CN114237304A - Multi-unmanned aerial vehicle consistency aggregation method based on Dubins dynamic path planning - Google Patents
Multi-unmanned aerial vehicle consistency aggregation method based on Dubins dynamic path planning Download PDFInfo
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Abstract
The invention discloses a multi-unmanned aerial vehicle consistency aggregation method based on Dubins dynamic path planning, which comprises the following steps: 1) multipath dynamic routing: aiming at the random distribution of the air positions of the unmanned aerial vehicles before aggregation, planning the unmanned aerial vehicles and target waypoints in an aggregation area by adopting a shortest path optimization algorithm, and dynamically generating multipath Dubins arc fairways; 2) aggregation of multiple unmanned aerial vehicles: the problem that unmanned aerial vehicles cannot finish consistency aggregation due to different distances of planned paths of the multiple unmanned aerial vehicles is solved, a control strategy that the tail end is in hovering waiting is provided through dynamically adjusting the hovering radius, and therefore the purpose that the multiple unmanned aerial vehicles reach an aggregation area at the same time is achieved accurately. The nonlinear numerical simulation of the three unmanned aerial vehicles shows that the method provided by the invention can complete dynamic path planning in real time and efficiently execute the consistency aggregation task of the multiple unmanned aerial vehicles.
Description
Background
With the increasing complexity of modern battlefield environments, a single unmanned aerial vehicle cannot complete designated combat tasks. In order to ensure the execution success rate of the combat mission, multiple unmanned aerial vehicles are needed to fight in a cooperative manner, and consistency aggregation is an important problem to be solved before the multiple unmanned aerial vehicles fight in a cooperative manner.
Therefore, a path planning and clustering method is designed to meet the requirement of rapid and consistent clustering.
Disclosure of Invention
In order to overcome the problems in the background art, the application aims to provide a multi-unmanned-aerial-vehicle consistency aggregation method based on Dubins dynamic path planning, and the multi-unmanned-aerial-vehicle air dynamic circular arc route planning can be completed by taking the shortest aggregation distance as an optimization target.
The application is realized by the following modes:
a multi-UAV consistency aggregation method based on Dubins dynamic path planning comprises the following steps:
1) the multiple unmanned aerial vehicles acquire information of target waypoints in the same number as the unmanned aerial vehicles in the aggregation area, and determine the corresponding target waypoint of each unmanned aerial vehicle in the aggregation area according to a shortest path optimization algorithm;
2) calculating the waypoint information required by different circular arc paths according to a Dubins real-time dynamic path planning algorithm;
3) aiming at the length of the planned path of each unmanned aerial vehicle, a terminal spiral waiting control strategy is adopted, and the consistent arrival of the multiple unmanned aerial vehicles is realized.
Further, the determining a target waypoint corresponding to each unmanned aerial vehicle in the aggregation area according to the shortest path optimization algorithm specifically includes the following contents:
setting a shortest path optimization algorithm matrix phi:
wherein ,ΦijThe path length from the ith unmanned aerial vehicle to the jth target point is Dubins, and i is j;
firstly, determining the minimum path length in the matrix phi to obtain the first shortest path length phiijAnd stored, and then the row in which it is located,Deleting the columns to obtain an updated path matrix;
then searching the minimum path length in the updated matrix to obtain the length of a second shortest path, and deleting the row and the column; and so on until there is one element left in the matrix; and sequencing the stored elements to obtain a shortest path vector:
[Φ1，1～j Φ2，1～j … Φi，1～j] (2)
wherein, the first subscript of the above formula element is the ith unmanned aerial vehicle, the second subscript is the aggregation target point label corresponding to each unmanned aerial vehicle, phii，1～jAnd the shortest path length corresponding to the ith unmanned aerial vehicle is represented.
Further, according to the Dubins real-time dynamic path planning algorithm, 4 pieces of key waypoint information are calculated and respectively correspond to the circle center o of the first arc of the Dubins path in the Dubins real-time dynamic path planning algorithmsA transition point n from the first arc segment to the straight line segment1A transition point n from the straight line segment to the second circular arc segment2The center o of the second arcf。
Further, the starting waypoint P is knowni(x1,y1α), end waypoint Pf(x2,y2Beta), wherein alpha is the heading angle of the starting end waypoint, and beta is the heading angle of the tail end waypoint;
if the difference between the initial end course angle, the tail end course angle and the initial end and tail end connecting line is less than or equal to the angle sigma, directly planning a straight line path;
if the angle is larger than the angle sigma, dynamically planning a Dubins path according to the Dubins real-time dynamic path planning algorithm, and calculating the key waypoint information in the Dubins path through the following processes:
1) circle center osAnd (3) coordinate calculation:
wherein r is the radius of the first circular arc, and the '+/-' meaning in the above formula is as follows:
'-' indicates that the first arc is a right turn;
'+' indicates that the first arc is a left turn;
2) circle center ofAnd (3) coordinate calculation:
3) transition point n1And (3) coordinate calculation:
wherein ,
if the first arc is right-turning:
if the first arc is left-turning:
wherein t is the arc length of the first arc;
4) transition point n2And (3) coordinate calculation:
wherein ,
where p is the length of the straight line segment path.
As an effective implementation scheme of the present application, the specific implementation process of implementing the consistent arrival of multiple drones by using the terminal hovering wait control strategy is as follows:
setting a minimum equal path length phimin，ΦminExpressed as:
Φmin＝Φimax+2πrpmin (10)
wherein ,ΦimaxFor maximum Dubins Path Length, r, in multiple dronespminThe minimum circle radius of the unmanned plane;
then the tail end of the ith unmanned aerial vehicle spirals by the radius riComprises the following steps:
wherein ,ΦijThe Dubins path length of the ith drone before the leading end spirals;
if the planned path is a straight line, the circle center O of the circle is circledpThe coordinates are
wherein ,rpIs the radius of the unmanned plane in a circle,
if the planned path is a Dubins path, the circle center of the circle is OpThe coordinates are
Advantageous effects
The method combines the Dubins path planning with good real-time performance, high smoothness and low calculation amount with the consistency aggregation algorithm of the multiple unmanned aerial vehicles to finish the rapid consistency aggregation of the multiple unmanned aerial vehicles. The multiple unmanned aerial vehicles adopt a dynamic path planning algorithm to obtain the air routes with the same distance according to the real-time position information of the unmanned aerial vehicles and the position information of the integrated target points before the integration, so that the unmanned aerial vehicles can accurately complete the consistency integration task when flying at the cruising speed.
The problem that unmanned aerial vehicles cannot finish consistency aggregation due to different distances of planned paths of the multiple unmanned aerial vehicles is solved, a control strategy that the tail end is in hovering waiting is provided through dynamically adjusting the hovering radius, and therefore the purpose that the multiple unmanned aerial vehicles reach an aggregation area at the same time is achieved accurately. The nonlinear numerical simulation of the three unmanned aerial vehicles shows that the method provided by the invention can complete dynamic path planning in real time and efficiently execute the consistency aggregation task of the multiple unmanned aerial vehicles.
Drawings
Fig. 1 is a flow chart of dynamic path planning and consistency aggregation for multiple drones;
FIG. 2 is a schematic view of an unmanned aerial vehicle and an initial position of a rendezvous point;
FIG. 3 is a schematic view of a difference between heading angles;
FIG. 4 is a diagram of the Dubins path;
FIG. 5 is a schematic diagram of a conversion point coordinate solving geometry;
FIG. 6 is a schematic diagram of an end spiral waiting strategy;
fig. 7 shows a flight trajectory simulation result of the unmanned aerial vehicle in the northeast sky coordinate system.
Detailed Description
The invention is further described below with reference to the accompanying drawings.
This example describes a consistency aggregation method for multiple drones based on Dubins dynamic path planning, and a flowchart thereof is shown in fig. 1. The multiple unmanned aerial vehicles autonomously fly in the air to wait before executing tasks, after receiving the information of the aggregation target area, the optimal aggregation point of each unmanned aerial vehicle is determined according to the shortest path optimization algorithm, then the Dubins path is dynamically generated in real time, and the purpose of simultaneously reaching the aggregation area is realized by adding the tail end spiral radius.
Shortest path optimization algorithm
As shown in fig. 2: suppose that 3 unmanned aerial vehicles (1, 2 and 3) are distributed in the air, three aggregation points (1, 2 and 3) are arranged in a target aggregation area, and after the 3 unmanned aerial vehicles receive an aggregation command, the optimal target waypoint of the unmanned aerial vehicle is calculated through an optimization algorithm according to the shortest route.
Step 1: calculating the Dubins length phi of the ith unmanned aerial vehicle corresponding to the jth target pointijFinally, obtaining a shortest path matrix Φ:
wherein the first subscript is unmanned aerial vehicle reference numeral, and the second subscript is target aggregation point reference numeral.
Step 2: finding the minimum Dubins length phi in the matrix phi through a minimum algorithmijAnd stored. And then deleting the ith row and the jth column of the matrix to obtain a new matrix phi'.
E.g. phi in matrix phi13And finally, deleting the 1 st row and the 3 rd column to obtain an updated matrix phi':
and step 3: and (4) updating whether only one element remains in the matrix, if so, turning to the step 4, and if not, turning to the step 2.
And 4, step 4: arranging all the stored elements and the last element in the updating matrix according to the first subscript from small to large to obtain the shortest path vector:
[Φ1，1～j Φ2，1～j … Φi，1～j] (3)
the shortest path vector in this example is:
[Φ1，3 Φ2，1 Φ3，2] (4)
dynamic path planning
As can be seen from equation 4, the target aggregation point corresponding to each drone takes the 1 st drone as an example. Suppose that the current position of the unmanned aerial vehicle is Pi(x1,y1α), the target aggregation point 3 is Pf(x2,y2Beta), where alpha is the unmanned aerial vehicle heading angle, the direction is a forward counterclockwise rotation from the x-axis, and beta is the heading angle of the target rendezvous point.
When the position and the course angle of the unmanned aerial vehicle in the air and the position and the course angle of the target aggregation point are shown in figure 3, the difference values of the course angle alpha of the unmanned aerial vehicle, the course angle beta of the target aggregation point and the included angle gamma of the connecting line of the starting end and the tail end are as follows
If epsilon1Sigma or less and epsilon2When σ is ≦ the path is still planned using Dubins, a path with a green dotted center as in fig. 3 is generated, which is clearly not reasonable. To optimize this defect, when ε1Sigma or less and epsilon2And when the sigma is less than or equal to the sigma, using the starting end and the tail end connecting lines as the planned path.
The unmanned aerial vehicle and the target aggregation point are randomly distributed in the coordinate system and are not beneficial to solving the coordinates of the waypoint, and the method for solving the coordinates through the coordinate system conversion simplifies the coordinate solving method so as to solve the coordinates by the current position P of the unmanned aerial vehicleiAs the origin of coordinates o', with the drone and the target rendezvous point PfThe connecting line of (A) is an x axis, the vertical line of (B) is a y axis, and a transformation coordinate system x ' o ' y ' is established.
According to the unmanned plane position P, as shown in FIG. 4iAnd a target rendezvous point PfThe obtained Dubins curve has four key waypoints which are respectively the circle center o of the first section of circular arcsA conversion point n from the circular arc section to the straight line section1A point n of transition from the straight segment to the second circular segment2The circle center o of the second section of arcf。
Circle center osCoordinate calculation
Wherein r is the radius of the arc, and the '+/-' meaning in the above formula is as follows:
'-' indicates the arc segment is right-turned;
'+' indicates that the arc segment is left-handed.
Circle center ofCoordinate calculation
Transition point n1And (3) coordinate calculation:
as shown in fig. 5, taking the example of the first arc right turn, the heading angle δ of the unmanned aerial vehicle is transformed by the coordinate system1Is composed of
δ1＝α-θ (7)
Wherein theta is an included angle between a connecting line of the current position of the unmanned aerial vehicle and the target aggregation point and an x axis of the rectangular coordinate system,
after the unmanned aerial vehicle passes through the first section of arc, the point n is converted1Course angle delta of2Is composed of
Wherein t is the arc length of the first section of arc, and r is the radius of the arc.
From geometrical relationships
δ3＝δ2 (9)
The transformation point n in the transformation coordinate system can be obtained from the formulas 7, 8, 9 and 101Has the coordinates of
If the first arc is left-turning, the transformation point n in the coordinate system is transformed1The coordinates of (a) are:
a transformation matrix M for transforming the coordinate system to the rectangular coordinate system is
Conversion point n in rectangular coordinate system1Has the coordinates of
The following can be obtained:
transition point n2And (3) coordinate calculation:
as shown in FIG. 5, the transformation point n in the transformed coordinate system can be obtained from equation 102Has the coordinates of
Wherein p is the length of the straight line segment of the path.
I.e. the conversion point n in the rectangular coordinate system2Has the coordinates of
The following can be obtained:
end spiral waiting strategy
As shown in fig. 6, the path of the green solid line in the graph can be obtained through dynamic path planning, and because the path lengths of each unmanned aerial vehicle are different, if the aggregation point is to be reached at the same time, the flight speed of each unmanned aerial vehicle needs to be calculated in real time according to the distance from each unmanned aerial vehicle to the target aggregation point in the flight, so that the target aggregation point is reached at the same time. Because the unmanned aerial vehicle has higher speed and the speed control precision can not meet the requirement in a battlefield environment, the unmanned aerial vehicle flies at a constant speed during aggregation, and a hovering waiting strategy is introduced at the tail end, so that the flying distance of each unmanned aerial vehicle reaching a target aggregation point is the same, and the simultaneous arrival of multiple unmanned aerial vehicles is realized.
Radius of spiral
In order to enable the unmanned aerial vehicle to reach the target aggregation point at the highest speed, the total path length is shortest. Minimum same path length phi of unmanned aerial vehicle after circling for one circle at tail endminCan be expressed as:
Φmin＝Φimax+2πrpmin (19)
wherein ,ΦimaxFor maximum Dubins Path Length, r, in multiple dronespminThe minimum radius of the spiral of the unmanned plane.
Then the tail end of the ith unmanned aerial vehicle spirals by the radius riComprises the following steps:
wherein ,ΦijThe Dubins path length of the ith drone before the leading end hover.
Circle center coordinate of circle
And determining the spiral direction according to the path calculated by the dynamic path planning algorithm. If the path is a straight line, the hovering direction of the unmanned aerial vehicle is specified as right hovering; if the path is a Dubins path and the second arc segment in the path is a left turn, the unmanned aerial vehicle is specified to be left-handed in the spiral direction, and if the second arc segment in the path is a right turn, the unmanned aerial vehicle is specified to be right-handed in the spiral direction.
The planned path being a straight line
Circle center O of the circlepThe coordinates are
wherein ,rpIs the radius of the unmanned plane.
The planned path is a circular arc
Assuming a circle center ofThe coordinate is (x)of,yof) According to the theorem of similar triangle, the circle center O of the circle can be obtainedpThe coordinates are
The unmanned aerial vehicle realizes consistency aggregation by tracking the planned route waypoints.
Simulation experiment verification
In order to verify the correctness of the multi-unmanned aerial vehicle dynamic path planning and consistency aggregation method provided by the invention, a simulation test is carried out through a certain type of fixed wing unmanned aerial vehicle nonlinear model. According to the flight performance of the fixed-wing unmanned aerial vehicle, simulation parameters are set as follows: the cruising speed V of the unmanned aerial vehicle is 40m/s, the flying height is 1000m, the turning radius r of the unmanned aerial vehicle is 400m, and the minimum hovering radius is 400 m. The initial position of the unmanned aerial vehicle and the position of the aggregation target point are as follows:
table 1 simulation initial position information of unmanned aerial vehicle
| Unmanned aerial vehicle reference numeral | North coordinate/m | East coordinate/m | Course angle/° |
| 1 | 0 | 0 | -30 |
| 2 | 200 | 1000 | 60 |
| 3 | 300 | -1000 | 180 |
TABLE 2 aggregation of target Point simulation initial position information
| Aggregating target point labels | North coordinate/m | East coordinate/m | Course angle/° |
| 1 | 3000 | 0 | 0 |
| 2 | 2900 | -100 | 0 |
| 3 | 2900 | -100 | 0 |
The simulation result is shown in fig. 7, 3 unmanned aerial vehicles fly along an initial course angle straight line at an initial position, send a concentration command to the unmanned aerial vehicles when flying for 10s, and 3 unmanned aerial vehicles concentrate to a target concentration point. It can be seen from the figure that when 3 unmanned aerial vehicles receive the rendezvous command, the path is planned immediately according to the current position, and the unmanned aerial vehicles fly according to the planned path, the spiral paths with different radii can be seen at the tail end of the planned path, and the 3 unmanned aerial vehicles reach the rendezvous target point after circling for one circle.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and adjustments can be made without departing from the principle of the present invention, and these modifications and adjustments should also be regarded as the protection scope of the present invention.
Claims (5)
1. A consistency aggregation method for multiple unmanned aerial vehicles based on Dubins dynamic path planning is characterized in that: the method comprises the following steps:
1) the multiple unmanned aerial vehicles acquire information of target waypoints in the same number as the unmanned aerial vehicles in the aggregation area, and determine the corresponding target waypoint of each unmanned aerial vehicle in the aggregation area according to a shortest path optimization algorithm;
2) calculating the waypoint information required by different circular arc paths according to a Dubins real-time dynamic path planning algorithm;
3) aiming at the length of the planned path of each unmanned aerial vehicle, a terminal spiral waiting control strategy is adopted, and the consistent arrival of the multiple unmanned aerial vehicles is realized.
2. The Dubins dynamic path planning-based multi-UAV (unmanned aerial vehicle) consistency aggregation method according to claim 1, wherein: the method comprises the following steps of determining a corresponding target waypoint of each unmanned aerial vehicle in a rendezvous area according to a shortest path optimization algorithm, and specifically comprising the following contents:
setting a shortest path optimization algorithm matrix phi:
wherein ,ΦijThe path length from the ith unmanned aerial vehicle to the jth target point is Dubins, and i is j;
firstly, determining the minimum path length in the matrix phi to obtain the first shortest path length phiijStoring the updated path matrix, and deleting the row and the column where the updated path matrix is located to obtain an updated path matrix;
then searching the minimum path length in the updated matrix to obtain the length of a second shortest path, and deleting the row and the column; and so on until there is one element left in the matrix; and sequencing the stored elements to obtain a shortest path vector:
[Φ1，1～j Φ2，1～j…Φi，1～j] (2)
wherein, the first subscript of the above formula element is the ith unmanned aerial vehicle, the second subscript is the aggregation target point label corresponding to each unmanned aerial vehicle, phii，1～jAnd the shortest path length corresponding to the ith unmanned aerial vehicle is represented.
3. The Dubins dynamic path planning-based multi-UAV (unmanned aerial vehicle) consistency aggregation method according to claim 1, wherein: calculating 4 pieces of key waypoint information according to the Dubins real-time dynamic path planning algorithm, wherein the key waypoint information corresponds to the circle center o of the first circular arc of the Dubins path in the Dubins real-time dynamic path planning algorithm respectivelysA transition point n from the first arc segment to the straight line segment1A transition point n from the straight line segment to the second circular arc segment2The center o of the second arcf。
4. The Dubins dynamic path planning-based multi-UAV consistency aggregation method according to claim 3, wherein: known starting point Pi(x1,y1α), end waypoint Pf(x2,y2Beta), wherein alpha is the heading angle of the starting end waypoint, and beta is the heading angle of the tail end waypoint;
if the difference between the initial end course angle, the tail end course angle and the initial end and tail end connecting line is less than or equal to the angle sigma, directly planning a straight line path;
if the angle is larger than the angle sigma, dynamically planning a Dubins path according to the Dubins real-time dynamic path planning algorithm, wherein the key waypoint information in the Dubins path is obtained by calculation through the following processes:
1) circle center osAnd (3) coordinate calculation:
wherein r is the radius of the first circular arc, and the '+/-' meaning in the above formula is as follows:
'-' indicates that the first arc is a right turn;
'+' indicates that the first arc is a left turn;
2) circle center ofAnd (3) coordinate calculation:
3) transition point n1And (3) coordinate calculation:
wherein ,
if the first arc is right-turning:
if the first arc is left-turning:
wherein t is the arc length of the first arc;
4) transition point n2And (3) coordinate calculation:
wherein ,
where p is the length of the straight line segment path.
5. The Dubins dynamic path planning-based multi-UAV (unmanned aerial vehicle) consistency aggregation method according to claim 1, wherein: the specific implementation process for realizing the consistency arrival of the multiple unmanned aerial vehicles by adopting the tail end spiral waiting control strategy is as follows:
setting a minimum equal path length phimin，ΦminExpressed as:
Φmin＝Φimax+2πrpmin (10)
wherein ,ΦimaxFor maximum Dubins Path Length, r, in multiple dronespminThe minimum circle radius of the unmanned plane;
then the tail end of the ith unmanned aerial vehicle spirals by the radius riComprises the following steps:
wherein ,ΦijThe Dubins path length of the ith drone before the leading end spirals;
if the planned path is a straight line, the circle center O of the circle is circledpThe coordinates are
wherein ,rpIs the radius of the unmanned plane in a circle,
if the planned path is a Dubins path, the circle center of the circle is OpThe coordinates are
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