CN114326816A - Method and device for planning formation flight path of fixed-wing unmanned aerial vehicle based on artificial potential field - Google Patents
Method and device for planning formation flight path of fixed-wing unmanned aerial vehicle based on artificial potential field Download PDFInfo
- Publication number
- CN114326816A CN114326816A CN202210006529.9A CN202210006529A CN114326816A CN 114326816 A CN114326816 A CN 114326816A CN 202210006529 A CN202210006529 A CN 202210006529A CN 114326816 A CN114326816 A CN 114326816A
- Authority
- CN
- China
- Prior art keywords
- unmanned aerial
- aerial vehicle
- wing
- plane
- formation
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 230000015572 biosynthetic process Effects 0.000 title claims abstract description 130
- 238000000034 method Methods 0.000 title claims abstract description 54
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 41
- 230000004888 barrier function Effects 0.000 claims abstract description 28
- 238000003860 storage Methods 0.000 claims abstract description 12
- 230000001133 acceleration Effects 0.000 claims description 27
- 230000009471 action Effects 0.000 claims description 22
- 239000011159 matrix material Substances 0.000 claims description 21
- 230000003068 static effect Effects 0.000 claims description 16
- 238000004590 computer program Methods 0.000 claims description 14
- 230000008859 change Effects 0.000 claims description 10
- 230000006870 function Effects 0.000 claims description 8
- 230000009467 reduction Effects 0.000 claims description 5
- 239000000126 substance Substances 0.000 claims description 3
- 238000003619 Marshal aromatic alkylation reaction Methods 0.000 claims 1
- 230000002776 aggregation Effects 0.000 abstract description 5
- 238000004220 aggregation Methods 0.000 abstract description 5
- 230000008569 process Effects 0.000 description 12
- 238000010586 diagram Methods 0.000 description 7
- 238000004364 calculation method Methods 0.000 description 6
- 230000000694 effects Effects 0.000 description 4
- 238000011160 research Methods 0.000 description 4
- 239000007787 solid Substances 0.000 description 4
- 230000008901 benefit Effects 0.000 description 3
- 238000012423 maintenance Methods 0.000 description 3
- 230000001276 controlling effect Effects 0.000 description 2
- 238000009826 distribution Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000001105 regulatory effect Effects 0.000 description 2
- 230000001360 synchronised effect Effects 0.000 description 2
- 230000009466 transformation Effects 0.000 description 2
- 238000013459 approach Methods 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000005290 field theory Methods 0.000 description 1
- 230000002068 genetic effect Effects 0.000 description 1
- 239000004973 liquid crystal related substance Substances 0.000 description 1
- 239000002245 particle Substances 0.000 description 1
- 230000002085 persistent effect Effects 0.000 description 1
- 238000005096 rolling process Methods 0.000 description 1
- 238000010845 search algorithm Methods 0.000 description 1
Images
Landscapes
- Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)
Abstract
The application relates to a method and a device for planning formation flight paths of fixed-wing unmanned aerial vehicles based on an artificial potential field, computer equipment and a storage medium. The method comprises the following steps: aiming at the motion characteristics of a fixed wing aircraft, a kinematics model of a single fixed wing unmanned aerial vehicle is established, then based on an artificial potential field method, a potential field function is changed into an attraction signal from a target and a repulsion signal from an obstacle, and a single-machine path planning algorithm of the single fixed wing unmanned aerial vehicle under the conditions of the known target and the obstacle is provided. On the basis of the structure of "captain-captain", firstly, the captain target points are set according to the formation requirements and captain positions, and each unmanned aerial vehicle is assigned with the attribute of the obstacle, so that the formation integrated track planning is realized. On the basis of realizing the path planning of the single unmanned aerial vehicle, the invention considers the treatment measures of dynamic barriers, sets the cooperative rules among the members of the formation, and finally realizes the formation aggregation of the fixed-wing unmanned aerial vehicles.
Description
Technical Field
The application relates to the field of unmanned aerial vehicle formation track planning, in particular to a fixed wing unmanned aerial vehicle formation track planning method and device based on an artificial potential field, computer equipment and a storage medium.
Background
With the progress of science and technology, unmanned aerial vehicles are widely applied to various fields, such as disaster detection, low-altitude reconnaissance, atmospheric research, communication relay, disaster area search and rescue, scientific research and the like. At present, rotor unmanned aerial vehicle is used in many fields because it has the characteristics that the motion characteristic is comparatively nimble, but it also has not enoughly in aspects such as speed, voyage. Compared with the prior art, the fixed-wing unmanned aerial vehicle has the advantages of long range, long dead time, high speed, heavy load and the like, and is more suitable for tasks with long continuous working time and high requirement on airborne equipment. Meanwhile, the task execution capacity of the single unmanned aerial vehicle is limited, the fault tolerance rate of task execution can be improved by formation flight, the task completion effect is improved, and the reasonable formation even can reduce the task cost. For the path planning of the unmanned aerial vehicle, a lot of researchers carry out a lot of research, and a lot of mature algorithms are also provided. A number of intelligent algorithms such as a-search algorithm, Genetic Algorithm (GA), particle swarm algorithm (PSO), etc. However, since most of these studies are based on the assumption that the unmanned aerial vehicle is a gyroplane, the limitation on the kinematics and initial state of the unmanned aerial vehicle is small, and thus the obtained path is difficult to meet the motion requirement of the fixed-wing aircraft, and the path needs to be further smoothed, which reduces the practicability of the path.
At present, many intelligent planning algorithms need to be combined with Voronoi diagrams, dubins curves, PH curves and other relevant theories, the theories limit a planning space in a two-dimensional space, although the theories can also be applied to a three-dimensional space through proper modification, the efficiency of the algorithms can be obviously reduced due to the increase of the calculated amount caused by the increase of the complexity, and therefore the algorithms lose the original advantages. Meanwhile, many path planning methods do not consider the handling measures of dynamic obstacles, which is disadvantageous for the flight of multiple drones. Therefore, the prior art regarding the formation trajectory planning of fixed-wing drones has a problem of poor adaptability.
Disclosure of Invention
Therefore, in order to solve the technical problems, it is necessary to provide a method, an apparatus, a computer device and a storage medium for planning formation flight path of fixed-wing uavs based on artificial potential field, which can solve the problem of poor planning effect of formation flight path of fixed-wing uavs.
A method for fixed wing drone formation trajectory planning based on an artificial potential field, the method comprising:
establishing a kinematics model of a single unmanned aerial vehicle according to the kinematics characteristics of the fixed-wing unmanned aerial vehicle; the kinematics model comprises a relation between a pose under an unmanned aerial vehicle inertial system and a speed under a carrier system, and a relation between the speed and an acceleration under the unmanned aerial vehicle carrier system; the relationship between the speed and the acceleration of the unmanned aerial vehicle carrier system comprises a speed change trend matrix; the inertia system is a coordinate system which is static relative to the ground; the vehicle is a coordinate system which is static relative to the unmanned aerial vehicle;
setting a global attraction signal according to a preset target point, setting a local repulsion signal according to a preset barrier, and determining a total guide signal received by the unmanned aerial vehicle under the carrier system according to the global attraction signal and the local repulsion signal; the local rejection signal is a rejection signal received when the unmanned aerial vehicle is located within an action radius of a preset obstacle;
determining a single-machine path planning algorithm of the single fixed-wing unmanned aerial vehicle under the conditions of the known target and the obstacle according to the kinematic model of the single unmanned aerial vehicle, the preset target point and the total guide signal received by the single unmanned aerial vehicle under the preset obstacle;
acquiring formation information of a fixed-wing unmanned aerial vehicle, and determining a target point of a wing plane at a carrier system of a captain plane according to formation parameters in the formation information and current position and posture information of the captain plane;
obtaining external obstacle information, setting internal obstacle information for internal obstacles by wing plane team members, enabling the landlors to fly to formation target areas through the single-machine path planning algorithm according to the external obstacle information, the internal obstacle information and the wing plane target points, and enabling each wing plane to aggregate to the landlors through the single-machine path planning algorithm so as to realize formation aggregate track planning of fixed-wing unmanned aerial vehicle formation.
In one embodiment, the method further comprises the following steps: setting a global attraction signal according to a preset target point, setting a local repulsion signal according to a preset barrier, and determining a total guide signal received by the unmanned aerial vehicle under the carrier system according to the global attraction signal and the local repulsion signal; wherein the local repulsion signal is ∞ when the drone is within a preset action radius of an obstacle.
In one embodiment, the method further comprises the following steps: when the barrier is in the dead ahead of the unmanned aerial vehicle, the calculation formula of the action radius of the preset barrier is as follows:
wherein R is the acting radius of the obstacle, Ro is the maximum radius of the obstacle, and RvTurning radius for unmanned aerial vehicle carrier, Rv＝vl/ω+rvOmega is the maximum angular velocity that the unmanned aerial vehicle can achieve, vlLinear velocity of the unmanned aerial vehicle carrier, rvIs the collision radius of the unmanned aerial vehicle carrier itself.
In one embodiment, the method further comprises the following steps: the speed variation trend matrix is:
wherein the content of the first and second substances,
or
flIs a linear velocity variation trend value, fθIs a pitch angle velocity variation trend value, fψIs a yaw rate trend value,
in order to make the unmanned aerial vehicle under the vehicle receive the guiding signal in the x direction,
the unmanned aerial vehicle under the vehicle receives the guiding signal in the y direction,
the unmanned aerial vehicle under the vehicle system receives the guiding signal in the z direction.
In one embodiment, the method further comprises the following steps: when the line between the preset target point and the preset barrier and the linear velocity of the unmanned aerial vehicle carrier are collinear and the barrier is located between the unmanned aerial vehicle and the target point, an additional guide signal perpendicular to the linear velocity of the unmanned aerial vehicle carrier is set, so that the unmanned aerial vehicle is separated from the local minimum value point.
In one embodiment, the method further comprises the following steps: and numbering the captain plane and the bureaucratic plane team members, and setting the priority of the unmanned aerial vehicle for avoiding obstacles according to the number.
In one embodiment, the method further comprises the following steps: determining the time required for the reduction of the wing plane to the speed of the farm machine, as a function of the difference between the linear speeds of the wing plane and the farm machine and of the acceleration value of the reduction of the wing plane;
the distance required by said wing plane to decelerate to the speed of said long plane is determined as a function of the time required by said wing plane to decelerate to the speed of said long plane;
obtaining the distance between the current position of the wing plane and the target point of the wing plane, and decelerating the wing plane if the distance from the target point of the wing plane is not more than the distance required by the deceleration of the wing plane to the speed of the farm plane.
A fixed wing drone formation track planning device based on an artificial potential field, the device comprising:
the single-machine kinematics model building module is used for building a kinematics model of the single unmanned aerial vehicle according to the kinematics characteristics of the fixed-wing unmanned aerial vehicle; the kinematics model comprises a relation between a pose under an unmanned aerial vehicle inertial system and a speed under a carrier system, and a relation between the speed and an acceleration under the unmanned aerial vehicle carrier system; the relationship between the speed and the acceleration of the unmanned aerial vehicle carrier system comprises a speed change trend matrix; the inertia system is a coordinate system which is static relative to the ground; the vehicle is a coordinate system which is static relative to the unmanned aerial vehicle;
the single-machine path planning algorithm determining module is used for setting a global attraction signal according to a preset target point, setting a local repulsion signal according to a preset barrier, and determining a total guide signal received by the single unmanned aerial vehicle under the carrier system according to the global attraction signal and the local repulsion signal; the local rejection signal is a rejection signal received when the unmanned aerial vehicle is located within an action radius of a preset obstacle; determining a single-machine path planning algorithm of the single fixed-wing unmanned aerial vehicle under the conditions of the known target and the obstacle according to the kinematic model of the single unmanned aerial vehicle, the preset target point and the total guide signal received by the single unmanned aerial vehicle under the preset obstacle;
a bureau plane target point determining module, configured to acquire formation information of the fixed-wing unmanned aerial vehicle, and determine a bureau plane target point of a carrier of a bureau plane according to formation parameters in the formation information and current position and posture information of the bureau plane;
the formation integrated track planning and planning module is used for acquiring external obstacle information, setting internal obstacle information for internal obstacles by wing plane team members, enabling the captain to fly to a formation target area through the single-machine path planning algorithm according to the external obstacle information, the internal obstacle information and the wing plane target point, and enabling each wing plane to integrate to the captain through the single-machine path planning algorithm so as to realize formation integrated track planning of the formation of the fixed-wing unmanned aerial vehicles.
A computer device comprising a memory and a processor, the memory storing a computer program, the processor implementing the following steps when executing the computer program:
establishing a kinematics model of a single unmanned aerial vehicle according to the kinematics characteristics of the fixed-wing unmanned aerial vehicle; the kinematics model comprises a relation between a pose under an unmanned aerial vehicle inertial system and a speed under a carrier system, and a relation between the speed and an acceleration under the unmanned aerial vehicle carrier system; the relationship between the speed and the acceleration of the unmanned aerial vehicle carrier system comprises a speed change trend matrix; the inertia system is a coordinate system which is static relative to the ground; the vehicle is a coordinate system which is static relative to the unmanned aerial vehicle;
setting a global attraction signal according to a preset target point, setting a local repulsion signal according to a preset barrier, and determining a total guide signal received by the unmanned aerial vehicle under the carrier system according to the global attraction signal and the local repulsion signal; the local rejection signal is a rejection signal received when the unmanned aerial vehicle is located within an action radius of a preset obstacle;
determining a single-machine path planning algorithm of the single fixed-wing unmanned aerial vehicle under the conditions of the known target and the obstacle according to the kinematic model of the single unmanned aerial vehicle, the preset target point and the total guide signal received by the single unmanned aerial vehicle under the preset obstacle;
acquiring formation information of a fixed-wing unmanned aerial vehicle, and determining a target point of a wing plane at a carrier system of a captain plane according to formation parameters in the formation information and current position and posture information of the captain plane;
obtaining external obstacle information, setting internal obstacle information for internal obstacles by wing plane team members, enabling the landlors to fly to formation target areas through the single-machine path planning algorithm according to the external obstacle information, the internal obstacle information and the wing plane target points, and enabling each wing plane to aggregate to the landlors through the single-machine path planning algorithm so as to realize formation aggregate track planning of fixed-wing unmanned aerial vehicle formation.
A computer-readable storage medium, on which a computer program is stored which, when executed by a processor, carries out the steps of:
establishing a kinematics model of a single unmanned aerial vehicle according to the kinematics characteristics of the fixed-wing unmanned aerial vehicle; the kinematics model comprises a relation between a pose under an unmanned aerial vehicle inertial system and a speed under a carrier system, and a relation between the speed and an acceleration under the unmanned aerial vehicle carrier system; the relationship between the speed and the acceleration of the unmanned aerial vehicle carrier system comprises a speed change trend matrix; the inertia system is a coordinate system which is static relative to the ground; the vehicle is a coordinate system which is static relative to the unmanned aerial vehicle;
setting a global attraction signal according to a preset target point, setting a local repulsion signal according to a preset barrier, and determining a total guide signal received by the unmanned aerial vehicle under the carrier system according to the global attraction signal and the local repulsion signal; the local rejection signal is a rejection signal received when the unmanned aerial vehicle is located within an action radius of a preset obstacle;
determining a single-machine path planning algorithm of the single fixed-wing unmanned aerial vehicle under the conditions of the known target and the obstacle according to the kinematic model of the single unmanned aerial vehicle, the preset target point and the total guide signal received by the single unmanned aerial vehicle under the preset obstacle;
acquiring formation information of a fixed-wing unmanned aerial vehicle, and determining a target point of a wing plane at a carrier system of a captain plane according to formation parameters in the formation information and current position and posture information of the captain plane;
obtaining external obstacle information, setting internal obstacle information for internal obstacles by wing plane team members, enabling the landlors to fly to formation target areas through the single-machine path planning algorithm according to the external obstacle information, the internal obstacle information and the wing plane target points, and enabling each wing plane to aggregate to the landlors through the single-machine path planning algorithm so as to realize formation aggregate track planning of fixed-wing unmanned aerial vehicle formation.
According to the fixed wing unmanned aerial vehicle formation track planning method, the fixed wing unmanned aerial vehicle formation track planning device, the computer equipment and the storage medium based on the artificial potential field, a kinematics model of a single fixed wing unmanned aerial vehicle is established according to the movement characteristics of a fixed wing aircraft, then based on the artificial potential field method, a potential field function is changed into an attraction signal from a target and a repulsion signal from an obstacle, and a single-machine path planning algorithm of the single unmanned aerial vehicle under the conditions of the known target and the obstacle is provided. On the basis of the structure of "captain-captain", firstly, the captain target points are set according to the formation requirements and captain positions, and each unmanned aerial vehicle is assigned with the attribute of the obstacle, so that the formation integrated track planning is realized. On the basis of realizing the path planning of the single unmanned aerial vehicle, the invention considers the treatment measures of dynamic barriers, sets the cooperative rules among the members of the formation, and finally realizes the formation aggregation of the fixed-wing unmanned aerial vehicles.
Drawings
FIG. 1 is a schematic flow chart of a method for formation flight path planning of fixed-wing drones based on artificial potential fields according to an embodiment;
FIG. 2 is a schematic illustration of an inertial system in one embodiment;
FIG. 3 is a schematic view of a carrier in accordance with one embodiment;
fig. 4 is a schematic view of the determination of a target point of a bureaucratic plane in an embodiment;
FIG. 5 is a schematic illustration of co-linearity of a drone, an obstacle, and a target point in one embodiment;
fig. 6 is a schematic diagram of obstacle avoidance of the unmanned aerial vehicle in one embodiment;
FIG. 7 is a schematic diagram illustrating an embodiment of an obstacle coinciding with a target point;
fig. 8 is a schematic view of the avoidance between wing aircraft in an embodiment;
fig. 9 is a schematic illustration of the arrival of a bureaucratic at a target point in a formation in an embodiment;
FIG. 10 is a block diagram of an apparatus for formation trajectory planning of fixed wing drones based on artificial potential fields according to an embodiment;
FIG. 11 is a diagram illustrating an internal structure of a computer device in one embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
In one embodiment, as shown in fig. 1, there is provided a method for planning formation flight paths of fixed-wing drones based on artificial potential fields, comprising the following steps:
and 102, establishing a kinematics model of the single unmanned aerial vehicle according to the kinematics characteristics of the fixed-wing unmanned aerial vehicle.
The kinematics model comprises a relation between a pose under an unmanned aerial vehicle inertial system and a speed under a carrier system and a relation between the speed and an acceleration under the unmanned aerial vehicle carrier system; the relationship between the speed and the acceleration under the unmanned aerial vehicle carrier system comprises a speed change trend matrix; the inertia system is a coordinate system which is static relative to the ground; the vehicle is a stationary coordinate system relative to the drone.
Specifically, the inertial system is a stationary coordinate system relative to the ground, denoted as Sf(ofxfyfzf):ofAny point on the ground, ofxfIn any direction of the horizontal plane, ofxfyfIs a horizontal plane ofzfThe coordinate system conforms to the right-hand rule for the vertical ground to point to the sky. The inertial system is used in the present invention to represent the orientation of the drone. The research of the invention is positioned at the planning level of the flight path, so that the geometric orientation state matrix P of the unmanned aerial vehicle in the inertial system is defined as follows:
P＝[x y z θ ψ]T (1)
as shown in equation 1 and fig. 2, where x, y, and z are coordinate positions of the drone in the inertial system. Theta, psi is attitude information of the drone in the inertial system. Theta is a pitch angle (-phi/2 is not less than theta and not more than phi/2), psi is a yaw angle (azimuth angle) (-phi is not less than phi), and the rolling angle of the carrier does not need to be defined because the invention operates on a planning level and does not relate to specific details in the motion process of the carrier.
The vehicle is a stationary coordinate system relative to the UAV, and the vehicle is denoted as Sv(ovxvyvzv):ovFor a certain position on the drone at a certain moment, ovxvDirection to the nose of the unmanned aerial vehicle and the direction of flight thereofSame, ovyvPerpendicular to the plane of symmetry of the unmanned aerial vehicle, pointing to the right side of the unmanned aerial vehicle, ovzvPerpendicular ovxvyvThe plane points to the upper side of the unmanned aerial vehicle, and the coordinate system accords with the right-hand rule. The vehicle is used to describe the guiding signal and speed information received by the unmanned aerial vehicle. The motion state of the drone in the vehicle system is described as follows:
[vl vθ vψ al aθ aψ]T (2)
as shown in FIG. 3, wherein vl,vθ,vψRespectively defines the linear velocity, the pitch angle velocity and the yaw angle velocity of the carrier, al,aθ,aψLinear acceleration, pitch angle acceleration and yaw angle acceleration of the vehicle are respectively defined.
The following assumptions are made about the kinematics of a single drone vehicle:
(1) the linear acceleration of the drone is constant, i.e. alIs a constant.
(2) The angular velocity of unmanned aerial vehicle when turning is invariable. I.e. the vehicle can be turned with a constant turning radius immediately when it is desired to turn.
(3) Various speeds of the unmanned aerial vehicle have upper limits, and the linear speed has lower limits
And the lower limit is greater than 0, i.e. the drone cannot hover in place.
Let now the velocity matrix and acceleration matrix be as follows:
the velocity and acceleration relationships are obtained according to the above assumptions as follows:
where V (t) is a speed matrix of the vehicle at time t, V (t ') is a speed matrix of the vehicle at time t ', Δ t is a propulsion step length(s) of time, and t ' + Δ t; is a velocity trend matrix. f is a speed variation trend matrix of the vehicle, and the values thereof will be described in the next summary. From this, the relationship between the position and attitude of the drone and the speed can be obtained as follows:
and 104, setting a global attraction signal according to a preset target point, setting a local repulsion signal according to a preset obstacle, and determining a total guide signal received by the unmanned aerial vehicle under the vehicle system according to the global attraction signal and the local repulsion signal.
The local repulsion signal is the repulsion signal that the unmanned aerial vehicle received when being located within the effect radius of preset barrier.
In particular, the unmanned aerial vehicle researched by the invention is a fixed-wing vehicle and has more limitation on motion characteristics, so that the potential field function in the artificial potential field theory is replaced by a guide signal in the unmanned aerial vehicle, so that the unmanned aerial vehicle can fly according to the motion characteristics of the unmanned aerial vehicle. Wherein the target point can send the attraction signal to unmanned aerial vehicle carrier, attracts its flight towards the target point, and the barrier can send the repulsion signal to unmanned aerial vehicle carrier, drives it and keeps away from the barrier.
In the invention, the action range of the target point on the unmanned aerial vehicle is global, namely the target point can act on the unmanned aerial vehicle carrier in the whole planning process. For the obstacle, the unmanned aerial vehicle adopts an aggressive strategy, namely, the obstacle avoidance cannot be carried out in advance, and an avoidance measure is only adopted at the minimum safe distance, which is called as the acting radius R of the obstacle to the unmanned aerial vehicle (the distance of the unmanned aerial vehicle for implementing the obstacle avoidance action on the obstacle). R needs to be calculated and determined according to a certain rule. In the present invention, a single obstacle is set as oneRadius R0The vehicle cannot enter the sphere, otherwise the vehicle is considered to collide with the obstacle.
And step 106, determining a single-machine path planning algorithm of the single fixed-wing unmanned aerial vehicle under the conditions of the known target and the obstacle according to the kinematic model of the single unmanned aerial vehicle, the preset target point and the total guide signal received by the single unmanned aerial vehicle under the preset obstacle.
And step 108, acquiring formation information of the fixed-wing unmanned aerial vehicle, and determining a carrier-type wing aircraft target point of a wing aircraft at a captain aircraft according to formation parameters in the formation information and current position and attitude information of the captain aircraft.
In the course planning according to the invention, the formation is arranged to consist of a longplane and several bureaucratic planes, the role of the longplane in the formation being that the lead formation flies towards the target area, so that the target point of the longplane is fixed. The ideal position of a wing plane in a formation becomes the target point of the wing plane, which changes with the change of the azimuth state of the long plane. Therefore, the target point of a certain bureaucratic plane needs to be determined by the preset formation parameters and the current position and posture of the longplane.
As shown in fig. 4, taking the figure "people" formation as an example, in fig. 4, the coordinate system of the solid black line is an inertial system, the unmanned vehicle represented by the solid gray line is a grand plane, and the ideal positions of the remaining wing planes, i.e., the target point of the wing plane at this time, are the grand plane at a certain point of the inertial system. The gray dashed coordinates are the carrier system of the long machine. The target point of the francis plane is determined before the planning starts, and the target point of the francis plane needs to be calculated by the formation parameter set before the planning and the vehicle system of the francis plane, so that the number of the francis plane is 0, and the target point of the francis plane at a certain time has the following formula:
wherein the matrix
A target point corresponding to a wing plane with the number j (j ═ 1,2, …, n) in the inertial system. A. thevfAnd BvfFor a coordinate transformation matrix from the carrier system to the inertial system,. phi0And theta0Respectively a yaw angle and a pitch angle of the lower-length machine under the inertial system.
The target point (relative to the target formation position of the leader) of the bureaucratic plane under the vehicle of the leader. [ x ] of0 y0 z0]TIs the position of the lower lengthening machine under the inertial system.
And step 110, acquiring external obstacle information, setting internal obstacle information for internal obstacles by wing plane team members, and enabling the captain to fly to a formation target area through single-machine path planning and integrating each wing plane to the captain through single-machine path planning according to the external obstacle information, the internal obstacle information and wing plane target points to realize formation integrated track planning of fixed-wing unmanned aerial vehicle formation.
Because the assembly of the formation and the maintenance of the formation run through the whole process of a planning algorithm, when the assembly of each unmanned aerial vehicle individual takes a long plane as the center and the formation carries out complex motion in the formation maintenance stage, the unmanned aerial vehicles may have a risk of collision, and therefore obstacle avoidance rules need to be formulated for each unmanned aerial vehicle.
The strategy adopted by the invention is to assign each unmanned aerial vehicle with the attributes of one obstacle, including position and size. And when each unmanned aerial vehicle flies towards the respective target point, the friend aircraft is regarded as a moving obstacle, and the friend aircraft is avoided according to a single-aircraft path planning algorithm.
According to the fixed wing unmanned aerial vehicle formation track planning method based on the artificial potential field, a kinematics model of a single fixed wing unmanned aerial vehicle is established according to the motion characteristics of a fixed wing aircraft, then based on the artificial potential field method, a potential field function is changed into an attraction signal from a target and a repulsion signal from an obstacle, and a single-machine path planning algorithm of the single unmanned aerial vehicle under the conditions of the known target and the obstacle is provided. On the basis of the structure of "captain-captain", firstly, the captain target points are set according to the formation requirements and captain positions, and each unmanned aerial vehicle is assigned with the attribute of the obstacle, so that the formation integrated track planning is realized. On the basis of realizing the path planning of the single unmanned aerial vehicle, the invention considers the treatment measures of dynamic barriers, sets the cooperative rules among the members of the formation, and finally realizes the formation aggregation of the fixed-wing unmanned aerial vehicles.
In one embodiment, the method further comprises the following steps: setting a global attraction signal according to a preset target point, setting a local repulsion signal according to a preset barrier, and determining a total guide signal received by the unmanned aerial vehicle under the carrier system according to the global attraction signal and the local repulsion signal; wherein, when the unmanned aerial vehicle is located within the action radius of the preset obstacle, the local repulsion signal is ∞.
In one embodiment, as shown in fig. 5, the most typical obstacle distribution scenario is considered, that is, when the obstacle is right in front of the drone, the calculation formula of the acting radius of the obstacle is:
wherein R is the acting radius of the obstacle, Ro is the maximum radius of the obstacle, and RvTurning radius for unmanned aerial vehicle carrier, Rv＝vl/ω+rvOmega is the maximum angular velocity that the unmanned aerial vehicle can achieve, vlLinear velocity of the unmanned aerial vehicle carrier, rvIs the collision radius of the unmanned aerial vehicle carrier itself. Under the inertial system, the drone is subjected to an attraction signal from the target point and a repulsion signal from the obstacle as follows:
wherein i is the obstacle number (i ═ 1,2 …), SgIs an attraction signal of a target point, is pointed to the target point by the unmanned aerial vehicle carrier, and has a size of 1, SiThe rejection signal for the unmanned vehicle for the ith obstacle is directed by the obstacle to the unmanned vehicle, whose magnitude is determined by equation 9. Wherein d isiDistance of unmanned plane from ith obstacle, RiThe radius of action of the ith obstacle. Formula 9 shows that the unmanned aerial vehicle carrier will carry out the action of keeping away the barrier with full force and no longer receive the attraction signal influence of target point after entering in barrier effect radius. Then the total pilot signal that unmanned aerial vehicle receives is:
formula 10 obtains a guidance signal S received by the unmanned aerial vehicle in the inertial system, and then S needs to be converted into a guidance signal in the unmanned aerial vehicle carrier system for regulating and controlling the motion state of the unmanned aerial vehicle, and the calculation formula is as follows:
wherein SvFor guiding the unmanned aerial vehicle under the vehicle system, AfvAnd BfvRespectively coordinate transformation matrix from inertial system to carrier systemAnd theta and psi are the pitch angle and yaw angle of the unmanned aerial vehicle under the inertial system respectively. From this, it can be determined that the aforementioned speed trend values are as follows:
flis a linear velocity variation trend value, fθIs a pitch angle velocity variation trend value, fψIs a yaw rate trend value,
in order to make the unmanned aerial vehicle under the vehicle receive the guiding signal in the x direction,
the unmanned aerial vehicle under the vehicle receives the guiding signal in the y direction,
the unmanned aerial vehicle under the vehicle system receives the guiding signal in the z direction.
In one embodiment, the method further comprises the following steps: when a connecting line of the preset target point and the preset barrier is collinear with the linear velocity of the unmanned aerial vehicle carrier and the barrier is located between the unmanned aerial vehicle and the target point, an additional guide signal perpendicular to the linear velocity of the unmanned aerial vehicle carrier is set, so that the unmanned aerial vehicle is separated from a local minimum value point.
The unmanned aerial vehicle carrier can receive the influence of various obstacles in the process of flying to the target point. Meanwhile, the unmanned aerial vehicle is limited by actual conditions, and cannot find a state which can just reach a target point, so that the requirement on the accuracy of the judgment on whether the unmanned aerial vehicle completes the flight path planning cannot be very accurate, a precision range must be set, and the completion of the flight path planning can be judged as long as the unmanned aerial vehicle enters a certain range taking the target point as the center, which is called as an effective range of the target point.
In a scene of obstacle distribution, a connecting line of a target point and an obstacle and the linear velocity of the unmanned aerial vehicle carrier are collinear, which is an extreme case. Unmanned aerial vehicle only can receive the guide signal opposite with speed direction under this kind of circumstances, and unmanned aerial vehicle can not take effectual obstacle avoidance action this moment, need set up a judgement rule, and when this kind of circumstances appeared in unmanned aerial vehicle, the guide signal of a perpendicular to linear velocity was applyed manually, makes unmanned aerial vehicle break away from such scene.
As shown in fig. 6, under the carrier system, with ovxvzvThe plane is taken as an example, at this time, the linear velocity direction of the unmanned aerial vehicle points to the target point, and the obstacle appears between the target point and the vehicle and is collinear with the velocity. After the unmanned aerial vehicle enters the acting radius of the obstacle, the unmanned aerial vehicle will receive a guide signal S as shown in figure 6vAt the moment, the carrier only decelerates continuously but does not turn, so that the carrier cannot avoid obstacles, and when the situation is detected, an additional signal such as S can be applied to the unmanned aerial vehiclevy', the additional signal edge ovzvAxis positive direction, unmanned aerial vehicle carrier can be pulled up in order to avoid the barrier when slowing down this moment. In actual operation, the direction of the additional signal is determined according to the maximum turning capability of the drone at the moment.
In one embodiment, the method further comprises the following steps: and numbering the captain plane and the bureaucratic plane team members, and setting the priority of the unmanned aerial vehicle for avoiding obstacles according to the number.
Specifically, as shown in fig. 7, the solid gray line indicates the position where the persistent plane is located at a certain moment, the dashed gray line indicates the target point corresponding to each bureaucratic plane, it can be seen that the target point of a partial bureaucratic plane coincides with an obstacle, and the partial bureaucratic plane located at the position of the black solid line can obviously not fly to the ideal position (target point). At this moment, according to the stand-alone path planning algorithm, the unmanned aerial vehicle can solve the problem by the strategy of avoiding the obstacle with full force when entering the acting radius of the obstacle. At this time, some bureaucratic planes give up forming a formation to preferentially avoid the obstacle, and fly to the ideal position (target point) after the target point leaves the action range of the obstacle.
As shown in fig. 8, in a certain situation the target point of the left wing-1 is located to the right, while the target point of the right wing-2 is opposite. And two wing planes will all fly on the same plane. In this case, the bureaucratic plane number 1 needs to lead to ovyvPositive direction of axisThe movement is to approach the target point, while the opposite is true for the 2 wing plane. In the process of movement, the distance between two wing machines can be continuously reduced, and when the two machines mutually enter the action range of each other at a certain moment, obstacle avoidance operation can be adopted. Because of the particularity of this scene, the kinematic characteristics of the two wing machines are geometrically symmetrical, and they will repeatedly avoid each other, as shown by the black dashed trajectory in fig. 8, and eventually it is difficult to reach the target point.
In order to avoid the situation, the solution provided by the invention sets the priority for avoiding the obstacles among the members in the formation, namely, in the thought before the planning starts, the members in the formation are numbered, and the obstacle avoiding rules of the unmanned aerial vehicles are regulated according to the principle that the larger the number is, the lower the priority is. According to the rule, the long plane does not need to consider the obstacle avoidance of the wing plane in the flying process, and only needs to consider the influence of obstacles except members of the formation. In the situation shown in fig. 8, a wing plane 1 only needs to pay attention to avoiding a long plane, while a wing plane 2 only needs to avoid a long plane and a wing plane 1. By applying this rule, the movement trajectory of two bureaucratic planes at the stage of entering into obstacle avoidance from each other can be obtained as shown by the grey dashed line in fig. 8.
In one embodiment, the method further comprises the following steps: determining the time required by the wing plane to decelerate to the speed of the wing plane according to the difference between the linear speeds of the wing plane and the long plane and the acceleration value of the wing plane deceleration; the distance required by the wing plane to decelerate to the speed of the farm machine is determined according to the time required by the wing plane to decelerate to the speed of the farm machine; and when the distance between the current position of the wing plane and the target point of the wing plane is not more than the distance required by the deceleration of the wing plane to the speed of the long plane, the wing plane is decelerated.
In the course of the flight of a long plane towards the target point, a wing plane experiences two phases: a formation aggregation stage and a formation holding stage. In the staging phase of the formation, the wing aircraft needs to avoid obstacles and fly towards a target point that varies constantly with the movements of the farm aircraft. In the path planning of a single drone, if the drone reaches the target point, the path planning ends. In a multi-plane formation, however, even if a wing plane arrives at the target point at that time, the track planning continues until a long plane arrives at the target point. Thus, any wing plane arrives at the target point and enters the formation-keeping phase, which needs to maintain as much as possible the same movement as the long plane in order to maintain the current formation.
In the formation staging phase, in order to increase the speed of the formation staging, the wing plane needs to fly at the fastest speed towards the target point, while the farm plane needs to wait at the slowest speed while it has not yet all reached the ideal position in the formation. When all the wing aircraft in the formation enter the formation keeping stage, the long aircraft needs to take the formation to accelerate to fly to the target point, and in the process, the formation may encounter obstacles again, so that the formation of the formation is broken, and the formation gathering and formation keeping processes are repeated. When a wing plane is in place, the line speed difference between the main plane and the wing plane can cause the instability of the formation, and a strategy needs to be arranged to avoid the situation.
As shown in fig. 9, at a certain moment, a bureaucratic machine indicated by a solid black line has reached the target point at that moment along a dashed black line (indicated by a dashed gray line). At this point, however, the farm plane (grey solid line) is waiting at a slow speed, so that the speed of the wing plane is greater than that of the farm plane, resulting in the wing plane flying again outside the effective range of the current target point. Due to the characteristics of the artificial potential field law and the characteristics of the fixed-wing unmanned aerial vehicle model, bureaucratic opportunities take a turn around measure to return to the target point (shown by the gray dashed line locus on the way). This results in instability of the formation, since the wing machines may be in the process of formation marshalling for a long time, while the leader machines are also difficult to judge whether the wing machines marshalling is finished. One idea for solving this problem is to let the wing plane decelerate in advance, just as the long plane can keep the same speed when it reaches the effective range of the target point. This is similar to the "relay" problem.
However, the existence of obstacles and the like make it difficult for the wing plane to accurately predict the flight trajectory and the speed variation law of the long plane, so that the wing plane has difficulty in accurately controlling the speed thereof so as to exactly match the motion state when reaching the target point with the long plane. The solution proposed by the present invention is therefore to let the wing plane estimate the distance from the target point and roughly determine the timing at which the advance deceleration is required, so that as far as possible the state of motion of the wing plane when it reaches the ideal position matches the state of motion of the long plane. The calculation formula of the method is as follows:
Δt＝(vl-v0l)/al (18)
fl＝-1,d≤vΔt-aΔt2/2 (19)
wherein v islAnd v0lLinear velocity of wing-like and long planes at a certain moment, alIs the acceleration. The time Δ t required by the bureaucratic plane to decelerate to the speed of the longeron at that moment can be derived from the equation 18. When d is the distance between the wing plane and the target point, the formula 19 shows that the wing plane must be decelerated when the distance between the wing plane and the target point is not greater than the distance required by the wing plane to decelerate to the linear velocity of the wing plane. The change in the linear speed of the fixed wing is not considered in the process of the reduction of the wing, since the fixed wing waits at the lowest linear speed before all the fixed wings reach the target point.
The estimation rule that the state of the wing plane is adjusted according to the distance between the wing plane and the leader and the linear velocity difference between the wing plane and the leader is set, so that formation aggregation and formation maintenance of the fixed-wing unmanned aerial vehicle can be realized.
It should be understood that, although the steps in the flowchart of fig. 1 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not limited to be performed in the exact order provided for in the present invention, and may be performed in other orders unless explicitly stated. Moreover, at least a portion of the steps in fig. 1 may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performance of the sub-steps or stages is not necessarily sequential, but may be performed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
In one embodiment, as shown in fig. 10, there is provided an artificial potential field-based fixed-wing drone formation flight path planning apparatus, including: a stand-alone kinematics model construction module 1002, a stand-alone path planning algorithm determination module 1004, a wing plane target point determination module 1006, and a formation collective track planning module 1008, wherein:
a single-machine kinematics model building module 1002, configured to build a kinematics model of a single drone according to the kinematics characteristics of the fixed-wing drone; the kinematics model comprises a relation between a pose under an unmanned aerial vehicle inertial system and a speed under a carrier system and a relation between the speed and an acceleration under the unmanned aerial vehicle carrier system; the relationship between the speed and the acceleration under the unmanned aerial vehicle carrier system comprises a speed change trend matrix; the inertia system is a coordinate system which is static relative to the ground; the carrier is a coordinate system which is static relative to the unmanned aerial vehicle;
a single-machine path planning algorithm determining module 1004, configured to set a global attraction signal according to a preset target point, set a local repulsion signal according to a preset obstacle, and determine a total guiding signal received by a single unmanned aerial vehicle under a vehicle system according to the global attraction signal and the local repulsion signal; the local rejection signal is a rejection signal received when the unmanned aerial vehicle is located within the action radius of a preset obstacle; determining a single-machine path planning algorithm of the single fixed-wing unmanned aerial vehicle under the conditions of a known target and a known obstacle according to a kinematic model of the single unmanned aerial vehicle, a preset target point and a total guide signal received by the single unmanned aerial vehicle under the preset obstacle;
a bureau plane target point determining module 1006, configured to acquire formation information of the fixed-wing drone, and determine a bureau plane target point of a carrier of a bureau plane according to a formation parameter in the formation information and current position and posture information of the bureau plane;
the formation integrated track planning and planning module 1008 is used for acquiring external obstacle information, setting internal obstacle information for internal obstacles by wing plane team members, and enabling the fans to fly to a formation target area in a calculation way through a single-machine path plan according to the external obstacle information, the internal obstacle information and a wing plane target point, and enabling each wing plane to be integrated with the fans in a calculation way through the single-machine path plan so as to realize formation integrated track planning of fixed-wing unmanned aerial vehicle formation.
The single-machine path planning algorithm determining module 1004 is further configured to set a global attraction signal according to a preset target point, set a local repulsion signal according to a preset barrier, and determine a total guiding signal received by the single unmanned aerial vehicle under the vehicle system according to the global attraction signal and the local repulsion signal; wherein, when the unmanned aerial vehicle is located within the action radius of the preset obstacle, the local repulsion signal is ∞.
The stand-alone path planning algorithm determination module 1004 is further configured to set the action radius of the obstacle to:
wherein R is the acting radius of the obstacle, Ro is the maximum radius of the obstacle, and RvTurning radius for unmanned aerial vehicle carrier, Rv＝vl/ω+rvOmega is the maximum angular velocity that the unmanned aerial vehicle can achieve, vlLinear velocity of the unmanned aerial vehicle carrier, rvIs the collision radius of the unmanned aerial vehicle carrier itself.
The single-machine kinematics model building module 1002 is further configured to determine a velocity trend matrix as:
wherein the content of the first and second substances,
or
flIs a linear velocity variation trend value, f theta is a pitch angle velocity variation trend value, f psi is a yaw angle velocity variation trend value,
in order to make the unmanned aerial vehicle under the vehicle receive the guiding signal in the x direction,
attaching unmanned aerial vehicle to carrierA pilot signal directed in the y-direction,
the unmanned aerial vehicle under the vehicle system receives the guiding signal in the z direction.
The single-machine path planning algorithm determining module 1004 is further configured to set an additional guiding signal perpendicular to the linear velocity of the unmanned aerial vehicle carrier when a line connecting the preset target point and the preset obstacle is collinear with the linear velocity of the unmanned aerial vehicle carrier and the obstacle is located between the unmanned aerial vehicle and the target point, so that the unmanned aerial vehicle is separated from the local minimum point.
The bureau plane target point determination module 1006 is further configured to number the captain planes and bureaus of bureaus, and set up the priority of unmanned aerial vehicle obstacle avoidance according to the size of the number.
The convoy integrated track planning module 1008 is also adapted to determine the time required for the wing plane to decelerate to the farm machine, in function of the difference between the linear speeds of the wing plane and the farm machine and the acceleration value at which the wing plane decelerates; the distance required by the wing plane to decelerate to the speed of the farm machine is determined according to the time required by the wing plane to decelerate to the speed of the farm machine; and when the distance between the current position of the wing plane and the target point of the wing plane is not more than the distance required by the deceleration of the wing plane to the speed of the long plane, the wing plane is decelerated.
For specific limitations of the device for planning formation flight path of fixed-wing uavs based on artificial potential field, reference may be made to the above limitations of the method for planning formation flight path of fixed-wing uavs based on artificial potential field, which are not described herein again. All modules in the artificial potential field-based fixed wing unmanned aerial vehicle formation track planning device can be completely or partially realized through software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as shown in fig. 11. The computer device includes a processor, a memory, a network interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a method for formation flight path planning for fixed wing drones based on an artificial potential field. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on the shell of the computer equipment, an external keyboard, a touch pad or a mouse and the like.
Those skilled in the art will appreciate that the architecture shown in fig. 11 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In an embodiment, a computer device is provided, comprising a memory storing a computer program and a processor implementing the steps of the above method embodiments when executing the computer program.
In an embodiment, a computer-readable storage medium is provided, on which a computer program is stored, which computer program, when being executed by a processor, carries out the steps of the above-mentioned method embodiments.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A method for planning formation flight paths of fixed-wing unmanned aerial vehicles based on artificial potential fields is characterized by comprising the following steps:
establishing a kinematics model of a single unmanned aerial vehicle according to the kinematics characteristics of the fixed-wing unmanned aerial vehicle; the kinematics model comprises a relation between a pose under an unmanned aerial vehicle inertial system and a speed under a carrier system, and a relation between the speed and an acceleration under the unmanned aerial vehicle carrier system; the relationship between the speed and the acceleration of the unmanned aerial vehicle carrier system comprises a speed change trend matrix; the inertia system is a coordinate system which is static relative to the ground; the vehicle is a coordinate system which is static relative to the unmanned aerial vehicle;
setting a global attraction signal according to a preset target point, setting a local repulsion signal according to a preset barrier, and determining a total guide signal received by the unmanned aerial vehicle under the carrier system according to the global attraction signal and the local repulsion signal; the local rejection signal is a rejection signal received when the unmanned aerial vehicle is located within an action radius of a preset obstacle;
determining a single-machine path planning algorithm of the single fixed-wing unmanned aerial vehicle under the conditions of the known target and the obstacle according to the kinematic model of the single unmanned aerial vehicle, the preset target point and the total guide signal received by the single unmanned aerial vehicle under the preset obstacle;
acquiring formation information of a fixed-wing unmanned aerial vehicle, and determining a target point of a wing plane at a carrier system of a captain plane according to formation parameters in the formation information and current position and posture information of the captain plane;
obtaining external obstacle information, setting internal obstacle information for internal obstacles by wing plane team members, enabling the landlors to fly to formation target areas through the single-machine path planning algorithm according to the external obstacle information, the internal obstacle information and the wing plane target points, and enabling each wing plane to aggregate to the landlors through the single-machine path planning algorithm so as to realize formation aggregate track planning of fixed-wing unmanned aerial vehicle formation.
2. The method of claim 1, wherein setting a global attraction signal according to a predetermined target point, setting a local repulsion signal according to a predetermined obstacle, and determining a total guiding signal received by the unmanned aerial vehicle under the vehicle system according to the global attraction signal and the local repulsion signal comprises:
setting a global attraction signal according to a preset target point, setting a local repulsion signal according to a preset barrier, and determining a total guide signal received by the unmanned aerial vehicle under the carrier system according to the global attraction signal and the local repulsion signal; wherein the local repulsion signal is ∞ when the drone is within a preset action radius of an obstacle.
3. The method according to claim 2, wherein when the obstacle is directly in front of the drone, the preset obstacle radius of action is calculated by:
wherein R is the action radius of the barrier, RoIs the maximum radius of the obstacle, RvTurning radius for unmanned aerial vehicle carrier, Rv＝vl/ω+rvOmega is the maximum angular velocity that the unmanned aerial vehicle can achieve, vlLinear velocity of the unmanned aerial vehicle carrier, rvIs the collision radius of the unmanned aerial vehicle carrier itself.
4. The method of claim 3, wherein the speed trend matrix is:
wherein the content of the first and second substances,
or
flIs a linear velocity variation trend value, fθIs a pitch angle velocity variation trend value, fψIs a yaw rate trend value,
in order to make the unmanned aerial vehicle under the vehicle receive the guiding signal in the x direction,
the unmanned aerial vehicle under the vehicle receives the guiding signal in the y direction,
the unmanned aerial vehicle under the vehicle system receives the guiding signal in the z direction.
5. The method according to claim 4, wherein when setting a global attraction signal according to a predetermined target point, setting a local repulsion signal according to a predetermined obstacle, and determining a total guiding signal received by the unmanned aerial vehicle under the vehicle system according to the global attraction signal and the local repulsion signal, further comprising:
when the line between the preset target point and the preset barrier and the linear velocity of the unmanned aerial vehicle carrier are collinear and the barrier is located between the unmanned aerial vehicle and the target point, an additional guide signal perpendicular to the linear velocity of the unmanned aerial vehicle carrier is set, so that the unmanned aerial vehicle is separated from the local minimum value point.
6. The method as claimed in claim 5, before obtaining formation information of fixed wing drones, and determining the objective point of carrier bureaucratic of a bureaucratic plane according to formation parameters and current position and attitude information of the bureau in the formation information, further comprising:
and numbering the captain plane and the bureaucratic plane team members, and setting the priority of the unmanned aerial vehicle for avoiding obstacles according to the number.
7. The method as claimed in claim 6, characterized in that, when each bureaucratic plane marshals to said elongator through said standalone path planning algorithm, it further comprises:
determining the time required for the reduction of the wing plane to the speed of the farm machine, as a function of the difference between the linear speeds of the wing plane and the farm machine and of the acceleration value of the reduction of the wing plane;
the distance required by said wing plane to decelerate to the speed of said long plane is determined as a function of the time required by said wing plane to decelerate to the speed of said long plane;
obtaining the distance between the current position of the wing plane and the target point of the wing plane, and decelerating the wing plane if the distance from the target point of the wing plane is not more than the distance required by the deceleration of the wing plane to the speed of the farm plane.
8. A fixed wing unmanned aerial vehicle formation track planning device based on artifical potential field, its characterized in that, the device includes:
the single-machine kinematics model building module is used for building a kinematics model of the single unmanned aerial vehicle according to the kinematics characteristics of the fixed-wing unmanned aerial vehicle; the kinematics model comprises a relation between a pose under an unmanned aerial vehicle inertial system and a speed under a carrier system, and a relation between the speed and an acceleration under the unmanned aerial vehicle carrier system; the relationship between the speed and the acceleration of the unmanned aerial vehicle carrier system comprises a speed change trend matrix; the inertia system is a coordinate system which is static relative to the ground; the vehicle is a coordinate system which is static relative to the unmanned aerial vehicle;
the single-machine path planning algorithm determining module is used for setting a global attraction signal according to a preset target point, setting a local repulsion signal according to a preset barrier, and determining a total guide signal received by the single unmanned aerial vehicle under the carrier system according to the global attraction signal and the local repulsion signal; the local rejection signal is a rejection signal received when the unmanned aerial vehicle is located within an action radius of a preset obstacle; determining a single-machine path planning algorithm of the single fixed-wing unmanned aerial vehicle under the conditions of the known target and the obstacle according to the kinematic model of the single unmanned aerial vehicle, the preset target point and the total guide signal received by the single unmanned aerial vehicle under the preset obstacle;
a bureau plane target point determining module, configured to acquire formation information of the fixed-wing unmanned aerial vehicle, and determine a bureau plane target point of a carrier of a bureau plane according to formation parameters in the formation information and current position and posture information of the bureau plane;
the formation integrated track planning and planning module is used for acquiring external obstacle information, setting internal obstacle information for internal obstacles by wing plane team members, enabling the captain to fly to a formation target area through the single-machine path planning algorithm according to the external obstacle information, the internal obstacle information and the wing plane target point, and enabling each wing plane to integrate to the captain through the single-machine path planning algorithm so as to realize formation integrated track planning of the formation of the fixed-wing unmanned aerial vehicles.
9. A computer device comprising a memory and a processor, the memory storing a computer program, wherein the processor implements the steps of the method of any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210006529.9A CN114326816A (en) | 2022-01-04 | 2022-01-04 | Method and device for planning formation flight path of fixed-wing unmanned aerial vehicle based on artificial potential field |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210006529.9A CN114326816A (en) | 2022-01-04 | 2022-01-04 | Method and device for planning formation flight path of fixed-wing unmanned aerial vehicle based on artificial potential field |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114326816A true CN114326816A (en) | 2022-04-12 |
Family
ID=81024031
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210006529.9A Pending CN114326816A (en) | 2022-01-04 | 2022-01-04 | Method and device for planning formation flight path of fixed-wing unmanned aerial vehicle based on artificial potential field |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114326816A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115657725A (en) * | 2022-11-10 | 2023-01-31 | 哈尔滨工业大学 | Primary-secondary unmanned aerial vehicle release decision and path planning integrated method and system |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111506079A (en) * | 2020-05-13 | 2020-08-07 | 浙江大学 | Novel unmanned ship virtual structure formation control method considering obstacle avoidance |
| CN112731942A (en) * | 2020-12-30 | 2021-04-30 | 哈尔滨工程大学 | Multi-AUV formation control method based on improved navigator virtual structure method |
| CN113157000A (en) * | 2021-05-06 | 2021-07-23 | 西北工业大学 | Flight formation cooperative obstacle avoidance self-adaptive control method based on virtual structure and artificial potential field |
-
2022
- 2022-01-04 CN CN202210006529.9A patent/CN114326816A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111506079A (en) * | 2020-05-13 | 2020-08-07 | 浙江大学 | Novel unmanned ship virtual structure formation control method considering obstacle avoidance |
| CN112731942A (en) * | 2020-12-30 | 2021-04-30 | 哈尔滨工程大学 | Multi-AUV formation control method based on improved navigator virtual structure method |
| CN113157000A (en) * | 2021-05-06 | 2021-07-23 | 西北工业大学 | Flight formation cooperative obstacle avoidance self-adaptive control method based on virtual structure and artificial potential field |
Non-Patent Citations (4)
| Title |
|---|
| 吴俊成: "基于诱导航线的多无人机编队飞行控制方法", 北京航空航天大学学报, pages 1518 - 1525 * |
| 周良: "无人机雁行仿生群飞路径规划", 兵工自动化, pages 88 - 91 * |
| 姜阳: "多旋翼无人机集群拓扑保持与协同避障算法设计与验证", 中国优秀硕士学位论文全文数据库工程科技Ⅱ辑, pages 031 - 456 * |
| 程祥: "四轴飞行器多机协作控制系统研究", 中国优秀硕士学位论文全文数据库工程科技Ⅱ辑, pages 031 - 256 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115657725A (en) * | 2022-11-10 | 2023-01-31 | 哈尔滨工业大学 | Primary-secondary unmanned aerial vehicle release decision and path planning integrated method and system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108549407B (en) | Control algorithm for multi-unmanned aerial vehicle cooperative formation obstacle avoidance | |
| CN112068598B (en) | Unmanned aerial vehicle formation flying method and control system | |
| Saska et al. | Motion planning and control of formations of micro aerial vehicles | |
| Park et al. | Collision avoidance for quadrotor using stereo vision depth maps | |
| CN110825113A (en) | Formation keeping method suitable for quad-rotor unmanned aerial vehicle cluster flight | |
| Zhao et al. | Cooperative path following control of fixed-wing unmanned aerial vehicles with collision avoidance | |
| Mills et al. | Vision based control for fixed wing UAVs inspecting locally linear infrastructure using skid-to-turn maneuvers | |
| Gautam et al. | Application of guidance laws to quadrotor landing | |
| CN111880574A (en) | Unmanned aerial vehicle collision avoidance method and system | |
| CN115712308A (en) | Multi-machine formation anti-collision method based on distributed model prediction control and formation control | |
| CN114326816A (en) | Method and device for planning formation flight path of fixed-wing unmanned aerial vehicle based on artificial potential field | |
| Bodi et al. | Reinforcement learning based UAV formation control in GPS-denied environment | |
| Harbick et al. | Planar spline trajectory following for an autonomous helicopter | |
| Larin et al. | TWO-STAGE PROCEDURE OF H∞-PARAMETERIZATION OF STABILIZING CONTROLLERS APPLIED TO QUADROTOR FLIGHT CONTROL. | |
| CN112258896A (en) | Unmanned aerial vehicle fusion airspace operation method based on flight path | |
| Ruangwiset | Path generation for ground target tracking of airplane-typed UAV | |
| Iovino et al. | Implementation of a distributed flocking algorithm with obstacle avoidance capability for UAV swarming | |
| CN113031643B (en) | Unmanned aerial vehicle formation and obstacle avoidance method | |
| CN115525063A (en) | Trajectory planning method for unmanned helicopter | |
| Johnson et al. | Flight testing of nap of-the-earth unmanned helicopter systems | |
| El-Kalubi et al. | Vision-based real time guidance of UAV | |
| Kumaresan et al. | Application of ℒ1 Adaptive Controller for the Design of a Novel Decentralized Leader Follower Formation Algorithm | |
| Xie et al. | Dynamic output feedback image-based visual servoing of rotorcraft UAVs | |
| Le Bras et al. | Image-based visual servo control for circular trajectories for a fixed-wing aircraft | |
| Narkiewicz et al. | Optimal selection of UAV for ground target tracking |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |