CN107806877A - A kind of trajectory planning optimization method of four rotor wing unmanned aerial vehicles based on ant group algorithm - Google Patents
A kind of trajectory planning optimization method of four rotor wing unmanned aerial vehicles based on ant group algorithm Download PDFInfo
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Abstract
The invention discloses a kind of trajectory planning optimization method of four rotor wing unmanned aerial vehicles based on ant group algorithm, divides planning space first with 3 d grid figure method, then draws the three-dimensional environment map based on 3 d grid figure, finally carry out four rotor wing unmanned aerial vehicle path plannings.The present invention considers the own characteristic of unmanned plane and extraneous complex environment, and the three dimensional environmental model of a high level is established when model is established to reduce and encounters the probability of barrier.In terms of planning algorithm, the present invention uses three-dimensional ant group algorithm, the search of flight path is completed by choose principal direction, the change principal direction search strategy of invention is employed in the improvement of algorithm simultaneously and simplifies flight path strategy, when successfully solving traditional three-dimensional ant group algorithm progress flight path search substantially, two planes, which are joined directly together, easily makes flight path directly through barrier, and the flight path node searched out is more, the problem of fitness value is excessive, avoiding obstacles well, path length is reduced, improves search efficiency.
Description
Technical field
The invention belongs to areas of information technology, are related to a kind of ant group algorithm path planning method of four rotor wing unmanned aerial vehicles, tool
Body is related to a kind of trajectory planning optimization method of four rotor wing unmanned aerial vehicles based on ant group algorithm.
Background technology
In recent years, the development of unmanned plane was very rapid, and various model unmanned planes occur in succession, but four rotor wing unmanned aerial vehicles
Following all many deficiencies be present in trajectory planning：
Most of existing path planning algorithm can only plan two dimensional surface path, and in general three-dimensional planning algorithm, greatly
Most mathematical algorithms are complicated, it is necessary to very big memory space, while are difficult to global path planning.
When application three-dimensional ant group algorithm substantially carries out flight path search, two planes be joined directly together easily make flight path directly through
Barrier, and the flight path node searched out is more, and fitness value is excessive.
For drawbacks described above, a series of solution is there has been, wherein the theory used, specifically can be total
Knot is as follows：
(1) environment geometric modeling based on grid map method；
Environment geometric modeling grid map method based on grid map method, is the list for free space C being divided into series of rules
First lattice, judge according to whether there is the area coverage of barrier and barrier in cell.Grid map in robot path planning
Method is used widely.According to the dimension of free space, grid map method can be divided into two-dimensional grid trrellis diagram method and 3 d grid figure method.
See Fig. 1, two-dimensional grid trrellis diagram is a planar graph, and 3 d grid figure is a three-dimensional mould magic square shape.In two-dimensional grid trrellis diagram, if
The maximum of X-axis is Xmax, minimum value Xmin, X-axis dividing unit size is XGrid, the maximum of Y-axis is Ymax, minimum value is
Ymin, Y-axis dividing unit size is YGrid, then total lattice number N of two-dimensional grid trrellis diagram be：
See Fig. 2, for 3 d grid figure, if the maximum X of X-axismax, minimum value Xmin, X-axis dividing unit size is
XGrid, the maximum Y of Y-axismax, minimum value Ymin, Y-axis dividing unit size is YGrid, the maximum Z of Z axismax, minimum value Zmin, Z
Axle dividing unit size is ZGrid, then total lattice number N of 3 d grid figure be：
(2) three-dimensional ant group algorithm；
Ant group algorithm is a kind of probability search method, it by the use of pheromone as ant select follow-up behavior according to
According to.Every ant can make a response to the pheromone that other ants spread in certain limit, according to pheromone
Intensity mulitpath selection is made by the judgement on probability and performs the selection at each road junction, thus perceive and influence
Their later behaviors.Inspired by this mode of looking for food of ant, M.Dorig o, V.Maniez-zo, A.Colo rini etc.
People first proposed ant group algorithm in 1991, and be applied to TSP problems, achieve certain effect.Here it is main to discuss
Because with dynamic, distributivity and concertedness etc., some are special in application problem of the ant group algorithm in Path Planning for Unmanned Aircraft Vehicle
Property, ant group algorithm will have good development prospect in routeing even mission planning.
The content of the invention
In order to solve the above-mentioned technical problem, the invention provides a kind of flight path of four rotor wing unmanned aerial vehicles based on ant group algorithm
Planning and optimization method.
A kind of trajectory planning optimization method of four rotor wing unmanned aerial vehicles based on ant group algorithm provided by the invention, its feature exist
In comprising the following steps：
Step 1：Planning space is divided using 3 d grid figure method；
Step 2：Draw the three-dimensional environment map based on 3 d grid figure；
Step 3：Carry out four rotor wing unmanned aerial vehicle path plannings.
The present invention is for solving the problems, such as the trajectory planning with four rotor wing unmanned aerial vehicles of optimization；In terms of model is established, it is contemplated that
The own characteristic of unmanned plane and extraneous complex environment, the present invention can establish high level when model is established
Three dimensional environmental model encounters the probability of barrier to reduce.In terms of planning algorithm, the present invention using three-dimensional ant group algorithm,
The search of flight path is completed by choose principal direction, while the change principal direction search plan of invention is employed in the improvement of algorithm
Slightly and simplify flight path strategy, when successfully solving traditional three-dimensional ant group algorithm progress flight path search substantially, the direct phase of two planes
The problem of company easily makes flight path directly through barrier, and the flight path node searched out is more, and fitness value is excessive, well
Avoiding obstacles, path length is reduced, improve search efficiency.
Brief description of the drawings
Fig. 1 is two-dimensional grid trrellis diagram in background of invention；
Fig. 2 is 3 d grid figure in background of invention；
Fig. 3 is that the trajectory planning schematic diagram that X-direction is principal direction is selected in the embodiment of the present invention.
Embodiment
Understand for the ease of those of ordinary skill in the art and implement the present invention, below in conjunction with the accompanying drawings and embodiment is to this hair
It is bright to be described in further detail, it will be appreciated that implementation example described herein is merely to illustrate and explain the present invention, not
For limiting the present invention.
The problem of three-dimensional ant group algorithm solves the problems, such as is mainly three-dimensional route planning, the model and basic ant group algorithm mould
Type has many differences.It is deformed on the basis of former ant group algorithm and innovatory algorithm, needed in deformation
In view of the environmental model of trajectory planning.Here, the present invention will use 3 d grid figure as environmental model.Because directly three
Tie up the amount of calculation for scanning for finding optimal path needs in grid map to all grids and the calculating time is very big, so here
The method of use is not that all grids are scanned for, but part grid is scanned for find optimal trajectory.
A kind of trajectory planning optimization method of four rotor wing unmanned aerial vehicles based on ant group algorithm provided by the invention, including it is following
Step：
Step 1：Planning space is divided using 3 d grid figure method；
Invention divides planning space first with 3 d grid figure method.When using 3 d grid figure as environmental model just
It must account for the resolution problem of 3 d grid, resolution ratio can neither too greatly can not be too small.Consider 3 d grid resolution ratio
When, first have to consider the problem of be exactly four rotor wing unmanned aerial vehicles volume.Ensure that four rotor wing unmanned aerial vehicles can in flight course
Without impinging on barrier, the length of grid is greater than the length of four rotors, in addition it is contemplated that error.An other side
Face, the positioning method of four rotors is also contemplated that when setting 3 d grid resolution ratio.GPS location is positioning method the most frequently used at present
One of, a GPS navigation module can is installed on four rotor wing unmanned aerial vehicles by receiving gps signal, and then judge four rotors
The current location of unmanned plane, so present invention selection uses GPS location.Often changing 0.001' during GPS location, on latitude is about
1.837m, it is about 1.281m often to change 0.001' on longitude, can there is 1~2m error during GPS location.To sum up consider above-mentioned two
The factor of individual aspect, can be according to actual conditions using latitude value as abscissa, and longitude is as ordinate, with ground obstacle
Ordinate is highly used as, perpendicular to sea level.So divide 3 d grid unit, it is possible to take into account the size of four rotors, examine again
The error of GPS location is considered, so as to reduce the probability for encountering barrier.
This has two schemes when being divided to grid cell：As long as one kind is that have barrier to decide that the unit in cell
Lattice are intransitable；Whether another scheme is can be by come judging unit lattice according to barrier area coverage in cell,
The area that barrier accounts for i.e. in cell if greater than certain proportion be considered as the cell be can not be by being otherwise taken as
It can pass through.The possibility that second scheme finds optimal solution than the first scheme is high, but unmanned plane encounter barrier can
Energy property is also higher, therefore selects some more of the first scheme.
Step 2：Draw the three-dimensional environment map based on 3 d grid figure；
Step 3：Carry out four rotor wing unmanned aerial vehicle path plannings；Specific implementation includes following sub-step：
Step 3.1：Parameter initialization set, including the determination of starting point, the selection of principal direction, the determination of population number, repeatedly
The selection of generation number, flight path search area are chosen, pheromones Initialize installation；
The determination of starting point, it is assumed that the coordinate value (X of the grid where four rotor wing unmanned aerial vehicle S starting pointsstart, Ystart,
Zstart), the coordinate value (X of grid where terminating pointend, Yend, Zend), 3 d grid map origin value is (XGridstart,
YGridstart, ZGridstart), then four rotor wing unmanned aerial vehicle S placement location (Slat, Slon, Sh) and its where grid grid coordinate
Position (Xstart, Ystart, Zstart) relation be：
Wherein：XGridRepresent the unit-sized of X-axis division, YGridRepresent the unit-sized of Y-axis division, ZGridRepresent that Z axis is drawn
The unit-sized divided, SlatRepresent the coordinate for the X-axis that current unmanned plane is placed, SlonRepresent the seat for the Y-axis that current unmanned plane is placed
Mark, ShThe coordinate for the Z axis that current unmanned plane is placed is represented, ceil represents to round to positive infinity.
The selection of principal direction, it is to select grid in latitude direction and longitudinal to change the most direction of number as four rotors
Unmanned aerial vehicle flight path plans principal direction；
The selection of principal direction in the present embodiment, it is the changing value size for comparing starting point and ending point transverse and longitudinal coordinate, that is, compares
Compared with (Xstart- Xend)/XGrid(Ystart- Yend)/YGridSize, if (Xstart- Xend)/XGridMore than (Ystart-
Yend)/YGrid, then X-direction is selected as principal direction；Otherwise it is principal direction to select Y-direction；
The determination of the population number, artificially being determined according to actual conditions, quantity is critically important, because when quantity is excessive,
Pheromones change tends to be average on the path that can cause to search for, thus the bad path found out；When quantity is too small, easily
The routing information element for making not to be searched is reduced to 0, so it is possible that precocious, does not find globally optimal solution.In this implementation
Population number can be fixed tentatively in example as 25.
The selection of the iterations, artificially determined according to actual conditions, quantity is critically important, because iterations value
Too small, may causing algorithm, also no convergence has not just terminated；It is excessive, the wasting of resources can be caused, can be fixed tentatively repeatedly in the present embodiment
Algebraically is 50.
Flight path search area is chosen, it is assumed that selected X-direction is principal direction, along X-direction from XstartTo XendIt is divided into n=|
Xstart- Xend|+1 plane, numbering Π1, Π2, Π3..., Πn, then four rotor wing unmanned aerial vehicle flight paths be just divided into (n-1) section；
Assuming that four rotor wing unmanned aerial vehicles are run to i-th of plane ΠiOn a bit (Xi, Yi, Zi) place, then the grid of next operation is just
In Πi+1On；The detailed process of next grid coordinate selection for：For on principal direction X directly with plane Πi+1Abscissa make
For the abscissa of next node, i.e., new X-coordinate value is Xi+1；Selection for Y-direction and Z-direction coordinate value is not direct
Selection, but in plane Πi+1Select the grid of no barrier to be put into array Allowed, be otherwise rejected,；Then from
One grid point of middle selection is as next operation grid；But because all grids are carried out directly in 3 d grid figure
Search find the amount of calculation of optimal path needs and during calculating it is very big, so method employed herein is not to all grids
Scan for, but part grid is scanned for find optimal trajectory.For non-principal direction Y, from plane ΠiTo plane
Πi+1, in Y-direction with YiCentered on from Yi- bcmaxTo Yi+bcmaxIn the range of point be all to be selectable as Yi+1Point；Together
Sample, with Z in Z-directioniCentered on from Zi- hcmaxTo Zi+hcmaxIn the range of point be all to be selectable as Zi+1Point；
Wherein, bcmaxRepresent the radius searched in Y-axis, hcmaxRepresent the radius searched on Z axis.
See Fig. 3, for X-direction, probability meter of any one grid (X, Y, Z) as next operation grid in plane
It is：
In formula：τ (X, Y, Z) is plane Πi+1Upper coordinate is the pheromones value of the grid of (X, Y, Z)；H (X, Y, Z) is plane
Πi+1Upper coordinate is the heuristic function of the grid of (X, Y, Z), and its calculation formula is：
H (X, Y, Z)=D (X, Y, Z)ω1× S (X, Y, Z)ω2× Q (X, Y, Z)ω3；
In formula：D (X, Y, Z) is the path length of current point and (X, Y, Z), and this can promote ant to select then distance as far as possible
The nearest point of current point, calculation formula are：
S (X, Y, Z) represents safety factor, promotes ant to select point of safes；Current grid (Xi, Yi, Zi) and (X, Y, Z)
It can not connect, or have barrier in grid (X, Y, Z), then it is inaccessible to claim the grid；S (X, Y, Z) calculating is public
Formula is：
Q (X, Y, Z) is that grid (X, Y, Z) arrives termination grid (Xend, Yend, Zend) distance, Q (X, Y, Z) can promote ant
Ant select from target grid closer to grid, its calculation formula is：
ω1、ω2、ω3It is coefficient, its size represents the importance degree of above-mentioned each factor, and the bigger representative of coefficient value should
Item is more important, otherwise illustrates that this is more inessential；
Pheromones Initialize installation, all grid pheromones values in the three-dimensional environment map of 3 d grid figure are arranged to
Fixed value；
Step 3.2：Flight path is searched for；
In flight path search procedure, it is assumed that the kth ant in PopNum ant has been run to plane ΠiOn point
(Xi, Yi, Zi) place, search in plane Πi+1On with (Xi+1, Yi, Zi) centered on count=(2 × bcmax+1)×(2×hcmax+1)
Individual point；Feasible grid all in count grid is put into array Allowed, feasible grid is the grid of no barrier
Lattice；If array Allowed is sky, then by current point (Xi, Yi, Zi) ZiCoordinate value adds 1, and current point coordinates is changed into (Xi,
Yi, Zi+1), the feasible grid in plane is re-searched for, until array Allowed is not sky；According to wheel from array Allowed
Disk gambling method selects a feasible grid as plane Πi+1On flight path node；
Step 3.3：To plane ΠiOn node carry out local information element renewal；
Ant is to scan for path according to pheromone concentration in ant group algorithm, is often covering one section or All Paths
When it is necessary to be updated to pheromones value, so pheromones initial value setting and renewal whether can succeed for ant group algorithm
Search has material impact.Here pheromones value is stored in the point of three dimensions series of discrete, it is then discrete to these
The pheromones value of point is updated, so there is a pheromones value for each grid, this pheromones value is with regard to generation
Table attraction degree of the grid to ant, the pheromones value of each grid will be updated after ant passes through.Information
The renewal of element is divided into local updating and global renewal two parts.As long as local updating refers to have ant to pass through certain grid, the grid
Pheromones value will be updated, the pheromones value of grid can be reduced after renewal, so in the search of this grid afterwards
Selected probability is just lowered, and correspondingly increases the probability that other grids not being searched are searched, and can thus be reached
To the purpose of global search.
The Pheromone update formula of Local Search is：
In formula, ζ represents pheromones attenuation coefficient, τX, Y, ZRepresent the pheromones value of grid (X, Y, Z).
When global information element renewal refers to that ant completes the search of flight path, the fitness value in the path is calculated, from existing
Most short flight path, the pheromones for all grids that the minimum flight path of renewal fitness value is passed through are selected in the path searched
Value, Pheromone update formula are
τX,Y,Z=(1- ρ) τX,Y,Z+ρΔτX,Y,Z
In formula：Length (m) represents the path length that ant m passes through, and ρ represents pheromones volatility coefficient, and K is coefficient.
Step 3.4：Step 3.2 and step 3.3 are repeated, until reaching plane ΠN-1, then searched using change principal direction
Rope strategy and simplified flight path strategy；
When application three-dimensional ant group algorithm substantially carries out flight path search, two planes be joined directly together easily make flight path directly through
Barrier, and the flight path node searched out is more, and fitness value is excessive.So algorithm is entered for the two problems present invention
Improvement is gone, it is proposed that become principal direction search strategy and simplify flight path strategy.
1. become principal direction search strategy：
By ΠN-1Node and Π in planenNode in plane is that terminal is joined directly together and easily propagates through barrier.Therefore it is first
First need to judge the connectedness of point-to-point transmission.Want to know in space that whether connect, and first has to judge which space line passes through at 2 points
A little grids, the obstacle height and straight line height relationships of the longitude and latitude position where these grids are then judged, if barrier
Highly higher than straight line height, then this straight line passes through barrier, that is to say, that 2 points can not connect in space, otherwise
It can connect.And when needing to judge 3 d-line projection by which grid, as long as finding out the transverse and longitudinal coordinate of straight line and grid
Intersection point can.
Becoming the basic thought of principal direction search strategy is:If ΠN-1Node and Π in planenTerminal is not in plane
Connection, then just with ΠN-1Node (X in planeN-1, YN-1, ZN-1) it is starting point, with ΠnTerminal (X in planeend,
Yend, Zend) it is terminating point, search again for flight path.Plane is divided into Π to note in the principal direction of search for the first time1,1, Π1,2...,
Π1, n1, search node is followed successively by (X1,1, Y1,1, Z1,1), (X1,2, Y1,2, Z1,2) ..., (X1, n1-1, Y1, n1-1, Z1, n1-1)；Similarly,
Plane division in the principal direction of the m times search is designated as ΠM, 1, ΠM, 2..., ΠM, nm, the node of search is designated as (X successivelyM, 1,
YM, 1, ZM, 1), (XM, 2, YM, 2, ZM, 2) ..., (XM, nm-1, YM, nm-1, ZM, nm-1).Final Three-dimensional Track is：
(X1,1, Y1,1, Z1,1) ..., (X1, n1-1, Y1, n1-1, Z1, n1-1), (X2,2, Y2,2, Z2,2) ..., (X2, n2-1,
Y2, n2-1, Z2, n2-1) ..., (XM, 2, YM, 2, ZM, 2) ..., (XM, nm-1, YM, nm-1, ZM, nm-1), (Xend, Yend, Zend)
2. simplify flight path strategy：
Flight path node excessively means that four rotor wing unmanned aerial vehicles need turnover number excessive during flight, using certain
Simplified strategy can reduce fitness value reducing flight path node and can also reduce flight path main thought and be:Searched assuming that becoming principal direction
The feasible flight path that rope strategy is searched out is route=(r1, r2..., rn).First node r1It is put into new flight path array
In newroute, r is judged1With rnConnectedness, if connection, r1It is put into array newroute；Otherwise r is judged1With
rN-1Connectedness, until finding a point riWith r1It is connection.To riIdentical operation is carried out, until terminating point.
Step 3.5：The fitness value of every flight path is calculated according to fitness value function, compares and finds out minimum fitness value,
And flight path corresponding to minimum fitness is current optimal trajectory；
Fitness value function is：W=k1c1L+k2c2/dmin+(1-k1-k2)c3H；
Wherein, W is fitness value.k1.k2For weight coefficient, can be adjusted according to different mission requirementses.c1.c2.c3
For proportionality coefficient.L is distance.dminFor the every bit on path from most dangerous point with a distance from.H is the height of every bit plane off sea
Degree.
Step 3.6：Global information element renewal is carried out, i.e., selects most short flight path from the existing path searched,
The pheromones value for all grids that the minimum flight path of renewal fitness value is passed through.
τX,Y,Z=(1- ρ) τX,Y,Z+ρΔτX,Y,Z
In formula:Length (m) represents the path length that ant m passes through；ρ represents pheromones volatility coefficient；K is coefficient.
Step 3.7：By above-mentioned steps 3.2- step 3.6 process iteration n times, the optimal trajectory of iteration n times is found.
Step 4：Using the longitude of the environmental model arrived after tested, latitude and altitude information drawing three-dimensional environmental map,
Then optimal trajectory is drawn in three-dimensional environment map.
It should be appreciated that the part that this specification does not elaborate belongs to prior art.
It should be appreciated that the above-mentioned description for preferred embodiment is more detailed, therefore can not be considered to this
The limitation of invention patent protection scope, one of ordinary skill in the art are not departing from power of the present invention under the enlightenment of the present invention
Profit is required under protected ambit, can also be made replacement or deformation, be each fallen within protection scope of the present invention, this hair
It is bright scope is claimed to be determined by the appended claims.
Claims (9)
1. a kind of trajectory planning optimization method of four rotor wing unmanned aerial vehicles based on ant group algorithm, it is characterised in that including following step
Suddenly：
Step 1：Planning space is divided using 3 d grid figure method；
Step 2：Draw the three-dimensional environment map based on 3 d grid figure；
Step 3：Carry out four rotor wing unmanned aerial vehicle path plannings.
2. the trajectory planning optimization method of four rotor wing unmanned aerial vehicles according to claim 1 based on ant group algorithm, its feature
It is：In step 1, using latitude value as abscissa, longitude is sat as ordinate using the height of ground obstacle as perpendicular
Mark, perpendicular to sea level, divide 3 d grid unit；If having barrier in cell, it is to pass through to judge the cell
Cell.
3. the trajectory planning optimization method of four rotor wing unmanned aerial vehicles according to claim 1 based on ant group algorithm, its feature
It is, the specific implementation of step 3 includes following sub-step：
Step 3.1：Parameter initialization is set, including the determination of starting point, the selection of principal direction, the determination of population number, iteration time
Several selection, flight path search area are chosen, pheromones Initialize installation；
The determination of the starting point, it is assumed that the coordinate value (X of the grid where four rotor wing unmanned aerial vehicle S starting pointsstart, Ystart,
Zstart), the coordinate value (X of grid where terminating pointend, Yend, Zend), 3 d grid map origin value is (XGridstart,
YGridstart, ZGridstart), then four rotor wing unmanned aerial vehicle S placement location (Slat, Slon, Sh) and its where grid grid coordinate
Position (Xstart, Ystart, Zstart) relation be：
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Wherein：XGridRepresent the unit-sized of X-axis division, YGridRepresent Y-axis dividing unit size, ZGridRepresent the list of Z axis division
Position size, SlatRepresent the coordinate for the X-axis that current unmanned plane is placed, SlonRepresent the coordinate for the Y-axis that current unmanned plane is placed, ShTable
Show the coordinate for the Z axis that current unmanned plane is placed, ceil represents to round to positive infinity；
The selection of the principal direction, it is to select grid in latitude direction and longitudinal to change the most direction of number as four rotors
Unmanned aerial vehicle flight path plans principal direction；
The determination of the population number, artificially determined according to actual conditions；
The selection of the iterations, artificially determined according to actual conditions；
The flight path search area is chosen, it is assumed that selected X-direction is principal direction, along X-direction from XstartTo XendIt is divided into n=|
Xstart- Xend|+1 plane, numbering Π1, Π2, Π3..., Πn, then four rotor wing unmanned aerial vehicle flight paths be just divided into (n-1) section；
Assuming that four rotor wing unmanned aerial vehicles are run to i-th of plane ΠiOn a bit (Xi, Yi, Zi) place, then the grid of next operation is just
In Πi+1On；The detailed process of next grid coordinate selection for：For on principal direction X directly with plane Πi+1Abscissa make
For the abscissa of next node, i.e., new X-coordinate value is Xi+1；Selection for Y-direction and Z-direction coordinate value is not direct
Selection, but in plane Πi+1Select the grid of no barrier to be put into array Allowed, be otherwise rejected；Then from
One grid point of middle selection is as next operation grid；Part grid is scanned for find optimal trajectory；For non-master
Direction Y, from plane ΠiTo plane Πi+1, in Y-direction with YiCentered on from Yi- bcmaxTo Yi+bcmaxIn the range of point all
It is to be selectable as Yi+1Point；Equally, with Z in Z-directioniCentered on from Zi- hcmaxTo Zi+hcmaxIn the range of point
All it is to be selectable as Zi+1Point；Wherein, bcmaxRepresent the radius searched in Y-axis, hcmaxRepresent what is searched on Z axis
Radius；
Described information element Initialize installation, all grid pheromones values in the three-dimensional environment map of 3 d grid figure are arranged to
Fixed value；
Step 3.2：Flight path is searched for；
In flight path search procedure, it is assumed that the kth ant in PopNum ant has been run to plane ΠiOn point (Xi, Yi,
Zi) place, search in plane Πi+1On with (Xi+1, Yi, Zi) centered on count=(2 × bcmax+1)×(2×hcmax+ 1) individual point；
Feasible grid all in count grid is put into array Allowed, feasible grid is the grid of no barrier；Such as
Fruit array Allowed is sky, then by current point (Xi, Yi, Zi) ZiCoordinate value adds 1, and current point coordinates is changed into (Xi, Yi,
Zi+1), the feasible grid in plane is re-searched for, until array Allowed is not sky；According to roulette from array Allowed
Method selects a feasible grid as plane Πi+1On flight path node；
Step 3.3：To plane ΠiOn node carry out local information element renewal；
Step 3.4：Step 3.2 and step 3.3 are repeated, until reaching plane ΠN-1, then using change principal direction search plan
Omit and simplify flight path strategy；
Step 3.5：The fitness value of every flight path is calculated according to fitness value function, compares and finds out minimum fitness value, and most
Flight path corresponding to small fitness is current optimal trajectory；
Fitness value function is：
W=k1c1L+k2c2/dmin+(1-k1-k2)c3H；
Wherein, W is fitness value, k1、k2For weight coefficient, c1、c2、c3For proportionality coefficient, L is distance, dminTo be every on path
A little from most dangerous point with a distance from, H be every bit plane off sea height；
Step 3.6：Carry out global information element renewal；
Most short flight path is selected from the existing path searched, the minimum flight path of renewal fitness value is passed through all
The pheromones value of grid；
Step 3.7：By above-mentioned steps 3.2- step 3.6 process iteration n times, the optimal trajectory of iteration n times is found.
4. the trajectory planning optimization method of four rotor wing unmanned aerial vehicles according to claim 3 based on ant group algorithm, its feature
It is, the selection of principal direction described in step 3.1, is the changing value size for comparing starting point and ending point transverse and longitudinal coordinate, that is, compares
Compared with (Xstart- Xend)/XGrid(Ystart- Yend)/YGridSize, if (Xstart- Xend)/XGridMore than (Ystart-
Yend)/YGrid, then X-direction is selected as principal direction；Otherwise it is principal direction to select Y-direction.
5. the trajectory planning optimization method of four rotor wing unmanned aerial vehicles according to claim 3 based on ant group algorithm, its feature
It is, in step 3.1, for X-direction, probability meter of any one grid (X, Y, Z) as next operation grid in plane
It is：
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In formula：τ (X, Y, Z) is plane Πi+1Upper coordinate is the pheromones value of the grid of (X, Y, Z)；H (X, Y, Z) is plane Πi+1
Upper coordinate is the heuristic function of the grid of (X, Y, Z), and its calculation formula is：
H (X, Y, Z)=D (X, Y, Z)ω1× S (X, Y, Z)ω2× Q (X, Y, Z)ω3；
In formula：D (X, Y, Z) is the path length of current point and (X, Y, Z), and this can promote ant to select as far as possible then apart from current
The nearest point of point, calculation formula are：
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S (X, Y, Z) represents safety factor, promotes ant to select point of safes；Current grid (Xi, Yi, Zi) and (X, Y, Z) be not
It can connect, or have barrier in grid (X, Y, Z), then it is inaccessible to claim the grid；S (X, Y, Z) calculation formula is：
Q (X, Y, Z) is that grid (X, Y, Z) arrives termination grid (Xend, Yend, Zend) distance, Q (X, Y, Z) can promote ant to select
Select from target grid closer to grid, its calculation formula is：
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ω1、ω2、ω3It is coefficient, its size represents the importance degree of above-mentioned each factor, and coefficient value is bigger to represent the Xiang Yue
It is important, otherwise illustrate that this is more inessential.
6. the trajectory planning optimization method of four rotor wing unmanned aerial vehicles according to claim 3 based on ant group algorithm, its feature
It is, to plane Π described in step 3.3iOn node carry out local information element renewal, local information element more new formula is：
In formula, ζ represents pheromones attenuation coefficient, τX, Y, ZRepresent the pheromones value of grid (X, Y, Z).
7. the trajectory planning optimization method of four rotor wing unmanned aerial vehicles according to claim 3 based on ant group algorithm, its feature
Be, described in step 3.4 become principal direction search strategy into：If ΠN-1Node and Π in planenTerminal is not in plane
Connection, then just with ΠN-1Node (X in planeN-1, YN-1, ZN-1) it is starting point, with ΠnTerminal (X in planeend,
Yend, Zend) it is terminating point, search again for flight path；
Plane is divided into Π to note in the principal direction of search for the first time1,1, Π1,2..., Π1, n1, search node is followed successively by (X1,1,
Y1,1, Z1,1), (X1,2, Y1,2, Z1,2) ..., (X1, n1-1, Y1, n1-1, Z1, n1-1)；Similarly, it is flat in the principal direction of the m times search
Face division is designated as ΠM, 1, ΠM, 2..., ΠM, nm, the node of search is designated as (X successivelyM, 1, YM, 1, ZM, 1), (XM, 2, YM, 2,
ZM, 2) ..., (XM, nm-1, YM, nm-1, ZM, nm-1)；Final Three-dimensional Track is：
(X1,1, Y1,1, Z1,1) ..., (X1, n1-1, Y1, n1-1, Z1, n1-1), (X2,2, Y2,2, Z2,2) ..., (X2, n2-1, Y2, n2-1,
Z2, n2-1) ..., (XM, 2, YM, 2, ZM, 2) ..., (XM, nm-1, YM, nm-1, ZM, nm-1), (Xend, Yend, Zend)。
8. the trajectory planning optimization method of four rotor wing unmanned aerial vehicles according to claim 3 based on ant group algorithm, its feature
It is, simplifying flight path strategy described in step 3.4 is：Assuming that become a feasible flight path being searched out of principal direction search strategy into
Route=(r1, r2..., rn)；First node r1It is put into new flight path array newroute, judges r1With rnConnectedness,
If connection, r1It is put into array newroute；Otherwise r is judged1With rN-1Connectedness, until finding a point riWith
r1It is connection；To riIdentical operation is carried out, until terminating point.
9. the trajectory planning optimization method of four rotor wing unmanned aerial vehicles according to claim 3 based on ant group algorithm, its feature
It is：Global information element described in step 3.6 updates, the information for all grids that the minimum flight path of renewal fitness value is passed through
Element value, Pheromone update formula are：
τX,Y,Z=(1- ρ) τX,Y,Z+ρΔτX,Y,Z；
<mrow>
<msub>
<mi>&Delta;&tau;</mi>
<mrow>
<mi>X</mi>
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</msub>
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<mi>K</mi>
<mrow>
<mi>m</mi>
<mi>i</mi>
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</mrow>
</mfrac>
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</mrow>
In formula:Length (m) represents the path length that ant m passes through, and ρ represents pheromones volatility coefficient, and K is coefficient.
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Application publication date: 20180316 |
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