chromosome in step 5 using the changeable exclusive-OR operator in step 6
Make；
Step 7.6: countermeasure calculates；
Step 7.7: population recruitment；
Step 7.8: Evolution of Population, circulation step 7.2 to step 7.7；
Step 7.9: iteration ends seek having the special gray area of dotted line Area Objects to cover according to optimal objective allocation result
Search for track.
3. a kind of multiple no-manned plane collaboration rapid Cover as described in claim 1 searches for path planning method, it is characterised in that:
Covering search track is divided into the covering of covering the search track, line target of point target according to special objective type in step 1
Search for track, track is searched in the covering of Area Objects；
Step 1.1: track is searched in the covering of point target；
For directionless constraint point target, it is only necessary to which unmanned plane field of view center passes through point target and obtains directionless constraint point target
Covering search for track；For there is direction to constrain point target, it is to have that unmanned plane field of view center, which passes through point target along specific direction,
Track is searched in the covering that direction constrains point target；
Step 1.2: track is searched in the covering of line target；
Since line target length is far longer than unmanned plane visual field width, and line target width is less than unmanned plane visual field width, because
This, the covering search track of line target is the geometry line segment of line target, and unmanned plane is from any endpoint of line target along target
Geometry line segment direction is searched for another endpoint of line target and completes line target covering search；
Step 1.3: track is searched in the covering of Area Objects；
Area Objects are all far longer than unmanned plane visual field width on length and width direction, and unmanned plane needs back and forth reciprocal covering to search
Suo Caineng completes the search to Area Objects；Using the vertex of Area Objects as the covering initial search point of Area Objects, the length of Area Objects
Degree or width direction are as covering search prime direction, and unmanned plane is from starting point and prime direction using reciprocal covering search back and forth
Mode generates different covering search tracks and searches for track to get to the covering of Area Objects.
4. a kind of multiple no-manned plane collaboration rapid Cover as claimed in claim 3 searches for path planning method, it is characterised in that: institute
The covering search reciprocal back and forth stated is Z-shaped search；The title of the Z-shaped search includes that grass mower formula is searched for, seeder formula is searched
Rope, the search of mill ice formula, raster pattern search or scanning line search.
5. a kind of multiple no-manned plane collaboration rapid Cover as claimed in claim 1,2 or 3 searches for path planning method, feature exists
In:
Step three concrete methods of realizing are as follows: assuming that NUFrame unmanned planeNTIt is a to include
The target collection of point-line-surface three typesThese polyisocyanate structure Target Assignments are formed into columns to unmanned plane and are cooperateed with
Search, according to formula (2) calculating target function；
Wherein α, β ∈ [0,1] are the weight factors of corresponding sub-goal, and alpha+beta=1；Formula (3) calculates corresponding unmanned plane and completes
Time t required for respective search missionu；tuEqual to the detection range of unmanned plane divided by its constant cruising speed vu；
Wherein NV=NU+NTIndicate the node state that unmanned plane is in, w (qi,qj) indicate between two node states of unmanned plane
Track length, the track length include that track length is searched in the covering to respective objects of unmanned plane；It is two
System decision variable, if (qi,qj) represent unmanned plane Uu∈ U is from node state qiReach qj, decision variableEqual to 1,
Otherwise it is equal to 0；Formula (4), (5), which limit each target, only needs frame unmanned plane search.
6. a kind of multiple no-manned plane collaboration rapid Cover as claimed in claim 1,2 or 3 searches for path planning method, feature exists
In:
Single unmanned plane covering search track length computation concrete methods of realizing is to be asked using formula (6) in the step four
It solves unmanned plane and covers searching route length；
Wherein (x0,y0) it is unmanned plane initial position, θ0For the starting velocity direction of unmanned plane, (xj,yj) be j-th of target position
It sets, θjIt is unmanned plane in (xj,yj) at direction；d((xj,yj,θj),(xj+1,yj+1,θj+1) it is two node state (xj,yj,θj)
(xj+1,yj+1,θj+1) between the path Dubins；ljIt is the covering search track length of target j；d((xm,ym,θm), (x0,
y0)) be the last one target correcting action of unmanned plane distance；d((xj,yj,θj),(xj+1,yj+1,θj+1) according to the side of having
To constraint point target, directionless constraint point target, line target, the corresponding four kinds of calculation methods of four kinds of target types of Area Objects；Due to
Appearance indicates four alternative tracks, according to greedy strategy, therefrom selects most short track as connection track.
7. a kind of multiple no-manned plane collaboration rapid Cover as claimed in claim 1,2 or 3 searches for path planning method, feature exists
In:
The step five customization dual coding chromosome selects decimal system dual coding chromosome, customizes decimal system dual coding chromosome
Concrete methods of realizing is that chromosome 1 is used to indicate that the access order of target, chromosome 2 indicate search order spaced points, these
Dot interlace divides chromosome 1 for multiple gene sections, and each gene section will indicate the search target sequences of each unmanned plane；Chromosome 1
Value range be 1,2 ..., NT, gene number is NT, the gene number of chromosome 2 is NU-1；Assuming that having 10 targets and 4
Frame unmanned plane, chromosome 1 be (1,2,3,4,5,6,7,8,9,10), the different target assignment problem according to chromosome 2 include with
Under several typical distribution cases, the results are shown in Table 1 for the Target Assignment of each unmanned plane under different cases；
Example 1:
Chromosome 1:(1,2,3,4,5,6,7,8,9,10)
Chromosome 2:(3,5,8)
Example 2:
Chromosome 1:(1,2,3,4,5,6,7,8,9,10)
Chromosome 2:(0,5,8)
Example 3:
Chromosome 1:(1,2,3,4,5,6,7,8,9,10)
Chromosome 2:(4,7,10)
Example 4:
Chromosome 1:(1,2,3,4,5,6,7,8,9,10)
Chromosome 2:(4,4,7)
Example 5:
Chromosome 1:(1,2,3,4,5,6,7,8,9,10)
Chromosome 2:(4,4,4)
The Target Assignment result of each unmanned plane of table 1
。
8. a kind of multiple no-manned plane collaboration rapid Cover searches for path planning method, it is characterised in that: first according to battlefield surroundings
Gray area is extracted as three type of point target, line target and Area Objects by the geometrical characteristic of prior information and target domain of the existence
Then the focussing search target of type determines that the access of each focussing search target is suitable using dual coding countermeasure integer genetic algorithm
Sequence；Finally it is directed to the allocation order of focussing search target and unmanned plane, it is contemplated that the constraint of unmanned plane turning radius and solution time
Next search is reached it is required that obtaining unmanned plane using the path Dubins and greedy strategy and covering search track end point from current goal
Rope target coverage searches for the most short connection track in part between track initiation point, obtains the covering search boat of next search target
Mark, to obtain multiple no-manned plane collaboration covering search track, i.e. completion multiple no-manned plane collaboration rapid Cover searches for trajectory planning.
9. a kind of multiple no-manned plane collaboration rapid Cover as claimed in claim 8 searches for path planning method, it is characterised in that:
The covering search track of the focussing search target of the three types is divided into covering the search track, line of point target type
Track is searched in the covering of covering the search track, Area Objects type of target type；
Track is searched in the covering of point target type；
For directionless constraint point target, it is only necessary to which unmanned plane field of view center passes through point target and obtains directionless constraint point target
Covering search for track；For there is direction to constrain point target, it is to have that unmanned plane field of view center, which passes through point target along specific direction,
Track is searched in the covering that direction constrains point target；
Track is searched in the covering of line target type；
Since line target length is far longer than unmanned plane visual field width, and line target width is less than unmanned plane visual field width, because
This, the covering search track of line target is the geometry line segment of line target, and unmanned plane is from any endpoint of line target along target
Geometry line segment direction is searched for another endpoint of line target and completes line target covering search；
Track is searched in the covering of Area Objects type；
Area Objects are all far longer than unmanned plane visual field width on length and width direction, and unmanned plane needs back and forth reciprocal covering to search
Suo Caineng completes the search to Area Objects；Using the vertex of Area Objects as the covering initial search point of Area Objects, the length of Area Objects
Degree or width direction are as covering search prime direction, and unmanned plane is from starting point and prime direction using reciprocal covering search back and forth
Mode generates different covering search tracks and searches for track to get to the covering of Area Objects.
10. a kind of multiple no-manned plane collaboration rapid Cover as claimed in claim 9 searches for path planning method, it is characterised in that:
The described covering search reciprocal back and forth is Z-shaped search, the title of Z-shaped search include: the search of grass mower formula, the search of seeder formula,
Grind the search of ice formula, raster pattern search and scanning line search.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610936002.0A CN106406346B (en) | 2016-11-01 | 2016-11-01 | A kind of multiple no-manned plane collaboration rapid Cover search path planning method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610936002.0A CN106406346B (en) | 2016-11-01 | 2016-11-01 | A kind of multiple no-manned plane collaboration rapid Cover search path planning method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN106406346A CN106406346A (en) | 2017-02-15 |
| CN106406346B true CN106406346B (en) | 2019-04-16 |
Family
ID=58011724
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610936002.0A Active CN106406346B (en) | 2016-11-01 | 2016-11-01 | A kind of multiple no-manned plane collaboration rapid Cover search path planning method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106406346B (en) |
Families Citing this family (41)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106969778B (en) * | 2017-02-28 | 2020-10-16 | 南京航空航天大学 | Path planning method for cooperative pesticide application of multiple unmanned aerial vehicles |
| CN107037828B (en) * | 2017-03-24 | 2020-02-14 | 西安电子科技大学 | Single-step optimization method for unmanned aerial vehicle area coverage based on particle swarm optimization |
| CN106873628B (en) * | 2017-04-12 | 2019-09-20 | 北京理工大学 | A kind of collaboration paths planning method of multiple no-manned plane tracking multimachine moving-target |
| CN107037827B (en) * | 2017-04-14 | 2020-02-21 | 合肥工业大学 | Unmanned aerial vehicle aerial work task allocation and flight path planning combined optimization method and device |
| CN107037826B (en) * | 2017-04-14 | 2019-11-12 | 合肥工业大学 | Unmanned plane detection mission distribution method and device |
| CN107145161B (en) * | 2017-05-27 | 2020-02-21 | 合肥工业大学 | Flight path planning method and device for unmanned aerial vehicle to access multiple target points |
| CN107238388B (en) | 2017-05-27 | 2018-02-23 | 合肥工业大学 | Multiple no-manned plane task is distributed and trajectory planning combined optimization method and device |
| CN107168380B (en) * | 2017-06-28 | 2020-06-05 | 西安电子科技大学 | Multi-step optimization method for coverage of unmanned aerial vehicle cluster area based on ant colony algorithm |
| CN107450597A (en) * | 2017-08-18 | 2017-12-08 | 南方科技大学 | The communication system and method for rescue at sea equipment |
| CN107543549B (en) * | 2017-10-27 | 2020-09-01 | 上海理工大学 | Route planning method under single-side imaging constraint condition of unmanned aerial vehicle |
| CN108151748B (en) * | 2017-11-09 | 2021-04-06 | 广州极飞科技有限公司 | Flight device surveying and mapping operation route planning method and device and terminal |
| CN108168558B (en) * | 2017-12-22 | 2020-04-10 | 中国海洋大学 | Unmanned aerial vehicle track planning algorithm applied to river target search task |
| CN108459616B (en) * | 2018-03-07 | 2021-08-03 | 西安电子科技大学 | Unmanned aerial vehicle group collaborative coverage route planning method based on artificial bee colony algorithm |
| CN108613676B (en) * | 2018-03-27 | 2019-07-12 | 中国民用航空飞行学院 | A kind of unmanned plane and there is the emergency rescue path planning method under Mechanism of Human-Computer Cooperation |
| CN108616302B (en) * | 2018-04-28 | 2020-10-30 | 中国人民解放军陆军工程大学 | Unmanned aerial vehicle multiple coverage model under power control and deployment method |
| CN110262563A (en) * | 2018-05-23 | 2019-09-20 | 中国海洋大学 | Multiple no-manned plane collaboratively searching mesh calibration method waterborne |
| CN108983817B (en) * | 2018-08-07 | 2021-03-23 | 西北工业大学 | Multi-region searching method and device |
| CN109491402B (en) * | 2018-11-01 | 2020-10-16 | 中国科学技术大学 | Multi-unmanned aerial vehicle cooperative target monitoring control method based on cluster control |
| 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 |
| CN110058607B (en) * | 2019-04-08 | 2020-07-07 | 北京航空航天大学 | Simulated European 26891bird intelligent unmanned aerial vehicle large-scale cluster super-maneuvering turning method |
| CN110069074B (en) * | 2019-04-17 | 2020-08-18 | 南京航空航天大学 | Unmanned aerial vehicle collaborative flight path planning method based on multi-target three-point positioning |
| CN110398980B (en) * | 2019-06-05 | 2020-11-24 | 西安电子科技大学 | Flight path planning method for cooperative detection and obstacle avoidance of unmanned aerial vehicle group |
| CN110262547B (en) * | 2019-06-21 | 2021-08-31 | 西安电子科技大学 | Circular formation takeoff method based on path tracking |
| CN110426043B (en) * | 2019-08-06 | 2021-06-29 | 中国人民解放军陆军工程大学 | Unmanned aerial vehicle reconnaissance flight path planning method facing line target |
| WO2021031192A1 (en) * | 2019-08-22 | 2021-02-25 | 深圳市大疆创新科技有限公司 | Route planning method for multi-uav operation, control terminal, and computer-readable storage medium |
| CN110658847B (en) * | 2019-09-30 | 2021-01-15 | 北京航空航天大学 | Method for realizing unmanned aerial vehicle group formation reconstruction by using genetic algorithm and Dubins algorithm |
| CN111221349B (en) * | 2019-11-26 | 2022-12-23 | 西北工业大学 | Multi-unmanned aerial vehicle target positioning air route planning method |
| CN110906936B (en) * | 2019-12-18 | 2022-11-18 | 哈尔滨工程大学 | Underwater robot path planning method |
| CN111132014A (en) * | 2019-12-31 | 2020-05-08 | 南京烽火星空通信发展有限公司 | Wireless sensor network node positioning method based on unmanned aerial vehicle mobile beacon |
| CN111487997B (en) * | 2020-05-12 | 2023-06-23 | 西安爱生技术集团公司 | Attack type unmanned aerial vehicle double-machine collaborative guidance method |
| CN111811513B (en) * | 2020-06-28 | 2023-03-28 | 西安电子科技大学 | Flight path planning method for cooperative coverage and obstacle avoidance of multiple unmanned aerial vehicles |
| CN111817770B (en) * | 2020-06-30 | 2022-06-14 | 中国人民解放军陆军工程大学 | Relay unmanned aerial vehicle deployment method and terminal equipment |
| CN112327862B (en) * | 2020-11-16 | 2021-10-19 | 北京理工大学 | Path planning method for multi-robot collaborative search in uncertain environment |
| CN112327927B (en) * | 2020-11-25 | 2021-11-26 | 北京理工大学 | Multi-angle strike track planning method for formation unmanned aerial vehicles based on grid planning algorithm |
| CN112945255B (en) * | 2021-01-29 | 2022-06-21 | 中国人民解放军国防科技大学 | Method and system for planning multi-area coverage path by cooperation of multiple unmanned aerial vehicles carried by automobile |
| CN112947555B (en) * | 2021-02-04 | 2022-06-14 | 中国人民解放军国防科技大学 | Rapid flight path planning method and device for multi-machine cooperative strike and computer equipment |
| CN113359849B (en) * | 2021-07-06 | 2022-04-19 | 北京理工大学 | Multi-unmanned aerial vehicle collaborative rapid search method for moving target |
| CN114003059B (en) * | 2021-11-01 | 2024-04-16 | 河海大学常州校区 | UAV path planning method based on deep reinforcement learning under kinematic constraint condition |
| CN114047785B (en) * | 2021-11-26 | 2023-06-09 | 北京航天自动控制研究所 | Method and system for cooperatively searching multiple moving targets by unmanned aerial vehicle clusters |
| CN113959449B (en) * | 2021-12-22 | 2022-03-11 | 中国人民解放军空军预警学院 | Multi-unmanned aerial vehicle search route planning method and device |
| CN114489087B (en) * | 2022-04-18 | 2022-07-05 | 北京理工大学 | Multi-unmanned vehicle path collaborative planning method and system |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8639396B1 (en) * | 2008-10-08 | 2014-01-28 | Raytheon Company | Cooperative control of unmanned aerial vehicles for tracking targets |
| CN103676944A (en) * | 2013-12-11 | 2014-03-26 | 北京理工大学 | Unmanned aerial vehicle route planning method based on Dubins route and sparse A* searching |
| CN105700549A (en) * | 2016-01-21 | 2016-06-22 | 北京理工大学 | Unmanned plane multi-track planning method based on sequence ecological niche PSO (particle swarm optimization) algorithm |
| CN105973235A (en) * | 2016-04-25 | 2016-09-28 | 北京理工大学 | Rotor unmanned aerial vehicle coverage search flight path planning method for polygonal region having no-fly zone |
-
2016
- 2016-11-01 CN CN201610936002.0A patent/CN106406346B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8639396B1 (en) * | 2008-10-08 | 2014-01-28 | Raytheon Company | Cooperative control of unmanned aerial vehicles for tracking targets |
| CN103676944A (en) * | 2013-12-11 | 2014-03-26 | 北京理工大学 | Unmanned aerial vehicle route planning method based on Dubins route and sparse A* searching |
| CN105700549A (en) * | 2016-01-21 | 2016-06-22 | 北京理工大学 | Unmanned plane multi-track planning method based on sequence ecological niche PSO (particle swarm optimization) algorithm |
| CN105973235A (en) * | 2016-04-25 | 2016-09-28 | 北京理工大学 | Rotor unmanned aerial vehicle coverage search flight path planning method for polygonal region having no-fly zone |
Non-Patent Citations (2)
| Title |
|---|
| Enhanced Sparse A* Search for UAV Path Planning Using Dubins Path Estimation;WANG Zhu 等;《Proceedings of the 33rd Chinese Control Conference》;20140730;738-742 |
| 基于物理规划的无人机多目标航迹规划;于成龙 等;《电光与控制》;20140531;第21卷(第5期);1-5 |
Also Published As
| Publication number | Publication date |
|---|---|
| CN106406346A (en) | 2017-02-15 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106406346B (en) | A kind of multiple no-manned plane collaboration rapid Cover search path planning method | |
| CN107977743B (en) | Multi-unmanned aerial vehicle cooperative task allocation method and device | |
| CN108919641B (en) | Unmanned aerial vehicle flight path planning method based on improved goblet sea squirt algorithm | |
| CN107886201B (en) | Multi-objective optimization method and device for multi-unmanned aerial vehicle task allocation | |
| CN111240353B (en) | Unmanned aerial vehicle collaborative air combat decision method based on genetic fuzzy tree | |
| CN106969778B (en) | Path planning method for cooperative pesticide application of multiple unmanned aerial vehicles | |
| CN107807665B (en) | Unmanned aerial vehicle formation detection task cooperative allocation method and device | |
| CN108549402A (en) | Unmanned aerial vehicle group method for allocating tasks based on quantum crow group hunting mechanism | |
| CN111311049B (en) | Multi-agent cooperative task allocation method | |
| CN110375761A (en) | Automatic driving vehicle paths planning method based on enhancing ant colony optimization algorithm | |
| CN105512769A (en) | Unmanned aerial vehicle route planning system and unmanned aerial vehicle route planning method based on genetic programming | |
| CN104573812A (en) | Uninhabited combat air vehicle route path determining method based on PGSO (Particle-Glowworm Swarm Optimization) algorithm | |
| CN113671987B (en) | Multi-machine distributed time sequence task allocation method based on non-deadlock contract net algorithm | |
| CN112327923A (en) | Multi-unmanned aerial vehicle collaborative path planning method | |
| CN108548538B (en) | Method and system for multi-station multi-unmanned aerial vehicle task allocation and flight path planning | |
| CN110262563A (en) | Multiple no-manned plane collaboratively searching mesh calibration method waterborne | |
| CN108594645B (en) | Planning method and system for single-station multi-unmanned aerial vehicle distribution and flight route | |
| CN107330560A (en) | A kind of multitask coordinated distribution method of isomery aircraft for considering temporal constraint | |
| CN114417735B (en) | Multi-unmanned aerial vehicle cooperative task planning method in cross-regional combined combat | |
| CN115755963B (en) | Unmanned aerial vehicle group collaborative mission planning method considering carrier delivery mode | |
| CN112733251B (en) | Collaborative flight path planning method for multiple unmanned aerial vehicles | |
| CN111797966B (en) | Multi-machine collaborative global target distribution method based on improved flock algorithm | |
| CN110986960A (en) | Unmanned aerial vehicle track planning method based on improved clustering algorithm | |
| CN116702633B (en) | Heterogeneous warhead task reliability planning method based on multi-objective dynamic optimization | |
| CN110530373A (en) | A kind of robot path planning method, controller and system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |