CN109445463B - Unmanned aerial vehicle dynamic route planning method - Google Patents
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Abstract
The invention relates to a method for planning a dynamic route of an unmanned aerial vehicle, which comprises the following steps: 1) establishing a route cost estimation model: constructing a communication signal threat model and a terrain surface threat model, weighting and superposing the communication signal threat model and the terrain surface threat model to form a comprehensive threat model, and realizing the evaluation of a planned route on the basis of the comprehensive threat model; 2) calculating a flight route: and constructing a comprehensive coordinate set random flight line set, realizing dynamic updating of the flight line set aiming at the threat change condition, optimizing a planned flight path point set in the flight line, and selecting the flight line with the maximum safety performance and the capability of flying for each airplane. The invention relates to a method for selecting routes in consideration of flight safety, range, airplane performance and the like. The calculation results show that the planned flight route realizes real-time planning and route optimization of the global optimal route in a complex environment, and can avoid various dynamic threats in time, thereby improving the safety and reliability of flight.
Description
Technical Field
The invention belongs to the field of unmanned aerial vehicle route planning, and relates to a method for planning an optimal flight route by using information such as unmanned aerial vehicle performance, environmental threat and the like, in particular to a dynamic route planning method for an unmanned aerial vehicle.
Background
So far, the research on the air route planning in complex environment at home and abroad is still preliminary, and a complete and systematic unmanned aerial vehicle dynamic flight air route planning model and method are not formed yet. The unmanned aerial vehicle dynamic route planning in the complex geographic environment is not well solved at present, how to plan the flight route with the maximum safety performance by applying an artificial clustering algorithm is a problem to be solved urgently, and the solution of the problem has extremely important application value for military use and civil use no matter how to avoid dynamic threats and realize aviation safety rescue.
Disclosure of Invention
The invention aims to provide a dynamic route planning method for an unmanned aerial vehicle, which can meet the requirement that a plurality of unmanned aerial vehicles plan routes in a concealed environment and improve the flight safety and efficiency as much as possible. The method comprises the following specific steps:
1) establishing a route cost estimation model:
(1) construction of communication signal threat model f1:
Wherein: n isxt=(xi+xi+1)/2
N in the formula (1) is the total number of track points in the route, xn=Xe,yn=Ye,(xi,yi)、(nxi,nyi) And (X)e,Ye) Respectively representing the ith track point coordinate, the ith adjusted new track point coordinate and the target point coordinate, wherein tn is the total number of communication threats, (xc)k,yck) And RkRespectively representing the center coordinate and the influence radius of the kth communication threat;
(2) construction of a terrain surface threat model f2:
In the formula (2), Mn represents the number of peaks, Hmaxi(xo) in terms of the height of the peak of the ith peakj,yoj) And (x)sj,ysj) Respectively representing the plane coordinate of the peak top of the jth peak and parameters descending along the x direction and the y direction;
(3) threat model f by communication signal1And a terrain surface threat model f2And weighted superposition to form a comprehensive threat model F, wherein the mathematical description is as follows:
wherein p is1、p2Weights, f, corresponding to communication threats and terrain threats, respectivelyi1、fi2Respectively a communication threat value and a terrain threat value at the ith track point;
(4) flight line evaluation function fit:
2) calculating a flight route, and specifically comprising the following steps:
(1) constructing a comprehensive coordinate set (xy) random route set
xy=(lb+(ub-lb)*rand(Nfp)*100) (5)
xy=x*100+y (6)
xy={xy1,xy2,...,xyi,...,xyNfp} (7)
In formulas (5) - (7), xy represents a comprehensive track point coordinate set, (x, y) are coordinate values of track points, rand (Nfp) generates Nfp random numbers between 0 and 1, lb and ub respectively represent upper limit and lower limit values of the track point coordinates, and xy and ub respectively represent upper limit and lower limit values of the track point coordinatesiThe comprehensive coordinate value of the ith track point is represented;
(2) dynamic update of a set of routes
ff1=xyi+c1*rand*(bestXY-xyi)+c2*rand*(pxyi-xyi) (9)
P, V in formula (8)pRespectively representing a randomly generated probability value and a probability value threshold, ff1、ff2Respectively is a comprehensive coordinate value corresponding to the random probability value less than or equal to the probability threshold value; c in formulae (9) to (10)1And c2Respectively representing global and local route adjustment parameters, rand representing generated random numbers between 0 and 1, bestXY and pxyiRespectively representing coordinate values corresponding to the global and ith integrated track points, mean P representing the average integrated track point set, a1And a2Representing the coefficients, pFit, corresponding to means P and pxy, respectivelyiAnd pFitn respectively, corresponds to pxyiAnd the cost value of the global optimal route, sumFit represents the sum of the cost of the global optimal route, d is a parameter for avoiding the route from having an infeasible solution, and realmin represents the minimum value which can be represented by a floating point number type in Matlab software;
(3) for planned intra-route track point set { (x)1,y1,h1),(x2,y2,h2),…(x12,y12,h12) Optimization is carried out, and the formula is as follows:
b0=1.0/6*(-1*u3+3*u2-3*u+1) (11)
b1=1.0/6*(3*u3-6*u2+4) (12)
b2=1.0/6*(-3*u3+3*u2+3*u+1) (13)
b3=1.0/6*u3 (14)
xn=b0*x(i)+b1*x(i+1)+b2*x(i+2)+b3*x(i+3) (15)
yn=b0*y(i)+b1*y(i+1)+b2*y(i+2)+b3*y(i+3) (16)
hn=b0*h(i)+b1*h(i+1)+b2*h(i+2)+b3*h(i+3) (17)
in the formulae (11) to (17), h (i) represents the height corresponding to the ith track pointThe spline curve parameters are u, b0, b1, b2 and b3 are coefficients of basis functions, xn、ynAnd hnThe abscissa, ordinate and elevation of the corresponding curve.
Compared with the prior art, the invention has the advantages that: the real-time planning and route optimization of the global optimal route in the complex environment are realized, and various dynamic threats can be avoided in time.
Detailed Description
The technical solution of the present invention is further illustrated by the following examples. In the embodiment, 1 winged dragon unmanned aerial vehicle executes the task of disaster monitoring, and 3 terrain threats are totally arranged at positions of (55km ), (15km, 25km) and (10km, 15 km); a total of 7 communication threats at (55km ), (10km, 50km), (15km, 25km), (30km, 25km), (10km, 15km), (40km, 15km) and (40km, 10 km);
the parameters are set as follows: the number of the sets of the to-be-selected routes is 30, the number of the tracks is 12, the global mean P and the adjustment parameter c of the local route pxy1、c21.5 and 1.5 respectively, and averaging the coefficient a of the integrated track point set mean P1And coefficient a of the set of synthetic track points pxy for the optimal route2Are all 1; the coordinates of the departure point and the target point are (1km, 1km, 150m) and (60km, 60km, 150m), respectively. The method comprises the following specific steps:
1) establishing a route cost estimation model:
(1) construction of communication signal threat model f1:
nxi=(xi+xi+1)/2
(2) Construction of a terrain surface threat model f2:
f2(xi,yi)=100*exp(-(xi-55)2/52-(yi-55)2/52)+100*exp(-(xi-10)2/52-(yi-50)2/52)+100*exp(-(xi-15)2/72-(yi-25)2/62)
(2)
(3) Threat model f by communication signal1And a terrain surface threat model f2And weighted superposition to form a comprehensive threat model F, wherein the mathematical description is as follows:
(4) flight line evaluation function fit:
2) calculating a flight route, and specifically comprising the following steps:
(1) constructing a comprehensive coordinate set (xy) random route set:
lb=[1,1,1,1,1,1,1,1,1,1,1,1]
ub=[60,60,60,60,60,60,60,60,60,60,60,60]
xy=(lb+(ub-lb)*rand(1,12)*100) (5)
xy=x*100+y (6)
xy={1001,2002,…,6060} (7)
(2) dynamic update of a set of routes
prob=rand(30,1)*0.2+0.8
ff1=xyi+1.5*rand*(bestX-xyi)+1.5*rand*(pX(i,:)-xyi) (9)
ff2=xyi+rand*(meanP-xyi)*1*exp(-pFit(i)/(sumPfit+realmin)*30))+1*(rand*2-1)*(pX(person,:)-xyi)*exp(-pFit(person)-pFit(i))/(|pFit(person)-pFit(i)|+realmin)*pFit(person)/(sumPfit+realmin)*30)(10)
(3) Optimizing a set of planned intra-route track points { (1, 1, 150), … (60, 60, 150) }:
u=0∶0.01∶1
b0=1.0/6*(-1*u3+3*u2-3*u+1) (11)
b1=1.0/6*(3*u3-6*u2+4) (12)
b2=1.0/6*(-3*u3+3*u2+3*u+1) (13)
b3=1.0/6*u3 (14)
xn=b0*x(i)+b1*x(i+1)+b2*x(i+2)+b3*x(i+3) (15)
yn=b0*y(i)+b1*y(i+1)+b2*y(i+2)+b3*y(i+3) (16)
hn=b0*h(i)+b1*h(i+1)+b2*h(i+2)+b3*h(i+3) (17)
and finishing the route planning.
Table 1 shows example communication and terrain threat parameters
Table 2 shows the results of the calculation of the dynamic flight path and the evaluation of the total flight path of the unmanned aerial vehicle
Experimental number | f1 | f2 | Total voyage evaluation value (kilometer) | Planning time (seconds) |
1 | 0.5 | 0.5 | 96.04 | 86.05 |
Table 2 shows that the weights of the landform and communication information threats in the experiment are both 0.5, the planned flight distance is 96.04 km, which is far less than the maximum flight distance of 4000 km, and the maximum flight speed is 280 km/h, and the flight time is at least 64.29 seconds and far longer than the real-time planning time of 5 seconds under the limitation of the minimum track segment of 5km, so that the online flight route planning can be realized, and the safety guarantee is provided for emergency rescue.
Claims (1)
1. An unmanned aerial vehicle dynamic route planning method is characterized by comprising the following steps:
1) establishing a route cost estimation model:
(1) construction of communication signal threat model f1:
Wherein: nxi=(xi+xi+1)/2
N in the formula (1) is the total number of track points in the route, xn=Xe,yn=Ye,(xi,yi)、(nxi,nyi) And (X)e,Ye) Respectively representing the ith track point coordinate, the ith adjusted new track point coordinate and the target point coordinate, wherein tn is the total number of communication threats, (xc)k,yck) And RkRespectively representing the center coordinate and the influence radius of the kth communication threat;
(2) construction of a terrain surface threat model f2:
In the formula (2), Mn represents the number of peaks, Hmaxi(x) is the elevation of the peak top of the ith peakoj,yoj) And (x)sj,ysj) Respectively representing the plane coordinate of the peak top of the jth peak and parameters descending along the x direction and the y direction;
(3) threat model f by communication signal1And a terrain surface threat model f2And weighted superposition to form a comprehensive threat model F, wherein the mathematical description is as follows:
wherein p is1、p2Weights, f, corresponding to communication threats and terrain threats, respectivelyi1、fi2Respectively a communication threat value and a terrain threat value at the ith track point;
(4) flight line evaluation function fit:
2) calculating a flight route, and specifically comprising the following steps:
(1) constructing a comprehensive coordinate set (xy) random route set
xy=(lb+(ub-lb)*rand(Nfp)*100) (5)
xy=x*100+y (6)
xy={xy1,xy2,...,xyi,...,xyNfp} (7)
In formulas (5) - (7), xy represents a comprehensive track point coordinate set, (x, y) are coordinate values of track points, rand (Nfp) generates Nfp random numbers between 0 and 1, lb and ub respectively represent upper limit and lower limit values of the track point coordinates, and xy and ub respectively represent upper limit and lower limit values of the track point coordinatesiThe comprehensive coordinate value of the ith track point is represented;
(2) dynamic update of a set of routes
ff1=xyi+c1*rand*(bestXY-xyi)+c2*rand*(pxyi-xyi) (9)
P, V in formula (8)pRespectively representing a randomly generated probability value and a probability value threshold, ff1、ff2Respectively is a comprehensive coordinate value corresponding to the random probability value less than or equal to the probability threshold value; c in formulae (9) to (10)1And c2Respectively representing global and local route adjustment parameters, rand representing generated random numbers between 0 and 1, bestXY and pxyiRespectively representing coordinate values corresponding to the global and ith integrated track points, mean P representing the average integrated track point set, a1And a2Representing the coefficients, pFit, corresponding to means P and pxy, respectivelyiAnd pFitn respectively, corresponds to pxyiAnd the cost value of the global optimal route, sumFit represents the sum of the cost of the global optimal route, d is a parameter for avoiding the route from having an infeasible solution, and realmin represents the minimum value which can be represented by a floating point number type in Matlab software;
(3) for planned intra-route track point set { (x)1,y1,h1),(x2,y2,h2),…(x12,y12,h12) Optimization is carried out, and the formula is as follows:
b0=1.0/6*(-1*u3+3*u2-3*u+1) (11)
b1=1.0/6*(3*u3-6*u2+4) (12)
b2=1.0/6*(-3*u3+3*u2+3*u+1) (13)
b3=1.0/6*u3 (14)
xn=b0*x(i)+b1*x(i+1)+b2*x(i+2)+b3*x(i+3) (15)
yn=b0*y(i)+b1*y(i+1)+b2*y(i+2)+b3*y(i+3) (16)
hn=b0*h(i)+b1*h(i+1)+b2*h(i+2)+b3*h(i+3) (17)
h (i) in the formulas (11) to (17) represents the elevation corresponding to the ith track point, the spline curve parameter is u, b0, b1, b2 and b3 are coefficients of basis functions, and xn、ynAnd hnThe abscissa, ordinate and elevation of the corresponding curve.
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