CN110793522B - Flight path planning method based on ant colony algorithm - Google Patents
Flight path planning method based on ant colony algorithm Download PDFInfo
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Abstract
The invention belongs to the technical field of unmanned aerial vehicles, and particularly relates to an ant colony algorithm-based flight path planning method. By organically combining the flight path planning problem with the ant colony algorithm, a brand new flight path planning problem different from the traditional area coverage optimizing flight path planning situation can be solved, namely the flight path planning problem that the starting point and the end point of a flight path are not specified, and the maximum continuous monitoring coverage range of a specified area is realized when the unmanned aerial vehicle group flies along the flight path is required.
Description
Technical Field
The invention belongs to the technical field of unmanned aerial vehicles, and particularly relates to an ant colony algorithm-based flight path planning method.
Background
An Unmanned Aerial Vehicle (UAV) is a short name for Unmanned aircraft, has the characteristics of low risk, low cost and good concealment, and occupies an important application position in the military and civil fields. The unmanned aerial vehicle flight path planning is to plan an optimal or suboptimal flight path for the unmanned aerial vehicle on the premise of comprehensively considering the arrival time, oil consumption, threat, flight area and other factors of the unmanned aerial vehicle so as to ensure that a flight task is satisfactorily completed. In practical applications of unmanned aerial vehicle reconnaissance, certain tasks require maximum coverage monitoring of a designated area. In order to pursue high-efficiency application, a reference track of the unmanned aerial vehicle needs to be planned in advance by a ground command center, so that the unmanned aerial vehicle can fly according to the reference track according to reconnaissance requirements. Therefore, the unmanned aerial vehicle coverage optimizing track planning technology is an important content of the flight mission of the unmanned aerial vehicle.
At present, the research on the unmanned aerial vehicle area coverage problem is generally less at home and abroad, wherein the research on the unmanned aerial vehicle area coverage problem is more representative; in 2006, the research of Agarwal also adopts the idea of area division, a flight area is divided into a plurality of rectangular sub-areas, the areas are allocated according to the capability of each unmanned aerial vehicle for executing the covering task, the unmanned aerial vehicle is simplified to only allow 90-degree and 180-degree turning, but the turning radius is not considered in the defect of the covering scheme; in 2010, Chenhai et al proposed a track planning algorithm for a convex polygon area, which converts the problem of track planning coverage of the convex polygon area into a problem of solving the width of the convex polygon, and the unmanned aerial vehicle only needs to fly along a Z-shaped route along the direction of a support parallel line when the width appears, but does not consider the influence of the minimum turning radius on the Z-shaped route in the flying process. Studies on obstacle avoidance; in 2012, Dong S et al used Dijkstra' S algorithm to find the optimal track based on the Voronoi diagram, and regarded the threats as one point, and selected the intersection point of the perpendicular bisector of the connecting lines between the threat points as the track point. The method can ensure that the flight path avoids each threat to the maximum extent, the safety is high, the flight path is long, the maximum turning angle constraint of the unmanned aerial vehicle is not considered, and the flight path can not fly. In 2016, Maini P et al use Dijkstra's algorithm to find the shortest track based on the visual map, regard each vertex of the polygonal obstacle as track point, and establish a turning angle constraint mechanism. The flight path obtained by the method is short, the maximum turning angle constraint of the unmanned aerial vehicle is met, and the safety is low because the flight path is close to an obstacle.
Most of the above methods for planning the area coverage tracks aim at the condition that the starting point and the end point of a required track are fixed, and an optimal track is formed by cutting an area, avoiding obstacles by planning and avoiding obstacles, restricting oil consumption and turning times, so that a specific unmanned aerial vehicle can realize the coverage of each cut area through a 'cattle-ploughing' flight route, and the purpose of avoiding the arrival of obstacles such as air-defense missiles at a flight target point is achieved. The flight path planned by the existing flight path planning method cannot reduce oil consumption; and the requirement on the starting point of the flight path is met when the flight path is planned, and the operation process is complex.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides an ant colony algorithm-based flight path planning method. The technical problem to be solved by the invention is realized by the following technical scheme:
an ant colony algorithm-based flight path planning method comprises the following steps:
step 3, obtaining a global optimal ant and an pheromone of the global optimal ant according to the predicted target node and the initial deflection angle;
step 4, respectively performing allowable error judgment and obstacle avoidance judgment on the N unmanned aerial vehicles in the global optimal ants according to the global optimal ants and the pheromones of the global optimal ants to obtain judgment results, and updating the optimal position deflection angles corresponding to the unmanned aerial vehicles in the global optimal ants according to the judgment results;
step 5, obtaining track position change angles of the N unmanned aerial vehicles in the global optimal ants at the next moment according to optimal position deflection angles corresponding to the N unmanned aerial vehicles in the global optimal ants;
step 6, judging whether N unmanned aerial vehicles in the global optimal ants complete the voyage or not, and ending when the voyage is completed; and when the voyage is not finished, jumping to the step 3.
In one embodiment of the present invention, the step 1 comprises:
1.1, setting a flyable area A, and designating a task monitoring area S in the flyable area A;
1.2, determining the time interval of single step planning;
1.3, according to the geometric relation between the current position of the unmanned aerial vehicle and the maximum turning angle, calculating a predicted target node reached by the unmanned aerial vehicle after a certain time interval.
In one embodiment of the present invention, the step 3 comprises:
3.1, setting the number of ants in the ant colony algorithm as L and the dimension of each ant as N to obtain an LxN dimensional initial ant colony;
3.2, calculating initial position information and an initial fitness value of the LxN dimensional initial ant colony according to the initial deflection angle;
3.3, storing the initial position information and the initial fitness value;
3.4, iterating the ant colony algorithm according to the initial deflection angle, the initial position information and the initial fitness value to obtain an initial pheromone;
3.5, obtaining a global optimal ant according to the initial pheromone;
and 3.6, obtaining pheromone of the global optimal ant according to the global optimal ant.
In one embodiment of the invention, said step 3.2 comprises:
3.21, obtaining the initial position information of the ith ant according to the initial deflection angles of N unmanned aerial vehicles in the initial ant colony, wherein i is less than or equal to L;
3.22, repeating the step of 3.21 to obtain the initial position information of the L ants;
3.23, obtaining a corresponding initial fitness value according to the possible position information of the ith ant;
and 3.24, repeating the step of 3.23 to obtain the initial fitness value corresponding to the L ants.
In one embodiment of the invention, said step 3.4 comprises:
3.41, updating an initial deflection angle according to the initial deflection angle, the initial position information and the initial fitness value to obtain a new deflection angle, and bringing the new deflection angle into 3.2 to obtain new position information and a new fitness value;
3.42, judging whether the new position information of the unmanned aerial vehicle in the ith ant is the same, and updating the initial fitness value according to the fitness of the ith ant when the new position information of a plurality of unmanned aerial vehicles in the ith ant is different;
and 3.43, calculating the initial pheromone according to the updated initial fitness value.
The invention has the beneficial effects that:
the method takes the deflection angle of the flying position of the unmanned aerial vehicle group as an independent variable, takes the scouting accumulated coverage area of the unmanned aerial vehicle group at a specified moment as an algorithm fitness function, avoids obstacles through hyperopia, and adds a straight-going allowable error to keep straight-going as much as possible so as to save fuel. By organically combining the flight path planning problem with the ant colony algorithm, a brand new flight path planning problem different from the traditional area coverage optimizing flight path planning situation can be solved, namely the flight path planning problem that the starting point and the end point of a flight path are not specified, and the maximum continuous monitoring coverage range of a specified area is realized when the unmanned aerial vehicle group flies along the flight path is required.
Drawings
Fig. 1 is a block flow diagram of a flight path planning method based on an ant colony algorithm according to an embodiment of the present invention;
fig. 2 is a block flow diagram of another ant colony algorithm-based flight path planning method according to an embodiment of the present invention;
fig. 3 is a schematic diagram of an unmanned aerial vehicle capable of reaching a position after a fixed time interval according to an ant colony algorithm-based flight path planning method provided by an embodiment of the present invention;
fig. 4 is a schematic diagram of node partitioning to be searched for in a flight path planning method based on an ant colony algorithm according to an embodiment of the present invention;
fig. 5 is a diagram of 4 positions of the unmanned aerial vehicle at the initial time of the route planning method based on the ant colony algorithm provided by the embodiment of the present invention;
fig. 6 is a diagram of a flight path planning result of a flight path planning method based on an ant colony algorithm according to an embodiment of the present invention;
fig. 7 is a graph illustrating percentage changes in the cumulative coverage area of the drone swarm in a flight path planning method based on the ant colony algorithm according to an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to specific examples, but the embodiments of the present invention are not limited thereto.
Referring to fig. 1, fig. 1 is a flow chart of a flight path planning method based on an ant colony algorithm according to an embodiment of the present invention, including:
step 3, obtaining global optimal ants and pheromones thereof according to the predicted target nodes and the initial deflection angle;
step 4, respectively carrying out execution allowable error judgment and obstacle avoidance judgment on the N unmanned aerial vehicles in the global optimal ants according to the global optimal ants and the pheromones thereof to obtain judgment results, and updating the optimal position deflection angles corresponding to the unmanned aerial vehicles in the global optimal ants according to the judgment results;
step 5, obtaining track position change angles of the N unmanned aerial vehicles in the global optimal ants at the next moment according to optimal position deflection angles corresponding to the N unmanned aerial vehicles in the global optimal ants;
step 6, judging whether N unmanned aerial vehicles in the global optimal ants complete the voyage or not, and ending when the voyage is completed; and when the voyage is not finished, jumping to the step 3.
The method takes the deflection angle of the flying position of the unmanned aerial vehicle group as an independent variable, takes the scouting accumulated coverage area of the unmanned aerial vehicle group at a specified moment as an algorithm fitness function, avoids obstacles through hyperopia, and adds a straight-going allowable error to keep straight-going as much as possible so as to save fuel. By organically combining the flight path planning problem with the ant colony algorithm, a brand new flight path planning problem different from the traditional area coverage optimizing flight path planning situation can be solved, namely the flight path planning problem that the starting point and the end point of a flight path are not specified, and the maximum continuous monitoring coverage range of a specified area is realized when the unmanned aerial vehicle group flies along the flight path is required.
In one embodiment of the present invention, the step 1 comprises:
1.1, setting a flyable area A, and designating a task monitoring area S in the flyable area A;
further, when the unmanned aerial vehicle executes a flight mission, a safe area allowing the unmanned aerial vehicle to fly is a flyable area of the unmanned aerial vehicle, the flyable area of the unmanned aerial vehicle is set as A, and if the unmanned aerial vehicle flies away from the flyable area A of the unmanned aerial vehicle, the unmanned aerial vehicle is probably hit by threats of hostile force such as air gun fire prevention, ground-to-air missile potential force and directional radiation devices, so that the flight mission fails.
A designated task monitoring area in an unmanned aerial vehicle flyable area A is set as S, and the flight task of the flight path planning requires the maximum accumulated monitoring coverage and obstacle avoidance of the designated task monitoring area S, so that the radar can continuously acquire a ground potential threat target of the designated task monitoring area S.
1.2, determining the time interval of single step planning;
further, the motion parameter of the unmanned aerial vehicle is a state parameter representing the unmanned aerial vehicle moving on the ground or flying in the air, and the motion of the unmanned aerial vehicle is determined by the state parameter, wherein the motion parameters related to the flight path planning problem are as follows: setting a yaw angle of an unmanned aerial vehicle
The device is used for representing an included angle between the flight speed direction of the unmanned aerial vehicle and the positive direction of the x axis of the horizontal coordinate system; setting a roll angle gamma of the unmanned aerial vehicle, wherein the roll angle gamma is used for representing an included angle between a symmetrical plane of the unmanned aerial vehicle and a vertical plane containing an x axis of a horizontal coordinate system; setting a turning angle theta of the unmanned aerial vehicle and a turning radius R of the unmanned aerial vehicle; and an airborne radar is arranged on the unmanned aerial vehicle, and the airborne radar is a transmitter and a receiverA machine is provided. In the embodiment of the invention, the roll angle gamma of the unmanned aerial vehicle is 30 degrees.
And analyzing the stress condition of the airplane when the airplane turns in the air. When the airplane turns, the airplane body needs to incline, then a centripetal component is generated by utilizing the difference of the lift forces of the left main wing and the right main wing to turn, and if the airplane turns at a constant speed at a certain height, the stress equation vertical to the axial plane of the airplane at the moment is as follows:
wherein L is lift, gamma is roll angle, i.e. inclination angle of fuselage, m is deadweight of the fuselage of the aircraft, R is turning radius, VpIs the aircraft flying speed. Then, from the above equation:
R＝Vp 2/(g·tanγ)，
tan γ is known in some literature as overload. From the above equation, the turning radius R decreases as the roll angle γ increases. The aircraft has a maximum overload limit when the overload reaches a maximum (maximum roll angle) at which the turning radius of the aircraft is at a minimum turning radius Rmin. Thus, the aircraft can only turn at greater than or equal to RminThe turning radius of (2) makes a turn.
According to the minimum turning radius RminThe time required for the carrier to make one turn at the minimum turning radius can be calculated as:
1.3, according to the geometric relation between the current position of the unmanned aerial vehicle and the maximum turning angle, calculating a predicted target node reached by the unmanned aerial vehicle after a certain time interval.
Further, dividing target search nodes of the unmanned aerial vehicle at the next moment: based on the current position and speed direction of the unmanned aerial vehicle, the positions which can be reached by the unmanned aerial vehicle after a certain time interval are calculated, and then points are uniformly sampled in the positions. As shown in figure 1, a current position of the unmanned aerial vehicle is arrangedAt points E, v1Is the velocity vector of the drone. Since the aircraft generally has only two flight modes during flight in the air, namely straight flight and turning (assuming that the drone always flies at the same altitude), the position that the drone can reach after a fixed time interval is determined by two parameters, namely the flight speed and the minimum turning radius of the drone. The minimum turning radius of the unmanned aerial vehicle is RminThe fixed time interval is t. If the unmanned plane keeps straight-line flight all the time, tminThe position where the unmanned aerial vehicle arrives after the time is point F; if the unmanned aerial vehicle turns left in the minimum turning semi-radial direction, the position where the carrier arrives after t time is a point G; if the unmanned aerial vehicle turns to the right at the minimum turning radius, the position where the aircraft arrives after t time is a point H; if the unmanned aerial vehicle turns left or right with a larger turning radius, the position reached by the unmanned aerial vehicle after the time t is determined to be on the arc between the point G and the point H. Here, in order to simplify the model, it is assumed that EG and EF are EH, that is, the euclidean distance from the point E after the time t when the drone turns the corner is approximately equal, and therefore all positions that can be reached after the time t when the drone flies are located on the circular arc GH.
Referring to fig. 3, fig. 3 is a schematic diagram of a position that an unmanned aerial vehicle can reach after a fixed time interval according to a flight path planning method based on an ant colony algorithm provided in an embodiment of the present invention, where if the unmanned aerial vehicle turns left in a minimum turning semi-radial direction, an aircraft arrives at a point G after Δ t, and at this time, a speed of the unmanned aerial vehicle changes to v2The angle at which the speed and direction of the drone change compared to point E is
Alpha is the deflection angle of the position of the aircraft flying from the point E to the point G, theta is the angle rotated by the aircraft in the turning with the minimum turning radius, and according to the geometrical relationship of similar triangles, the following can be proved:
although theta, alpha,
Are parameters in the case of a drone turning left at the minimum turning radius, but this is merely to illustrate the relationship between them, theta, alpha,
still satisfies the relationship given by the above formula.
Next, uniform point collection is performed on the circular arc GH, please refer to fig. 4, where fig. 4 is a schematic diagram of dividing nodes to be searched by the route planning method based on the ant colony algorithm provided in the embodiment of the present invention, and the circular arc GH is averagely divided into M segments, so as to obtain M +1 nodes to be searched. Since the case of turning left is completely symmetrical to that of turning right, M must be an even number. As can be seen in fig. 4:
αmdividing the M +1 equally-divided nodes to be searched into the following nodes for the position deflection angle of the target node relative to the front starting point E of the aircraft:
the absolute value is bilaterally symmetrical about 0, where αmWhen the value is 0, the line is straight, wherein
Setting the average flying speed of the unmanned aerial vehicle as vpFor representing the average value of the flight speed of the unmanned aerial vehicle within the single-step flight path planning time interval t; assuming the average value v of the flying speed of the unmanned aerial vehicle in the single-step flight path planning time interval t during the flight processpAlways kept unchanged.
In one embodiment of the present invention, the step 2 comprises:
2.1, setting the initial positions and the initial deflection angles of the N unmanned aerial vehicles, and calculating the coverage rate at the initial moment according to the initial positions and the initial deflection angles.
Further, setting a flight path planning problemInitial conditions of (1): respectively setting the initial time deflection angles of N unmanned aerial vehicles and the position coordinate matrix of the N unmanned aerial vehicles in the flyable area A at the initial time, namely respectively using vectors
Representing course vectors of N unmanned aerial vehicles at zero time by using matrix P0The position coordinate matrix of N unmanned aerial vehicles in the flyable area A at zero time is represented by the following expressions:
wherein the content of the first and second substances,shows the included angle between the speed direction of the ith unmanned aerial vehicle at zero time and the positive direction of an x axis,
Pi 0represents the flight path position of the ith unmanned plane in the flyable area A at the zero moment,
the x-axis coordinate of the track position of the ith unmanned plane in the flyable area A at the zero moment,
and the y-axis coordinate of the track position of the ith unmanned aerial vehicle in the flyable area A at zero time is represented, and the superscript T represents the transposition operation. The initial coverage area ratio percent is obtained by a statistical methodarea。
2.2, setting a single-step track planning termination criterion of the ant colony algorithm, and calculating the coverage rate of each unmanned aerial vehicle in the N unmanned aerial vehicles at the initial moment by combining the termination criterion, the initial position and the initial deflection angle.
Further, a fitness function termination criterion of the single-step flight path planning algorithm is set as follows: the flight mission of the flight path planning requires N unmanned aerial vehicles to realize continuous search in the maximum range in the designated mission monitoring area S, so the sum of the reconnaissance accumulated coverage areas of the N unmanned aerial vehicles is selected as a fitness function of a flight path planning algorithm, and t is a one-step flight path planning time interval. The fitness function termination criteria of the single-step flight path planning algorithm are as follows: and setting the maximum iteration algebra G of the ant colony algorithm, and terminating the flight path planning task when the iteration of the ant colony algorithm is performed for G times.
In one embodiment of the present invention, the step 3 comprises:
3.1, setting the ant number of the ant colony algorithm as L and the dimension of each ant as N to obtain an LxN dimensional initial ant colony.
Further, following the basic ant colony algorithm, the algorithm is initialized first, and the initialization process is equivalent to randomly determining a feasible flight plan, which is not necessarily optimal.
The position deflection angle of the ith unmanned aerial vehicle in the single-step flight path planning time interval t is as follows:
3.2, calculating initial position information and an initial fitness value of the LxN dimensional initial ant colony according to the initial deflection angle.
3.3, storing the initial position information and the initial fitness value.
Further, two important data structures AntSawrm and OptSawrm are introduced, wherein the AntSawrm is used for storing information of ants, the OptSawrm is used for storing ith ant history optimal information and global optimal ant information, and the AntSawrm and the OptSawrm are as follows:
AntSawrm and OptSawrm are three-dimensional data structures, in the above formula
Represents the position deflection angle p of the ith ant when the ith ant walks to the kth search stepikRepresents the position coordinate of the ith ant during the k-th search, fi_optRepresents the historical optimal fitness function value corresponding to the ith ant, foptIs the fitness value of the global optimal ant. In OptSawrm, when i is L +1, the information stored in OptSawrm is the information of the global history optimal ant position deflection angle, the corresponding position coordinate and the fitness function value. Note that the information of the L +1 st ant is not, but the information of the selected global history optimal ant. Thereby completing initialization of AntSawrm and OptSawrm.
The data structure Info is introduced and the initialization is completed. The Info is used for storing pheromones in the iterative process, the pheromones are used as important guiding information for ant colony algorithm optimization, real-time updating is needed in the algorithm implementation process, and the initial value of the pheromones is an initialized fitness function value, namely: info ═ f (f)1,...,fi,...,fL),i＝1,2,...,L。
And 3.4, iterating the ant colony algorithm according to the initial deflection angle, the initial position information and the initial fitness value to obtain initial pheromone.
And 3.5, obtaining the global optimal ants according to the iteration result.
Further, increasing the value of i from 1 to L and repeating step 3.4 to obtain the g1Optimal ant fitness value f corresponding to L ants in sub-iterationoptAnd corresponding position deflection angle and position coordinates.
And 3.6, obtaining pheromone of the global optimal ant according to the global optimal ant.
Further, g is1Increasing the value from 1 to G, repeating the steps 3.4 and 3.5, and finishing iteration to select the globally optimal ant information with the optimal fitness value foptCorresponding to a position deflection angle of
In one embodiment of the invention, said step 3.2 comprises:
3.21, obtaining the initial position information of the ith ant according to the initial deflection angles of N unmanned aerial vehicles in the initial ant colony, wherein i is less than or equal to L.
Further, the ant group Z with the ant number of L and the dimension of NgAs the predicted position deflection angle of the single-step flight path planning, the single step refers to the k-th flight path planning to the k + 1-th flight path planning, and the feasible positions of the N unmanned aerial vehicles of the ith ant at the (k +1) th time are calculated according to the following relational expression
wherein the content of the first and second substances,
x-axis coordinates representing feasible positions of the jth unmanned aerial vehicle at the (k +1) th time point when the ith ant is taken,
a y-axis coordinate representing a feasible position of the jth unmanned aerial vehicle at the (k +1) th t when the ith ant is taken,
the x-axis coordinate of the locus of the jth unmanned aerial vehicle in the flyable area A at the moment of kt,
y-axis coordinate, v, representing track position of jth man-machine in flyable area A at kt momentpThe average flight speed value of the unmanned aerial vehicle is shown,
the jth unmanned aerial vehicle speed deflection angle at the kt moment is shown,
the position deflection angles of the jth unmanned aerial vehicle and the ith ant subjected to linear coding in the single-step track planning time interval T are shown, cos represents a cosine operation, sin represents a sine operation, and superscript T represents a transposition operation.
3.22, repeating the step of 3.21 to obtain the initial position information of the L ants.
Further, let i take 1 to L respectively, repeat substep 3.21, and then obtain feasible positions of the N unmanned aerial vehicles at the (k +1) th time point when the 1 st ant is present respectivelyDevice for placing
The feasible positions of the N unmanned aerial vehicles at the (k +1) th time t by the Lth ant
Recording feasible positions of N unmanned aerial vehicles corresponding to L ants at the (k +1) th time t
The expression is as follows:
and 3.23, obtaining a corresponding initial fitness value according to the possible position information of the ith ant.
Further, an ant colony algorithm fitness function of feasible positions of the N unmanned aerial vehicles at the (k +1) th time t when the ith ant is taken as Y is specifically expressed as follows:
function (-) denotes solving the area coverage function, the resulting area coverage function value Y1And the historical coverage area value Y of the task area2The sum of (a) and (b) is a fitness value; recording the total area monitored by the N unmanned aerial vehicles of the ith ant as Si，Si＝Si1∪…∪Sij∪…∪SiNI ═ 1, 2., L @, u, denotes a union operation, SijThe area of the area monitored by the ith ant and the jth unmanned aerial vehicle is represented, and the following conditions are met:
wherein the content of the first and second substances,
the coordinates of the ith ant and the jth unmanned aerial vehicle on the x axis in the (k +1) th step,
the coordinates of the ith ant and the jth unmanned aerial vehicle on the y axis of the (k +1) th step are shown, x 'represents the independent variable of the x axis of the task area, y' represents the independent variable of the y axis of the task area, and RsThe maximum action distance of the airborne radar.
The feasible positions of N unmanned aerial vehicles at the (k +1) th time point when the ith ant is taken
Substituting the calculated fitness value f into an ant colony algorithm fitness function of feasible positions of the N unmanned aerial vehicles at the (k +1) th time t when the ith ant is used, and calculating the fitness value f of the ith anti。
And 3.24, repeating the step of 3.23 to obtain the initial fitness value corresponding to the L ants.
Further, let i take 1 to L respectively, repeat substep 3.4, and then obtain the fitness value of the 1 st ant to the fitness value of the L th ant respectively, and record them as the fitness values corresponding to the L ants in the initial population Zg:
f＝(f1,...,fi,...,fL),i＝1,2,...,L，
and selecting the ants corresponding to the maximum value from the adaptability values as initial historical optimal ants and initial global optimal ants.
In one embodiment of the invention, said step 3.4 comprises:
and 3.41, updating the initial deflection angle according to the initial deflection angle, the initial position information and the initial fitness value to obtain a new deflection angle, and bringing the new deflection angle into 3.2 to obtain new position information and a new fitness value.
Further, the state transition probability Pro of the ith ant is calculated according to the pheromone Info in the following way:
wherein Pro isiRepresenting the state transition probability, Info, of the ith anti＝fiIs the pheromone carried by the ith ant, and Info _ best is the maximum value of the pheromones carried by all ants. The state transition probability measures the difference between the ant i and the current optimal ant, so that the updating mode of the position deflection angle of the ant i can be determined according to the state transition probability. I.e. state transition probability Pro when ant iiLess than a set value Pro0When the ant i is far away from the current optimal ant, the position deflection angle is enabled to be within the maximum deflection angle [ -M/2, M/2]X Δ α search over the global range. G th1Second iteration ant i ant position deflection angle
The specific calculation method is as follows:
wherein the content of the first and second substances,
and searching the position deflection angles of the N unmanned aerial vehicles corresponding to the ith ant for the kth step, wherein delta alpha is the division node angle interval.
From the g th1Second iteration ant i ant position deflection angle
Position coordinates corresponding to N unmanned aerial vehicles are obtained
And fitness function value
The equations synchronize steps 3.21, 3.22, and 3.23.
3.42, judging whether the new position information of the unmanned aerial vehicle in the ith ant is the same, and updating the initial fitness value according to the fitness of the ith ant when the new position information of a plurality of unmanned aerial vehicles in the ith ant is different;
further, AntSawrm and OptSawrm are updated. Stored in AntSawrm is the g-th1The basic information of L ants in the next iteration, OptSawrm stores the g < th > information1And iterating the basic information of the ant with the optimal history every i and the basic information of the ant with the optimal global history. And updating AntSawrm by using the information obtained by calculation in 3.41, wherein the AntSawrm comprises a position deflection angle, an ant position and a fitness function value. In particular: unmanned aerial vehicle avoids the collision, when there are two unmanned aerial vehicles the abscissa is unanimous and the ordinate is unanimous, directly transfers to step 3.5, otherwise carries out two steps and compares: comparing the g of the ith ant1Fitness function value of sub-iteration
And the historically optimal fitness function value f of the ith ant stored in OptSawrmi_optIf, if
And for f in OptSawrmi_optThe information of the corresponding ith ant is updated, otherwise, the information is not updated; then comparing the historical optimal fitness function value f of the ith ant stored in OptSawrmi_optAnd a global optimum value foptIf f isi_opt＞fopt，fopt＝fi_optAnd for f in OptSawrmoptThe information of the corresponding ith ant is updated, otherwise, the information is not updated。
And 3.43, calculating the initial pheromone according to the updated initial fitness value.
Further, the pheromone Info is updated. The pheromone is composed of two parts, one part is the volatile residue of the original pheromone, and the other part is the addition of new pheromone. Defining pheromone evaporation coefficient rho to act on the current pheromone, then (1-rho) represents the residue of the pheromone, and the new pheromone is the current global history optimal fitness function, therefore, the pheromone updating mode is as follows:
Info＝(1-ρ)Infoi+fopt，
wherein the InfoiRepresents the g of the ith ant1Pheromones of the second iteration.
Furthermore, in step four, straight-ahead driving is encouraged, the target node position deflection angle in the straight-ahead driving is line ═ 0lineIf fopt-flineD, updating the optimal fitness value fopt＝flineAnd corresponding position deflection angle
Where δ is the allowable cost error set to encourage coverage reduction for straight line flight; otherwise, no update is performed.
Avoiding obstacles, and the horizontal and vertical coordinates of the far vision position are as follows:
wherein the content of the first and second substances,
representing x-axis coordinate, v, of track position of jth unmanned aerial vehicle in flyable area A at kt momentpThe average flight speed value of the unmanned aerial vehicle is shown,
shows the speed direction and the positive direction of the x axis of the jth unmanned aerial vehicle at the kth momentThe included angle is formed by the angle of inclination,
i.e. vpThe included angle between the X-axis direction and the horizontal X-axis direction,
represents a global optimal ant fitness value foptCorresponding angle of position deflection
Position deflection angle of jth unmanned aerial vehicle
And a y-axis coordinate representing the track position of the jth unmanned aerial vehicle in the flyable region A at the kt moment, wherein mu is a far vision coefficient, j is {1,2,. N }, and far vision horizontal and vertical coordinates of the N unmanned aerial vehicles are obtained: x ═ x1,...,xN},y＝{y1,...,yN}。
Judging whether the horizontal and vertical coordinates of the far-vision position are equal in the task area or in the N unmanned aerial vehicles, and forcibly turning to update the optimal position when the horizontal and vertical coordinates of the far-vision position are equal in the task area or in the N unmanned aerial vehicles, namely
Wherein epsilonjJ ═ 1,. ·, N, which represents the angle of the forced turn; otherwise, no update is performed. And finally, updating the position deflection angle of the N unmanned aerial vehicles from kt to (k +1) t.
For step 5, the optimal fitness value fo is calculatedptCorresponding angle of position deflection
Obtaining a position coordinate matrix P of N unmanned aerial vehicles in the flyable area A at the (k +1) th timek+1And a velocity direction vk+1The expression of (a) is:
wherein the content of the first and second substances,
represents the track position of the jth unmanned aerial vehicle in the flyable area A at the (k +1) th time point t,
an x-axis coordinate representing a track position of the jth drone within the flyable area a at time (k +1) t,
a y-axis coordinate representing a track position of the jth drone at the (k +1) th time point within the flyable area a,
an x-axis coordinate representing a track position of the jth drone within the flyable area a at a kt time,
y-axis coordinate, v, representing track position of jth unmanned aerial vehicle in flyable area A at kt momentpThe average flight speed value of the unmanned aerial vehicle is shown,
shows the included angle between the speed direction of the jth unmanned aerial vehicle and the positive direction of the x axis at the kt moment,
at this time, the route planning at the (k +1) th time is realized, and the percentage p of the cumulative coverage area of all the historical tracks in the total area S in the mission monitoring area S is obtained by a statistical methodarea2Let k be k + 1.
For step 6, if K is K or percent is 1, ending the iteration, otherwise repeating step 3, step 4 and step 5 in sequence. And finally, planning the accumulated maximum monitoring coverage and obstacle avoidance tracks of the K-step N unmanned aerial vehicles in the designated task monitoring area S.
Specifically, the optimal coordinate position P of N unmanned aerial vehicles at kt time is usedkDirection of velocity vkAnd (3) as an initial condition of the next step of single-step optimization flight path planning based on the genetic algorithm, using time serial processing, planning the flight path position of the next step by using the methods of the steps 3, 4 and 5, and continuously obtaining a plurality of optimal flight path positions after single-step planning, thereby realizing the maximum coverage and obstacle avoidance of the designated task monitoring area S by the N unmanned aerial vehicles.
The effect of the present invention is further verified and explained by the following simulation experiment.
Simulation conditions:
the simulation assumes that 4 unmanned aerial vehicles with a reconnaissance radius of 30km are used for monitoring a designated task monitoring area S of 200km multiplied by 200km, a flyable area A where the unmanned aerial vehicle cluster is located is a rectangular area of 220km multiplied by 220km, the designated task monitoring area is located at the center of the flyable area, and the speed direction and the x axis of the 4 unmanned aerial vehicles at zero timeAngle v in positive direction0And a position coordinate matrix P of the unmanned aerial vehicle in the flyable area A at the zero moment0Respectively as follows:
v0＝(00 90° -45° -90°)T
the flight path of each step of the unmanned aerial vehicle group is the flight path planning method based on the ant colony algorithm, the flight path obtained by the experiment is the result of 200-step single-step planning, and the detailed parameters refer to a parameter table:
parameter table
| Flyable area | 220km×220km |
| Area to be monitored | 200km×200km |
| Unmanned aerial vehicle detection radius | 30km |
| Average speed of flight of unmanned aerial vehicle | 150m/s |
| Single step scheduling interval | 20s |
| Absolute value of |
30° |
| Number of iterative ants | 3 |
| Single step planning maximum number of iterations | 8 |
| Far vision coefficient | 3 |
| Dividing the number of search nodes | 7 |
| Transition probability set value | 0.01 |
| Evaporation coefficient of pheromone | 0.9 |
| Straight line allowable error | 0.002 |
(II) simulation content and result analysis
The result of planning the flight path with the total steps of 200 steps by using the flight path planning method based on the ant colony algorithm provided by the invention is obtained. When the coverage rate reaches 100%, the search step number is 165, and the operation time is 6.718364 seconds.
Referring to fig. 5, fig. 5 is a diagram of positions of 4 unmanned aerial vehicles at an initial time according to a flight path planning method based on an ant colony algorithm provided by an embodiment of the present invention, and fig. 2 is a diagram of positions of 4 unmanned aerial vehicles at an initial time, where an asterisk indicates a position of an unmanned aerial vehicle.
Referring to fig. 6, fig. 6 is a diagram of a flight path planning result of a flight path planning method based on an ant colony algorithm according to an embodiment of the present invention, where 3 rectangular blocks are added to simulate an obstacle area, a solid line enclosed area is a flyable area a of 4 unmanned aerial vehicles, a dotted line enclosed area is a designated area S to be monitored, and a star line, a triangle line, a circle line, and a plus line are flight paths of the 4 unmanned aerial vehicles, respectively; as can be seen from fig. 3, the planned track points are all distributed in the flyable area a and can avoid the obstacle area, which indicates that the track points obtained by the method are effective and feasible.
Referring to fig. 7, fig. 7 is a graph illustrating a percentage change of an accumulated coverage area of an unmanned aerial vehicle fleet according to a flight path planning method based on an ant colony algorithm, where a vertical coordinate is an accumulated coverage area percentage of the unmanned aerial vehicle fleet for a designated area S to be monitored, and a horizontal coordinate is a number of steps for planning a flight path by using the method, and a unit is a step. As can be seen from the coverage area curve monitored by the unmanned aerial vehicle fleet in fig. 7, the coverage area percentage of the unmanned aerial vehicle fleet can reach 100% when the number of search steps reaches 165 based on the flight path obtained by the method of the present invention, which proves that the flight path planning method based on the ant colony algorithm provided by the present invention can realize the maximum coverage and obstacle avoidance of the unmanned aerial vehicle fleet on the designated area.
In conclusion, the simulation experiment verifies the correctness, the effectiveness and the reliability of the method.
The foregoing is a more detailed description of the invention in connection with specific preferred embodiments and it is not intended that the invention be limited to these specific details. For those skilled in the art to which the invention pertains, several simple deductions or substitutions can be made without departing from the spirit of the invention, and all shall be considered as belonging to the protection scope of the invention.
Claims (5)
1. An ant colony algorithm-based flight path planning method is characterized by comprising the following steps:
step 1, setting a flyable area A, designating a designated task monitoring area in the flyable area A as S, and dividing LxN unmanned aerial vehicles in L ants to predict target nodes at the next moment within a maximum turning angle constraint range, wherein L is greater than 0, and N is greater than 0;
step 2, initializing an initial position and an initial deflection angle of the LxN unmanned aerial vehicle, and calculating an initial moment coverage rate according to the initial position and the initial deflection angle;
step 3, obtaining a global optimal ant and an pheromone of the global optimal ant according to the predicted target node and the initial deflection angle;
step 4, respectively performing allowable error judgment and obstacle avoidance judgment on the N unmanned aerial vehicles in the global optimal ants according to the global optimal ants and the pheromones of the global optimal ants to obtain judgment results, and updating the optimal position deflection angles corresponding to the unmanned aerial vehicles in the global optimal ants according to the judgment results;
step 5, obtaining track position change angles of the N unmanned aerial vehicles in the global optimal ants at the next moment according to optimal position deflection angles corresponding to the N unmanned aerial vehicles in the global optimal ants;
step 6, judging whether N unmanned aerial vehicles in the global optimal ants complete the voyage or not, and ending when the voyage is completed; and when the voyage is not finished, jumping to the step 3.
2. The ant colony algorithm-based flight path planning method according to claim 1, wherein the step 1 comprises:
1.1, setting a flyable area A, and designating a task monitoring area S in the flyable area A;
1.2, determining the time interval of single step planning;
1.3, according to the geometric relation between the current position of the unmanned aerial vehicle and the maximum turning angle, calculating a predicted target node reached by the unmanned aerial vehicle after a certain time interval.
3. The ant colony algorithm-based flight path planning method according to claim 1, wherein the step 3 comprises:
3.1, setting the number of ants in the ant colony algorithm as L and the dimension of each ant as N to obtain an LxN dimensional initial ant colony;
3.2, calculating initial position information and an initial fitness value of the LxN dimensional initial ant colony according to the initial deflection angle;
3.3, storing the initial position information and the initial fitness value;
3.4, iterating the ant colony algorithm according to the initial deflection angle, the initial position information and the initial fitness value to obtain an initial pheromone;
3.5, obtaining a global optimal ant according to the initial pheromone;
and 3.6, obtaining pheromone of the global optimal ant according to the global optimal ant.
4. The ant colony algorithm-based flight path planning method according to claim 3, wherein the step 3.2 comprises:
3.21, obtaining the initial position information of the ith ant according to the initial deflection angles of N unmanned aerial vehicles in the initial ant colony, wherein i is less than or equal to L;
3.22, repeating the step of 3.21 to obtain the initial position information of the L ants;
3.23, obtaining a corresponding initial fitness value according to the possible position information of the ith ant;
and 3.24, repeating the step of 3.23 to obtain the initial fitness value corresponding to the L ants.
5. The ant colony algorithm-based flight path planning method according to claim 3, wherein the step 3.4 comprises:
3.41, updating an initial deflection angle according to the initial deflection angle, the initial position information and the initial fitness value to obtain a new deflection angle, and bringing the new deflection angle into 3.2 to obtain new position information and a new fitness value;
3.42, judging whether the new position information of the unmanned aerial vehicle in the ith ant is the same, and updating the initial fitness value according to the fitness of the ith ant when the new position information of a plurality of unmanned aerial vehicles in the ith ant is different;
and 3.43, calculating the initial pheromone according to the updated initial fitness value.
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