CN113759958A - Unmanned aerial vehicle formation flight path planning method based on positioning precision - Google Patents
Unmanned aerial vehicle formation flight path planning method based on positioning precision Download PDFInfo
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Abstract
The invention provides an unmanned aerial vehicle formation track planning method based on positioning accuracy, which comprises preliminary planning and accurate planning, wherein the preliminary planning is to set a proper step length to carry out track planning on a host by taking safety and unmanned aerial vehicle kinematic limitation in the flight process of the unmanned aerial vehicle into consideration through a traditional heuristic algorithm; generating a track of the host by preliminary planning, and recording the position of the host at the end of each step length as a track point needing to be calculated during accurate planning; and (3) calculating the optimal track of the slave in which the safety, the positioning precision and the path length are comprehensive costs and the maximum communication distance and the minimum anti-collision distance of the unmanned aerial vehicle are limited by the precise planning through a multi-target particle swarm optimization algorithm aiming at the primary planned host track point. The method has the advantages of high algorithm positioning precision and short running time.
Description
Technical Field
The invention relates to an unmanned aerial vehicle formation flight path planning method based on positioning accuracy, and belongs to the field of passive positioning.
Background
At present, the unmanned aerial vehicle technology is mature day by day, and the unmanned aerial vehicle has the characteristics of low manufacturing cost, simplicity in operation and the like, and a large amount of applications of the unmanned aerial vehicle can replace pilots to complete tasks in dangerous environments, so that the task cost is greatly reduced. Therefore, the unmanned aerial vehicle technology has become the research focus of all the scientific and technical countries at present. Because single unmanned aerial vehicle self flight load is limited, can't carry on a large amount of sensors to single unmanned aerial vehicle receives the unable normal completion task of striking very easily under the battlefield environment. Based on this, the mode of accomplishing the task through many unmanned aerial vehicle formation cooperatees is adopted more in the actual detection task. Through the collaborative working mode of the formation of the multiple unmanned aerial vehicles, the working efficiency can be well improved, meanwhile, the risk of task failure caused by single individual faults is avoided to a certain extent, and more comprehensive information can be obtained.
At present, the actual battlefield mission is finished according to unmanned aerial vehicle formation, which becomes the mainstream research direction, and the used planning scheme is mostly the shortest path principle on the premise of ensuring formation coordination. However, unmanned aerial vehicle cooperative work strategies for positioning purposes have also begun to be mentioned in recent years. When the mode that many unmanned aerial vehicle formation adopted the time difference location fixes a position, because the space of each unmanned aerial vehicle is arranged and is had decisive influence to positioning accuracy, so need optimize the flight track of unmanned aerial vehicle formation and improve the location effect. In the research on unmanned aerial vehicle fleet route planning based on improved MOGOA (Tianjin university institute of academic Press, volume 53, 9 th 2020 and 9 th months), an locust algorithm is used for carrying out unmanned aerial vehicle optimal formation route planning, and positioning accuracy is taken into consideration as a judgment index of route planning, but the route planning carried out by the method needs a large amount of time consumption, and the positioning effect is not good enough.
Therefore, on the premise of considering the positioning accuracy, a rapid and accurate unmanned aerial vehicle formation flight path planning scheme is provided.
Disclosure of Invention
The invention provides a solution for solving the problems that the positioning effect is not accurate enough and a great amount of time is needed for flight path planning in the current multi-unmanned aerial vehicle formation flight path planning based on positioning.
The purpose of the invention is realized as follows: the method comprises the steps of preliminary planning and accurate planning, wherein the preliminary planning is to set a proper step length to plan the flight path of a host by taking safety and unmanned aerial vehicle kinematic limit in the flight process of the unmanned aerial vehicle into consideration through a traditional heuristic algorithm; generating a track of the host by preliminary planning, and recording the position of the host at the end of each step length as a track point needing to be calculated during accurate planning; the method comprises the following steps of accurately planning, calculating an optimal track with the safety, the positioning precision and the path length of a slave machine as comprehensive costs and meeting the limitation of the maximum communication distance and the minimum anti-collision distance of the unmanned aerial vehicle aiming at a primary planned host track point by a multi-target particle swarm optimization algorithm, and specifically comprises the following steps:
step 1: modeling the flight path planning problem of the formation of the unmanned aerial vehicles into the flight path planning of the formation of the unmanned aerial vehicles by comprehensively considering the maximum communication distance, the minimum anti-collision distance, the path length, the safety, the positioning precision and the kinematic limit of the unmanned aerial vehicles as the cost, and acquiring the information of obstacles and threat areas in the environment;
step 2: performing primary track planning on the unmanned aerial vehicle host through a heuristic algorithm, and determining a plurality of positioning track points from a track starting point to a track end point;
and step 3: searching the optimal spatial position of the slave machine at the plurality of positioning track points determined in the step 2 by a particle swarm optimization algorithm;
and 4, step 4: forming the optimal positions of the master unmanned aerial vehicle and the slave unmanned aerial vehicle determined in the steps 2 and 3 at each track point into an unmanned aerial vehicle track;
and 5: and acquiring the position and the measurement data of each unmanned aerial vehicle at the positioning track point, resolving to obtain target position information, and finishing planning.
The invention also includes such structural features:
1. in the step 1, modeling the maximum communication distance, the minimum anti-collision distance, the path length, the safety, the positioning precision and the kinematics limit of the unmanned aerial vehicle as the following models:
(1) maximum communication distance:
wherein: (x)t yt zt) Is the host position of the unmanned aerial vehicle, (x)t,i yt,i zt,i) For the ith slave position, dmaxThe maximum communication distance of the unmanned aerial vehicle is set;
(2) minimum collision-proof distance:
wherein:
position of any two unmanned aerial vehicles at time t, n1≠n2，dminThe minimum collision-proof distance is set for the unmanned plane;
(3) the length of the path:
wherein: f. ofdisFor a cost of distance, vt,nThe flight speed of the nth unmanned aerial vehicle at the moment t, N is the number of the unmanned aerial vehicles, S is the position of the track end point,
the position of the unmanned aerial vehicle corresponding to the time t;
(4) safety:
fthreat＝fanti+fprohibit
wherein: f. ofthreatFor safety cost, fantiFor the enemy to counter-reconnaissance the threat cost, fprohibitTo disable a regional threat, wherein:
assuming that there are R radar detection areas, the center position coordinates of the R-th radar detection area can be expressed as
The farthest detection radius of the radar detection area is dr max，dr minRepresents the radius of the radar early warning area of the r-th part when drmin＜dt,n,r≤drmaxWhen a molecule is multiplied by drminMainly to raise the value of the threat cost, otherwise drmin＜dt,n,r≤drmaxWithin the range, the threat cost is extremely low and the change is not obvious; pt,n,r(dt,n,r) For the threat cost of the r radar to the nth unmanned aerial vehicle position at the time t, (x)t,n,yt,n,zt,n) The position of the nth unmanned aerial vehicle at the time t is shown;
assuming that W no-fly regions coexist, the center position of the W-th no-fly region is represented as
The minimum radius of the no-fly region is set as dw minWhen the unmanned aerial vehicle enters the area, collision can directly occur; the maximum threat radius of the no-fly zone is dw maxWhen the unmanned plane is at dw minTo dw maxWhen the unmanned aerial vehicle is in the area range, due to the problem of wind direction and shaking of the unmanned aerial vehicle during the flight process, the possibility of collision with the no-fly area exists, and the closer the distance between the unmanned aerial vehicle and the no-fly area is, the greater the threat is; when the distance from the unmanned aerial vehicle to the w-th no-fly area is larger than dw maxIn the meantime, the threat of the no-fly area to the unmanned aerial vehicle can be ignored at the moment, and dt,n,wCenter of w-th no-fly zone
Coordinate position X of unmanned aerial vehicle to nth framet,nThe planar distance of (a); when d iswmin＜dt,n,w≤dwmaxWhen a molecule is multiplied by dwminMainly for improving the numerical value of the threat cost;
(5) positioning accuracy:
fCRLB＝(trace(C))1/2
wherein: f. ofCRLBThe lower boundary of the Cramer-Rao is the positioning precision, and the lower boundary is used as the positioning precision cost;
(6) unmanned aerial vehicle kinematics restriction:
wherein: v. ofmax、
θmaxThe maximum speed, the maximum pitch angle and the maximum azimuth angle of the unmanned aerial vehicle.
2. In the step 2, in the flight path planning of the host machine of the unmanned aerial vehicle, the flight path planning is carried out by considering the kinematics constraint, the route cost and the safety of the unmanned aerial vehicle, in the step, the host machine is used as a main reference for judging the formation kinematics constraint of the unmanned aerial vehicle, and when the host machine does not reach the kinematics limit of the unmanned aerial vehicle and the position of a positioning point is far, the host machine can be used for replacing the formation of the unmanned aerial vehicle to carry out the kinematics constraint judgment approximately; determining the step length of each step of the unmanned aerial vehicle in the flight path planning according to the flight speed of the unmanned aerial vehicle; and planning a plurality of positioning track points according to the set step length to be used as positions for the unmanned aerial vehicles to form a formation to execute positioning operation.
3. In the step 3, in the planning of the positions of the slave machines, the optimization operation is carried out at the positions of the planned main machine flight path positioning points in the step 2 through a particle swarm optimization algorithm; the optimal position of the slave machine is determined by using the minimum anti-collision distance, the path length, the safety and the positioning precision as judgment indexes; because the Chan is adopted for resolving in the time difference positioning process, a positioning blind area and no solution phenomenon exist, and a Y-shaped station distribution mode of the unmanned aerial vehicle is introduced; the specific scheme is that the main machine is used as the center of the Y-shaped cloth station, and a slave machine is positioned on the vertical surface of the connecting line of the main machine and a target positioning pointIn the optimization process of the remaining two slave machines, the range of the horizontal angle theta is limited, so that the projection of the four unmanned aerial vehicles on the XOY plane is similar to Y-shaped station distribution, and the probability of falling into a positioning blind area and a non-solution area during resolving can be obviously reduced; is not limited in this step
The height of the four unmanned aerial vehicles is different, and the projection on the XOY plane is similar to Y-shaped station arrangement.
4. And 4, connecting all track points in the step 4 to obtain a track, namely the optimal formation track obtained by the particle swarm optimization algorithm.
5. In step 5, after the main unmanned aerial vehicle acquires the spatial position and the measurement data of each unmanned aerial vehicle, the Chan algorithm is adopted to perform time difference positioning calculation, and final target positioning data is generated.
Compared with the prior art, the invention has the beneficial effects that: the method has the advantages of high algorithm positioning precision and short running time. As shown in fig. 4, the unmanned aerial vehicle formation resolved by the particle swarm optimization scheme has a more excellent positioning effect compared with the unmanned aerial vehicle formation resolved by the locust algorithm. The unmanned aerial vehicle formation solved by the particle swarm algorithm has a very obvious positioning advantage at the positioning point at the beginning, and when the formation is finished, the unmanned aerial vehicle formation solved by the particle swarm algorithm also keeps a certain advantage in positioning effect compared with the unmanned aerial vehicle formation solved by the locust algorithm. As shown in fig. 5, the particle swarm algorithm solution has a more significant advantage in time than the locust algorithm solution. The operation time required by unmanned aerial vehicle formation flight path planning resolved by the multi-target particle swarm algorithm is 242 seconds, and compared with 4373 seconds of the locust algorithm, the algorithm speed is improved by 18 times.
Drawings
FIG. 1 is a flow chart of a multi-objective particle swarm algorithm;
FIG. 2 is a schematic diagram of spherical coordinates;
FIG. 3 is a planar projection of the Y-lay station XOY;
FIG. 4 is a comparison of algorithm effects;
FIG. 5 is a graph of algorithm operation time comparison;
FIG. 6 is a planar projection of the primary drone flight path XOY;
fig. 7 is a diagram of the primary drone flight path;
FIG. 8 is a plane projection view of a formation flight path XOY of the unmanned aerial vehicle;
fig. 9 is a diagram of unmanned aerial vehicle formation tracks.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and specific embodiments.
With reference to fig. 1 to 9, the invention provides a multi-unmanned aerial vehicle formation flight path planning method based on positioning and adopting multi-target particle swarm optimization. Four unmanned aerial vehicles are considered to be an unmanned aerial vehicle formation, one unmanned aerial vehicle is selected as a host in the formation flying process, formation control is realized by controlling the positions of the other three unmanned aerial vehicles relative to the host, and the other three controlled unmanned aerial vehicles are called slave machines. The flight track of the host is obtained by a heuristic search algorithm, and the track of the slave is obtained by optimizing through a multi-target particle swarm algorithm according to the track of the host. Defining a positioning track point at intervals in the formation flight process, and executing target positioning operation at the positioning track point by adopting a time difference positioning mode; at the moment, each slave machine transmits the self position and the target measurement data to the host machine, and the host machine completes the positioning operation; the optimal comprehensive cost including the positioning precision is realized by planning the formation track of the unmanned aerial vehicle.
The multi-target particle swarm optimization algorithm used in the invention has the algorithm flow as shown in figure 1; firstly, initializing a group of 9-dimensional random particles (random solution) by a multi-target particle swarm algorithm, wherein each dimension corresponds to a value needing optimization operation, and every three values form a group and serve as a three-dimensional coordinate of an unmanned aerial vehicle; in the invention, the flight path of the host is obtained by a heuristic algorithm, and the positions of the slave machines are optimized by adopting a multi-target particle swarm algorithm, so that the 9-dimensional particles correspond to the spatial positions of the three slave machines relative to the host. The optimal solution is then found by iteration, in each iteration the particle updates itself by tracking two "extrema". We call these two extrema individually the optimal extremum (abbreviated as pbest, which represents the historical optimal position of a certain particle in the population in the iterative optimization operation) and the optimal extremum of the population (abbreviated as gbest, which represents the historical optimal position of all particles in the population in the iterative optimization operation); different from a single-target particle swarm optimization algorithm, in the iteration result of each generation of the multi-target particle swarm optimization algorithm, the algorithm selects the non-inferior solution of the iteration, the non-inferior solution set is updated, and the optimal extreme value of the group is randomly selected from the non-inferior solution set. After finding these two optimal values, the particle updates its velocity and position by the following formula.
Wherein: k is the current iteration number, i is the dimension of the particle, viIs the velocity of the particles, c1And c2As a learning factor, pbestkFor the historical best position experienced by the individual particle, gbestkThe best position experienced by all particles in the population, here randomly chosen from the set of non-inferior solutions, w is the coefficient of inertia.
For non-inferior solutions and non-inferior solution sets: if there is no other feasible solution, making the targets in one solution all worse than the solution, then the solution is called the non-inferior solution of the multi-objective optimization problem. The set of all non-inferior solutions is called a non-inferior solution set.
The screening non-inferior solution set is mainly divided into an initial screening non-inferior solution set and an updating non-inferior solution set. The initial screening of the non-inferior solution set refers to that after the initialization of the particles, when one particle is not dominated by other particles (namely, the evaluation indexes of the non-inferior particles are superior to the particles), the particles are put into the non-inferior solution set, and one particle is randomly selected from the non-inferior solution set as the group optimal particle before the update of the particles. Updating the non-inferior solution set means that when the new particle is not supported by other particles and the particles in the current non-inferior solution set, the new particle is put into the non-inferior solution set, and one particle is randomly selected from the non-inferior solution set as the group optimal particle before the particle is updated each time.
The time difference positioning calculation is carried out by adopting a Chan algorithm, and excessive introduction is not carried out.
The invention is mainly optimized in the following aspects:
on the first hand, in order to shorten the algorithm time, the invention introduces a master-slave unmanned aerial vehicle control strategy aiming at the problem that the pitch angle and the yaw angle of the unmanned aerial vehicle need to be repeatedly judged in the existing multi-unmanned aerial vehicle space position optimization algorithm. Through setting up an unmanned aerial vehicle as the host computer, other unmanned aerial vehicles are from the computer, need satisfy unmanned aerial vehicle maximum communication distance restriction and minimum anticollision distance restriction between host computer and the follow computer, only need satisfy minimum anticollision distance restriction between the follow computer and the follow computer. And optimizing the space position of the unmanned aerial vehicle by preliminarily planning to determine the flight path of the host and accurately planning to determine the spatial position arrangement of the slave relative to the host. Under such strategy, the distance between two positioning track points is far, the distance between the master unmanned aerial vehicle and the slave unmanned aerial vehicle is small, and under the condition that the pitch angle and the yaw angle of the master unmanned aerial vehicle are not changed greatly, the slave unmanned aerial vehicle does not need to judge and correct the rotation angle limit of the unmanned aerial vehicle.
In a second aspect, the invention aims at the problem that the calculation amount is large when the maximum and minimum distances of the unmanned aerial vehicles need to be judged in the existing multi-unmanned aerial vehicle space position optimization algorithm, and introduces a spherical coordinate system to optimize the space position of the slave relative to the host, as shown in fig. 2, in the coordinates, the position of the host is taken as the origin of the spherical coordinate system, and the position of the host is used as the origin of the spherical coordinate system
The position of the slave machine is adjusted through parameters, and the position of the slave machine is ensured to meet the maximum and minimum distances between the unmanned aerial vehicle.
Most of the existing optimization strategies are to optimize the spatial position XYZ spatial coordinate of the unmanned aerial vehicle, and the spatial position of each unmanned aerial vehicle is required to be frequently compared and corrected to ensure that each unmanned aerial vehicle meets the maximum and minimum spacing requirements of the unmanned aerial vehicle. But using spherical coordinatesIs directed to
Optimizing is carried out, the radius R of the spherical coordinate is set to be the maximum distance between the unmanned aerial vehicles, optimizing of the space position of each unmanned aerial vehicle can be limited within the spherical coordinate, and therefore the problem that the operation time is too long due to the fact that the limiting condition of the maximum distance between the unmanned aerial vehicles needs to be considered in a large quantity in the optimizing process is solved. The optimization strategy through the spherical coordinates only needs to consider the limitation of the minimum distance of the unmanned aerial vehicle, so that a large amount of operation time is saved.
In order to improve the positioning performance, the problem that no solution point exists in positioning in time difference positioning is solved, and the probability of no solution point in the multi-unmanned aerial vehicle formation positioning process is reduced by introducing a Y-shaped station distribution mode.
The specific strategy is that in the space position optimization of the slave machines, the horizontal included angle between the host machine and a target positioning point is judged, one slave machine is positioned on the horizontal plane of the connection line of the host machine and a target point obtained by positioning, and the azimuth angle theta of the other two slave machines is limited1、θ2The angle of the positioning device is approximate to Y-shaped station distribution, and then the optimal position is calculated through a particle swarm optimization algorithm. As shown in fig. 3, the projection of the Y-shaped cloth station on the XOY plane is shown, the central unmanned aerial vehicle is the master, the peripheral unmanned aerial vehicles are the slaves, and the projection is controlled by theta1、θ2To change the slave position of the Y-lay station.
According to the invention, four unmanned aerial vehicles are taken as an unmanned aerial vehicle formation, the maximum communication distance, the minimum anti-collision distance, the path length, the safety, the positioning precision and the unmanned aerial vehicle kinematics limit of the unmanned aerial vehicles are comprehensively taken as consideration, the current position of the unmanned aerial vehicle is taken as a starting point, and the position of a target source is solved for the first time as a terminal point to carry out the flight path planning of the unmanned aerial vehicle formation.
The flight path planning is divided into two stages, namely primary planning and accurate planning.
The preliminary planning is to set a proper step length to plan the flight path of the host by taking the safety of the unmanned aerial vehicle in the flying process and the kinematic limit of the unmanned aerial vehicle into consideration through a traditional heuristic algorithm. And generating a track of the host machine through preliminary planning, and recording the position of the host machine at the end of each step length to be used as a track point needing to be calculated during accurate planning. Fig. 6 is an XOY plane projection of the generated host flight path diagram, fig. 7 is a three-dimensional flight path diagram of the host, three hollow cylinders in the middle represent a no-fly area, a semicircular area represents a radar detection area, the no-fly area and the radar detection area need to be avoided in consideration of safety during initial planning, and a reserved space ensures that the slave machine does not enter the no-fly area and the radar detection area during accurate planning.
During accurate planning, an optimal track of the three slave machines is calculated by a multi-target particle swarm optimization algorithm according to the primary planned host track point, wherein the optimal track has comprehensive cost of safety, positioning accuracy and path length and meets the limitation of the maximum communication distance and the minimum anti-collision distance of the unmanned aerial vehicle, and the optimal track is shown in fig. 8 and 9. FIG. 8 is a projection view of formation of unmanned aerial vehicles on an XOY plane, wherein four tracks correspond to four formations of unmanned aerial vehicles; fig. 9 is a three-dimensional flight path diagram of formation of unmanned aerial vehicles, and a cylindrical area is a no-fly area and a circular area is a radar detection area.
When accurate planning is carried out, in order to meet the limitation of the maximum communication distance of the unmanned aerial vehicle, the position of the unmanned aerial vehicle is planned by using the spherical coordinates. In spherical coordinates, the spherical radius is the maximum communication distance of the unmanned aerial vehicle, the initially planned host track point is taken as the sphere center, and the spherical coordinate parameters are limited to R less than or equal to Lmax、-π≤θ≤π、
By aligning spherical coordinates with groups of particles
And optimizing the data.
The unmanned aerial vehicle formation time difference positioning method based on particle swarm optimization algorithm calculation comprises the following steps:
step 1, modeling a flight path planning problem of unmanned aerial vehicle formation into a flight path planning of unmanned aerial vehicle formation comprehensively considering the maximum communication distance, the minimum anti-collision distance, the path length, the safety, the positioning precision and the kinematics limit of the unmanned aerial vehicle as the cost, and acquiring the information of obstacles and threat areas in the environment;
step 3, searching the optimal spatial position of the slave machine at the plurality of positioning track points determined in the step 2 through a particle swarm optimization algorithm;
further, in step 1, modeling the maximum communication distance, the minimum anti-collision distance, the path length, the safety, the positioning accuracy and the kinematics limit of the unmanned aerial vehicle as the following models:
(1) maximum communication distance:
(xt yt zt) Is the host position of the unmanned aerial vehicle, (x)t,i yt,i zt,i) For the ith slave position, dmaxThe maximum communication distance of the unmanned aerial vehicle.
(2) Minimum collision-proof distance:
(3) The length of the path:
fdisfor a cost of distance, vt,nThe flight speed of the nth unmanned aerial vehicle at the moment t, N is the number of the unmanned aerial vehicles, S is the position of the track end point,
for the position of unmanned aerial vehicle corresponding to moment t.
(4) Safety:
fthreat＝fanti+fprohibitequation 6
fthreatFor safety cost, fantiFor the enemy to counter-reconnaissance the threat cost, fprohibitTo disable a regional threat, wherein:
assuming that there are R radar detection areas, the center position coordinates of the R-th radar detection area can be expressed as
The farthest detection radius of the radar detection area is dr max，dr minRepresents the radius of the radar early warning area of the r-th part when drmin＜dt,n,r≤drmaxWhen a molecule is multiplied by drminMainly to raise the value of the threat cost, otherwise drmin＜dt,n,r≤drmaxInsofar, the threat cost is minimal and the change is insignificant. Pt,n,r(dt,n,r) At time tThreat cost per unit time for r radars to nth drone position, (x)t,n,yt,n,zt,n) The position of the nth unmanned aerial vehicle at the moment t is shown.
Assuming that W no-fly regions coexist, the center position of the W-th no-fly region is represented as
The minimum radius of the no-fly region is set as dw minWhen the unmanned aerial vehicle enters the area, collision can directly occur; the maximum threat radius of the no-fly zone is dw maxWhen the unmanned plane is at dw minTo dw maxWhen the unmanned aerial vehicle is in the area range, due to the problems of wind direction, shaking of the unmanned aerial vehicle in the flying process and the like, the possibility of collision with the no-fly area exists, and the closer the distance between the unmanned aerial vehicle and the no-fly area is, the greater the threat is; when the distance from the unmanned aerial vehicle to the w-th no-fly area is larger than dw maxAnd meanwhile, the threat of the no-fly area to the unmanned aerial vehicle can be ignored. dt,n,wCenter of w-th no-fly zone
Coordinate position X of unmanned aerial vehicle to nth framet,nThe planar distance of (a); when d iswmin＜dt,n,w≤dwmaxWhen a molecule is multiplied by dw minMainly to raise the value of the threat cost.
(5) Positioning accuracy:
fCRLB＝(trace(C))1/2 equation 13
fCRLBThe lower boundary of cramer-mero for positioning accuracy is used as the cost of positioning accuracy, and is not described in detail.
(6) Unmanned aerial vehicle kinematics restriction:
vmax、
θmaxthe maximum speed, the maximum pitch angle and the maximum azimuth angle of the unmanned aerial vehicle.
Furthermore, in the step 2, in the flight path planning of the host of the unmanned aerial vehicle, the flight path planning is carried out by considering the kinematics constraint, the distance cost and the safety of the unmanned aerial vehicle, in this step, the host is used as a main reference for judging the kinematics constraint of formation of the unmanned aerial vehicle, and when the host does not reach the kinematics limit of the unmanned aerial vehicle and the position of the positioning point is far, the host can be used for replacing the formation of the unmanned aerial vehicle to carry out the kinematics constraint judgment approximately. At the same time, the step size of each step of the drone in the flight path planning (i.e. the distance the drone has traveled once in the flight path planning) is determined at this step according to the flight speed of the drone. And planning a plurality of positioning track points according to the set step length to be used as positions for the unmanned aerial vehicles to form a formation to execute positioning operation.
Further, in the step 3 of planning the positions of the slaves, the position of the main machine track positioning point planned in the step 2 is optimized through a particle swarm optimization algorithm. In this step, the determination of the optimal position of the slave uses the minimum anti-collision distance, the path length, the safety and the positioning accuracy as judgment indexes. In the invention, the position of the slave machine is selected by adopting a spherical coordinate mode, the spherical radius is selected as the maximum communication distance of the unmanned aerial vehicle, and the position optimization of the slave machine is carried out in the radius range, so the maximum communication distance limit of the unmanned aerial vehicle is not required to be considered in the optimization. Meanwhile, as Chan is adopted for resolving in the time difference positioning process, a positioning blind area and no solution phenomenon exist, so that the time difference positioning method is used for positioning the time differenceAnd introducing a Y-shaped station distribution mode of an unmanned aerial vehicle. The specific scheme is that a host is used as the center of a Y-shaped station, one slave machine is positioned on a vertical surface of a connecting line of the host and a target positioning point, the range of a horizontal angle theta in the optimizing process of the remaining two slave machines is limited, and the projection of four unmanned aerial vehicles on an XOY plane is similar to the Y-shaped station, so that the probability of falling into a positioning blind area and a non-solution area during resolving can be obviously reduced. Is not limited in this step
The height of the four unmanned aerial vehicles is different, and the projection on the XOY plane is similar to Y-shaped station arrangement.
Further, the flight path obtained after connecting the flight path points in the step 4 is the formation optimal flight path obtained through the particle swarm optimization algorithm.
Further, in step 5, after the main unmanned aerial vehicle acquires the spatial position and the measurement data of each unmanned aerial vehicle, the Chan algorithm is adopted to perform time difference positioning calculation, and final target positioning data is generated.
Claims (6)
1. Positioning accuracy-based unmanned aerial vehicle formation flight path planning method is characterized by comprising the following steps: the method comprises the steps of preliminary planning and accurate planning, wherein the preliminary planning is to set a proper step length to plan the flight path of a host by taking safety and unmanned aerial vehicle kinematic limit in the flight process of the unmanned aerial vehicle into consideration through a traditional heuristic algorithm; generating a track of the host by preliminary planning, and recording the position of the host at the end of each step length as a track point needing to be calculated during accurate planning; the method comprises the following steps of accurately planning, calculating an optimal track with the safety, the positioning precision and the path length of a slave machine as comprehensive costs and meeting the limitation of the maximum communication distance and the minimum anti-collision distance of the unmanned aerial vehicle aiming at a primary planned host track point by a multi-target particle swarm optimization algorithm, and specifically comprises the following steps:
step 1: modeling the flight path planning problem of the formation of the unmanned aerial vehicles into the flight path planning of the formation of the unmanned aerial vehicles by comprehensively considering the maximum communication distance, the minimum anti-collision distance, the path length, the safety, the positioning precision and the kinematic limit of the unmanned aerial vehicles as the cost, and acquiring the information of obstacles and threat areas in the environment;
step 2: performing primary track planning on the unmanned aerial vehicle host through a heuristic algorithm, and determining a plurality of positioning track points from a track starting point to a track end point;
and step 3: searching the optimal spatial position of the slave machine at the plurality of positioning track points determined in the step 2 by a particle swarm optimization algorithm;
and 4, step 4: forming the optimal positions of the master unmanned aerial vehicle and the slave unmanned aerial vehicle determined in the steps 2 and 3 at each track point into an unmanned aerial vehicle track;
and 5: and acquiring the position and the measurement data of each unmanned aerial vehicle at the positioning track point, resolving to obtain target position information, and finishing planning.
2. The unmanned aerial vehicle formation flight path planning method based on positioning accuracy as claimed in claim 1, wherein: in the step 1, modeling the maximum communication distance, the minimum anti-collision distance, the path length, the safety, the positioning precision and the kinematics limit of the unmanned aerial vehicle as the following models:
(1) maximum communication distance:
wherein: (x)t yt zt) Is the host position of the unmanned aerial vehicle, (x)t,i yt,i zt,i) For the ith slave position, dmaxThe maximum communication distance of the unmanned aerial vehicle is set;
(2) minimum collision-proof distance:
wherein:
position of any two unmanned aerial vehicles at time t, n1≠n2，dminThe minimum collision-proof distance is set for the unmanned plane;
(3) the length of the path:
wherein: f. ofdisFor a cost of distance, vt,nThe flight speed of the nth unmanned aerial vehicle at the moment t, N is the number of the unmanned aerial vehicles, S is the position of the track end point,
the position of the unmanned aerial vehicle corresponding to the time t;
(4) safety:
fthreat＝fanti+fprohibit
wherein: f. ofthreatFor safety cost, fantiFor the enemy to counter-reconnaissance the threat cost, fprohibitTo disable a regional threat, wherein:
assuming that there are R radar detection areas, the center position coordinates of the R-th radar detection area can be expressed as
The farthest detection radius of the radar detection area is dr max，dr minRepresents the radius of the radar early warning area of the r-th part when drmin＜dt,n,r≤drmaxWhen a molecule is multiplied by drminMainly to raise the value of the threat cost, otherwise drmin＜dt,n,r≤drmaxIn scope, threat costs are minimal and varyingIs not obvious; pt,n,r(dt,n,r) For the threat cost of the r radar to the nth unmanned aerial vehicle position at the time t, (x)t,n,yt,n,zt,n) The position of the nth unmanned aerial vehicle at the time t is shown;
assuming that W no-fly regions coexist, the center position of the W-th no-fly region is represented as
The minimum radius of the no-fly region is set as dw minWhen the unmanned aerial vehicle enters the area, collision can directly occur; the maximum threat radius of the no-fly zone is dw maxWhen the unmanned plane is at dw minTo dw maxWhen the unmanned aerial vehicle is in the area range, due to the problem of wind direction and shaking of the unmanned aerial vehicle during the flight process, the possibility of collision with the no-fly area exists, and the closer the distance between the unmanned aerial vehicle and the no-fly area is, the greater the threat is; when the distance from the unmanned aerial vehicle to the w-th no-fly area is larger than dw maxIn the meantime, the threat of the no-fly area to the unmanned aerial vehicle can be ignored at the moment, and dt,n,wCenter of w-th no-fly zone
Coordinate position X of unmanned aerial vehicle to nth framet,nThe planar distance of (a); when d iswmin＜dt,n,w≤dwmaxWhen a molecule is multiplied by dwminMainly for improving the numerical value of the threat cost;
(5) positioning accuracy:
fCRLB＝(trace(C))1/2
wherein: f. ofCRLBClarmet for positioning accuracyThe lower bound, here as the cost of positioning accuracy;
(6) unmanned aerial vehicle kinematics restriction:
wherein: v. ofmax、
θmaxThe maximum speed, the maximum pitch angle and the maximum azimuth angle of the unmanned aerial vehicle.
3. The unmanned aerial vehicle formation flight path planning method based on positioning accuracy as claimed in claim 2, wherein: in the step 2, in the flight path planning of the host machine of the unmanned aerial vehicle, the flight path planning is carried out by considering the kinematics constraint, the route cost and the safety of the unmanned aerial vehicle, in the step, the host machine is used as a main reference for judging the formation kinematics constraint of the unmanned aerial vehicle, and when the host machine does not reach the kinematics limit of the unmanned aerial vehicle and the position of a positioning point is far, the host machine can be used for replacing the formation of the unmanned aerial vehicle to carry out the kinematics constraint judgment approximately; determining the step length of each step of the unmanned aerial vehicle in the flight path planning according to the flight speed of the unmanned aerial vehicle; and planning a plurality of positioning track points according to the set step length to be used as positions for the unmanned aerial vehicles to form a formation to execute positioning operation.
4. The unmanned aerial vehicle formation flight path planning method based on positioning accuracy as claimed in claim 3, wherein: in the step 3, in the planning of the positions of the slave machines, the optimization operation is carried out at the positions of the planned main machine flight path positioning points in the step 2 through a particle swarm optimization algorithm; the optimal position of the slave machine is determined by using the minimum anti-collision distance, the path length, the safety and the positioning precision as judgment indexes; because the Chan is adopted for resolving in the time difference positioning process, a positioning blind area and no solution phenomenon exist, and a Y-shaped station distribution mode of the unmanned aerial vehicle is introduced; the specific scheme is that a host machine is used as the center of a Y-shaped cloth station, a slave machine is positioned on a vertical surface of a connecting line of the host machine and a target positioning point,the range of the horizontal angle theta of the remaining two slave machines in the optimizing process is limited, so that the projection of the four unmanned aerial vehicles on the XOY plane is similar to Y-shaped station distribution, and the probability of falling into a positioning blind area and a non-solution area during resolving can be obviously reduced; is not limited in this step
The height of the four unmanned aerial vehicles is different, and the projection on the XOY plane is similar to Y-shaped station arrangement.
5. The unmanned aerial vehicle formation flight path planning method based on positioning accuracy as claimed in claim 4, wherein: and 4, connecting all track points in the step 4 to obtain a track, namely the optimal formation track obtained by the particle swarm optimization algorithm.
6. The unmanned aerial vehicle formation flight path planning method based on positioning accuracy as claimed in claim 5, wherein: in step 5, after the main unmanned aerial vehicle acquires the spatial position and the measurement data of each unmanned aerial vehicle, the Chan algorithm is adopted to perform time difference positioning calculation, and final target positioning data is generated.
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