CN114428516B - Unmanned aerial vehicle cluster obstacle avoidance method - Google Patents
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Abstract
The invention provides an unmanned aerial vehicle cluster obstacle avoidance method, which comprises the following steps: s1, selecting a long machine from an unmanned aerial vehicle cluster, and taking other unmanned aerial vehicles as a plane; s2, the long machine identifies the initial formation of the current unmanned aerial vehicle cluster by acquiring the position information of the plane; s3, the long machine and the assistant machine respectively sense the front obstacle information, and the assistant machine feeds back the obstacle information to the long machine; s4, the long aircraft redetermines the new formation position of the unmanned aerial vehicle cluster according to the obstacle information, updates the target position of each unmanned aerial vehicle in the cluster at the next moment according to the new formation position, and sends the target position at the next moment to the auxiliary aircraft; s5, performing formation transformation on the unmanned aerial vehicle cluster according to the target position at the next moment; s6, judging whether the unmanned aerial vehicle cluster passes through the obstacle, and S7, judging whether the current moment is the last moment. The invention completes cluster obstacle avoidance by means of formation transformation, and has small calculated amount, high response speed and stronger environmental adaptability.
Description
Technical Field
The invention relates to the technical field of unmanned aerial vehicles, in particular to an unmanned aerial vehicle cluster obstacle avoidance method.
Background
With the continuous development of the unmanned aerial vehicle industry, unmanned aerial vehicles are well applied in the field of army and civilian. However, a single individual is limited by own visual field, load, endurance and the like, so that large-area operation, reconnaissance and the like are difficult to complete, and the unmanned aerial vehicle cluster system becomes a necessary trend of development. The cluster system realizes cluster group behaviors by means of local information interaction among unmanned aerial vehicles, for example: the unmanned aerial vehicles in the clusters have the advantages of destruction resistance, cooperativity and expansibility due to the complementary effect among the unmanned aerial vehicles in the clusters, and play a great role in practical application.
In the existing clustering method, the complexity and the variability of the working environment and the large size of the clusters make the cluster obstacle avoidance one of the key problems unavoidable. Meanwhile, in the process of executing tasks, the clusters are often subjected to formation operation in different shapes, so that the cluster obstacle avoidance is performed in a formation conversion mode, and the cluster operation is more convenient, practical and high in efficiency. The invention provides a cluster obstacle avoidance method based on formation transformation.
There are also many related solutions to the above-mentioned needs.
Compared with the unmanned aerial vehicle formation obstacle avoidance method and system under the time-varying network topology, which are proposed by the patent CN107491086B, the overall formation transformation is carried out after the non-obstacle area is calculated by adopting the global formation information, and the characteristics of formation expansion and contraction are only utilized, so that the obstacle of continuous irregular arrangement is not avoided.
The current cluster obstacle avoidance methods are mainly divided into two types, one type is an obstacle avoidance method based on path planning, and the method mainly comprises path planning based on a rapid expansion random tree, path planning based on a particle swarm and the like. The method has long updating time and large operation amount. Another type of method is an emergency obstacle avoidance method based on a reaction type, which mainly comprises the following steps: and an obstacle avoidance algorithm based on potential field theory, speed obstacle theory and the like is researched. The method has high response speed, but has larger influence on clustered individuals, and sometimes causes the phenomenon that part of individuals are separated from clusters.
Disclosure of Invention
In view of the above problems, the present invention aims to provide an unmanned aerial vehicle cluster obstacle avoidance method. By researching the formation characteristics of the multi-unmanned aerial vehicle cluster, the multi-unmanned aerial vehicle can finish the cluster obstacle avoidance through formation transformation, the cluster mobility and instantaneity are improved, the environmental adaptability is higher, and the multi-unmanned aerial vehicle cluster is more beneficial to being applied to real task scenes.
In order to achieve the above purpose, the present invention adopts the following specific technical scheme:
the invention provides an unmanned aerial vehicle cluster obstacle avoidance method, which comprises the following steps:
S1, selecting a long machine from an unmanned aerial vehicle cluster, and taking other unmanned aerial vehicles as a plane;
S2, the long machine identifies the initial formation of the current unmanned aerial vehicle cluster by acquiring the position information of the plane;
s3, the long machine and the assistant machine respectively sense the front obstacle information, and the assistant machine feeds back the obstacle information to the long machine;
S4, the long aircraft redetermines the new formation position of the unmanned aerial vehicle cluster according to the obstacle information, updates the target position of each unmanned aerial vehicle in the cluster at the next moment according to the new formation position, and sends the target position at the next moment to the auxiliary aircraft;
S5, performing formation transformation on the unmanned aerial vehicle cluster according to the target position at the next moment;
S6, judging whether the unmanned aerial vehicle clusters pass through the obstacle or not:
If the obstacle passes, the unmanned plane cluster restores the initial formation, and the step S2 is returned;
If the obstacle does not pass, returning to the step S5;
S7, judging whether the current moment is the last moment or not:
If the current moment is the last moment, the operation is ended; otherwise, returning to the step S2.
Preferably, in step S2, the long machine is selected through an inheritance algorithm, and a backup long machine is set, and if the backup long machine does not receive heartbeat packet data of the long machine within a preset time, the backup long machine inherits the position of the long machine, and a new backup long machine is set.
Preferably, in step S3:
Acquiring the current position of the long machine through a sensor And speed/>：
The position of each bureau at the current moment is obtained by a sensorAnd speed/>：
Wherein,And/>Acceleration of the plane and acceleration of the plane respectively.
Preferably, in step S4:
The long machine updates the target position of the next moment of the long machine to be according to the new formation position information ；
The long machine updates the target position of the next moment of the plane as the following moment according to the position information of the new formation。
Preferably, in step S4:
when the distance between the obstacle and the long plane and the auxiliary plane is respectively smaller than the collision standard distance, the long plane and the auxiliary plane start to execute evading actions;
The collision standard distance is: ，
Wherein R 0 is collision avoidance distance, R is unmanned plane safety area, P (t) is current position of unmanned plane, and P m is central position of obstacle.
Preferably, in step S4:
When a non-identical obstacle exists in front of each unmanned aerial vehicle and the distance d m between the obstacles is more than 2 times of the wheelbase of the unmanned aerial vehicle, adopting an obstacle avoidance strategy of a rotation method; otherwise, adopting an obstacle avoidance strategy of a character method to perform formation transformation.
Preferably, the obstacle avoidance strategy of the rotation method is as follows: rotating the unmanned aerial vehicle by a preset angle to avoid obstacles;
The preset angle θ is:
Wherein N is the number of unmanned aerial vehicles.
Preferably, the obstacle avoidance strategy of the in-line method is as follows: firstly, selecting an unmanned plane path without an obstacle in front of an unmanned plane, and selecting the position with the maximum distance between the obstacles or advancing along the outer contour of the obstacles if the unmanned plane path does not exist; if the number of the unmanned aerial vehicles in front is greater than or equal to two, the unmanned aerial vehicle with the front obstacle can select the unmanned aerial vehicle path in front nearby.
Preferably, step S5 comprises the sub-steps of:
S501, calculating the speed and azimuth angle of the unmanned aerial vehicle cluster at the next moment;
After being converted into a machine body coordinate system from an inertial coordinate system, the position error of the plane is as follows:
wherein, psi j is the current azimuth angle of the plane;
The limiting conditions are as follows: d is the curved grain diameter difference;
；/> set by combining long machine with unmanned plane formation,/> The conversion included angle between the machine body coordinate system and the Frenet-Serset coordinate system is set;
The speed of the next moment of the long machine is as follows:
wherein, K ip、Kid is the proportional control gain and the differential control gain of the long machine speed respectively;
The speed of the bureau at the next moment is:
wherein, K jp、Kjd is the proportional control gain and differential control gain of the speed of the plane;
Constraint conditions of the yaw direction of the long machine are as follows: ；
the constraint conditions of the yaw direction of the wing plane are as follows: ；
wherein, psi t is the included angle between the tangential direction of the final target point on the target path and the x-axis of the inertial coordinate system; psi i is the current azimuth of the long machine;
S502, performing formation transformation on the unmanned aerial vehicle cluster according to the speed and the azimuth angle of the next moment;
s503, the long machine acquires the position information of the plane again to identify the formation of the current unmanned plane cluster and judge whether the formation transformation is completed; if the formation transformation is completed, entering step S6; if the formation transformation is not completed, the process returns to step S501.
Compared with the prior art, the invention has the following advantages:
1) The long-plane relay control algorithm is established by adopting an algorithm designed by the optimized long-plane model, namely, the cluster is not completely paralyzed until the last unmanned aerial vehicle in the cluster is damaged, and the characteristic of poor robustness of the traditional long-plane model is effectively avoided.
2) The method completes cluster obstacle avoidance in a formation transformation mode, has small calculated amount, high response speed and stronger environmental adaptability, and is more beneficial to being applied to a real task scene.
Drawings
Fig. 1 is a flowchart of an unmanned aerial vehicle cluster obstacle avoidance method according to an embodiment of the present invention.
Fig. 2 is a flowchart of an unmanned aerial vehicle cluster obstacle avoidance method according to an embodiment of the present invention.
Fig. 3 is a schematic diagram of an in-line obstacle avoidance method of an unmanned aerial vehicle cluster obstacle avoidance method according to an embodiment of the invention.
Fig. 4 is a schematic diagram of a rotating method obstacle avoidance method of an unmanned aerial vehicle cluster obstacle avoidance method according to an embodiment of the present invention.
Fig. 5 is a schematic diagram of yaw constraint of the unmanned aerial vehicle cluster obstacle avoidance method according to an embodiment of the present invention.
Fig. 6 is a long-range yaw constraint schematic diagram of an unmanned aerial vehicle cluster obstacle avoidance method according to an embodiment of the present invention.
Detailed Description
Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. In the following description, like modules are denoted by like reference numerals. In the case of the same reference numerals, their names and functions are also the same. Therefore, a detailed description thereof will not be repeated.
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be further described in detail with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not to be construed as limiting the invention.
Fig. 1 shows a flowchart of an unmanned aerial vehicle cluster obstacle avoidance method according to an embodiment of the present invention.
Fig. 2 shows a flowchart of an unmanned aerial vehicle cluster obstacle avoidance method according to an embodiment of the present invention.
As shown in fig. 1 and fig. 2, the unmanned aerial vehicle cluster obstacle avoidance method provided by the embodiment of the invention includes the following steps:
S0, acquiring parameters of the unmanned aerial vehicle clusters and the obstacles.
Setting the number N= { 3,4, 5..N } of unmanned aerial vehicles in the cluster, wherein the target aggregation point position coordinate is P t（Xt,Yt), ignoring the altitude coordinate, and setting the current position of the long machine i as P i（Xi,Yi), the current speed and the azimuth angle as V i and psi i respectively in a two-dimensional space; the current position P j（Xj,Yj of the plane j), the current speed and azimuth are V j and ψ j, respectively.
The number of obstacles m= {1, 2..m } is randomly generated in the two-dimensional space, each obstacle is a center coordinate P m（Xm,Ym), and a circular obstacle with a radius r.
S1, selecting a long machine from the unmanned aerial vehicle cluster through an inheritance algorithm, and taking other unmanned aerial vehicles as the plane.
And selecting a unique long machine from the clusters by adopting an inheritance algorithm, and setting a backup long machine. And when the backup long machine does not receive heartbeat packet data of the long machine within a certain time, the backup long machine formally inherits the positions of the long machine, and a new backup long machine is set.
The long machine combines the number of the current unmanned aerial vehicles to design an initial formation, and the positions of the adjacent unmanned aerial vehicles are utilized to control the distance of the vertical curve movement direction under the machine body coordinate system, so that the target curved line distanceThe distance of each unmanned aerial vehicle along the curve motion direction is controlled, and then the formation transformation of different patterns is realized.
S2, the long plane acquires the position information of the plane to identify the formation of the current unmanned plane cluster.
The unmanned aerial vehicle cluster advances according to a preset formation, and the speed and the position of the unmanned aerial vehicle cluster are updated according to the speed and the position designated by the unmanned aerial vehicle cluster. The plane moves towards the target position, and the plane follows the plane to move.
The position and the speed of the long machine at the current moment are obtained through a sensor:
The position and the speed of each plane at the current moment are acquired through a sensor:
Wherein, And/>Acceleration of the plane and acceleration of the plane respectively.
In practical application, the speed and the position of each unmanned aerial vehicle at the current moment are fed back and acquired by a sensor.
S3, the unmanned aerial vehicle clusters sense the front obstacle information, and the plane feeds the obstacle information back to the long plane.
Let the unmanned plane scanning range be the sector area of the aircraft nose direction 2α max, and α max be set to be related to the collision avoidance distance. When the obstacle is in the visible range, the unmanned aerial vehicle detecting the obstacle locates the position of the obstacle and sends the position of the obstacle to the long machine.
Judging collision standard: wherein R 0 is collision avoidance distance, R is unmanned plane safety area, P (t) represents unmanned plane current position, P m represents obstacle center position, and avoidance action is started when the trigger standard is reached.
S4, the long aircraft redetermines the new formation position of the unmanned aerial vehicle cluster according to the obstacle information, updates the target position of each unmanned aerial vehicle in the cluster at the next moment according to the new formation position, and sends the target position at the next moment to the auxiliary aircraft.
And the long machine updates the target position of each unmanned aerial vehicle in the cluster at the next moment according to the new formation position.
Updating the target position of the long machine to be according to the acquired new formation position informationThe target position of each wing is/>。
When a non-identical obstacle exists in front of each unmanned aerial vehicle and the distance d m between the obstacles is more than 2 times of the shaft distance of the unmanned aerial vehicle, an obstacle avoidance strategy of a rotation method is adopted; otherwise, performing formation transformation by adopting a character method;
the mark positions of the formation transformation method are as follows:
Fig. 3 shows a schematic diagram of an in-line obstacle avoidance method of an unmanned aerial vehicle cluster obstacle avoidance method according to an embodiment of the invention.
Fig. 4 shows a schematic diagram of a rotating method obstacle avoidance method of an unmanned aerial vehicle cluster obstacle avoidance method according to an embodiment of the invention.
As shown in figures 3 and 4 of the drawings,
Unmanned aerial vehicle rotation angle of rotation method is decided by unmanned aerial vehicle cluster's quantity:
The unmanned aerial vehicle formation transformation strategy of the character method is as follows: selecting an unmanned plane path without an obstacle in front of the unmanned plane preferentially, and selecting the position with the maximum distance between the obstacles or advancing along the outer contour of the obstacles if the unmanned plane path does not exist; if the number of the unmanned aerial vehicles in front is greater than or equal to two, the unmanned aerial vehicle with the front obstacle can select the unmanned aerial vehicle path in front nearby.
S5, performing formation transformation on the unmanned aerial vehicle cluster according to the target position at the next moment.
Step S5 comprises the following sub-steps:
s501, calculating the speed and yaw direction constraint conditions of the unmanned aerial vehicle cluster at the next moment.
After being converted into a machine body coordinate system from an inertial coordinate system, the position error of the plane is as follows:
wherein, the limiting conditions are: ，
d is the curved path difference, and d is determined according to the current formation;
；/> The long machine is combined with the unmanned aerial vehicle formation to set;
The conversion included angle between the machine body coordinate system and the Frenet-Serset coordinate system.
The speed of the next moment of the long machine is as follows:
Wherein, K ip、Kid is the proportional control gain and the differential control gain of the long machine speed respectively.
The speed of the bureau at the next moment is:
wherein, K jp、Kjd is the ratio control gain and differential control gain of the plane speed.
Wherein, the long engine speed proportional control gain K ip and the differential control gain K id are respectively 0.28 and 0.05, and the plane engine speed proportional control gain K jp and the differential control gain K jd are respectively 0.35 and 0.08.
The machine head directions follow the machine head direction of the long machine, and the machine head of the long machine always faces the final target point direction.
Fig. 5 shows a schematic diagram of yaw constraint of the unmanned aerial vehicle cluster obstacle avoidance method according to an embodiment of the present invention.
Fig. 6 shows a long-range yaw constraint schematic diagram of an unmanned aerial vehicle cluster obstacle avoidance method according to an embodiment of the invention.
As shown in fig. 5 and 6:
Constraint conditions of the yaw direction of the long machine are as follows: ；
the constraint conditions of the yaw direction of the wing plane are as follows: ；
wherein, ψ t is the angle between the tangential direction of the final target point on the target path and the x-axis of the inertial coordinate system.
S502, performing formation transformation on the unmanned aerial vehicle cluster according to the speed and the azimuth angle of the next moment.
S503, the long machine acquires the position information of the plane again to identify the formation of the current unmanned plane cluster and judge whether the formation transformation is completed. If the formation transformation is completed, entering step S6; if the formation transformation is not completed, the process returns to step S501.
And outputting and storing formation information of the unmanned aerial vehicle cluster at the next moment.
And confirming the formation shape of the current unmanned aerial vehicle cluster according to the position information fed back by the assistant machine, and outputting and storing the formation shape of the current unmanned aerial vehicle cluster. If the formation transformation is completed, the flag position is "1", otherwise the flag position is "0".
。
S6, judging whether the clusters pass through the obstacle.
Judging whether the clusters pass through the obstacle or not: if the obstacle is passed, the mark position is 1, the original formation is restored, and the step S7 is carried out; otherwise, the flag position is "0", and the process returns to step S5.
。
When the three-dimensional space is expanded, the height information can be utilized, and the cluster obstacle avoidance in the three-dimensional space can be realized through formation layering transformation in the height direction.
S7, judging whether the current time is the last time.
If the current time is the last time, the operation is ended; otherwise, returning to the step S2.
The method is not only limited to the use of unmanned aerial vehicle clusters, but also has reference significance for underwater clusters, unmanned aerial vehicle clusters and unmanned ship clusters.
While embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the invention, and that variations, modifications, alternatives and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the invention.
The above embodiments of the present invention do not limit the scope of the present invention. Any of various other corresponding changes and modifications made according to the technical idea of the present invention should be included in the scope of the claims of the present invention.
Claims (5)
1. The unmanned aerial vehicle cluster obstacle avoidance method is characterized by comprising the following steps of:
S1, selecting a long machine from an unmanned aerial vehicle cluster, and taking other unmanned aerial vehicles as a plane;
S2, the long machine identifies the initial formation of the current unmanned aerial vehicle cluster by acquiring the position information of the plane;
S3, the long machine and the assistant machine respectively sense the front obstacle information, and the assistant machine feeds back the obstacle information to the long machine;
S4, the long machine redetermines a new formation position of the unmanned aerial vehicle cluster according to the obstacle information, updates a target position of each unmanned aerial vehicle at the next moment in the cluster according to the new formation position, and sends the target position at the next moment to the auxiliary plane; when a non-identical obstacle exists in front of each unmanned aerial vehicle and the distance d m between the obstacles is more than 2 times of the wheelbase of the unmanned aerial vehicle, adopting an obstacle avoidance strategy of a rotation method; otherwise, performing formation transformation by adopting an obstacle avoidance strategy of a character method, wherein the obstacle avoidance strategy of the rotation method is as follows: rotating the unmanned aerial vehicle by a preset angle to avoid obstacles;
The preset angle theta is as follows:
；
Wherein N is the number of unmanned aerial vehicles;
the obstacle avoidance strategy of the in-line method is as follows: firstly, selecting an unmanned plane path without an obstacle in front of an unmanned plane, and selecting the position with the maximum distance between the obstacles or advancing along the outer contour of the obstacle if the unmanned plane path does not exist; if the number of the unmanned aerial vehicles in front is greater than or equal to 2, the unmanned aerial vehicle with the front obstacle can select a path of the unmanned aerial vehicle with the front obstacle nearby;
S5, the unmanned aerial vehicle cluster performs formation transformation according to the target position at the next moment;
Step S5 comprises the following sub-steps:
S501, calculating the speed and azimuth angle of the unmanned aerial vehicle cluster at the next moment; after being converted into a machine body coordinate system from an inertial coordinate system, the position error of the plane is as follows:
；
wherein, psi j is the current azimuth angle of the plane;
The limiting conditions are as follows: d is the curved grain diameter difference;
；/> set by combining long machine with unmanned plane formation,/> The conversion included angle between the machine body coordinate system and the Frenet-Serset coordinate system is set;
The speed of the next moment of the long machine is as follows:
Wherein, K ip、Kid is the proportional control gain and the differential control gain of the long machine speed respectively;
The speed of the wing plane at the next moment is as follows:
Wherein K jp、Kjd is the proportional control gain and the differential control gain of the speed of the plane;
Constraint conditions of the yaw direction of the long machine are as follows: ；
the constraint conditions of the yaw direction of the plane are as follows: ；
Wherein, psi t is the included angle between the tangential direction of the final target point on the target path and the x-axis of the inertial coordinate system; psi i is the current azimuth of the long machine;
S502, the unmanned aerial vehicle cluster performs formation transformation according to the speed and the azimuth angle of the next moment;
S503, the long machine acquires the position information of the plane again to identify the formation of the current unmanned plane cluster and judge whether the formation transformation is completed or not; if the formation transformation is completed, entering step S6; if the formation transformation is not completed, returning to step S501;
S6, judging whether the unmanned aerial vehicle cluster passes through an obstacle or not:
if the unmanned aerial vehicle cluster is in the obstacle, the unmanned aerial vehicle cluster is in the initial formation, and the step S2 is returned;
if the obstacle does not pass, returning to the step S5;
S7, judging whether the current moment is the last moment or not:
If the current moment is the last moment, the operation is ended; otherwise, returning to the step S2.
2. The unmanned aerial vehicle cluster obstacle avoidance method according to claim 1, wherein in the step S2, the long machine is selected through an inheritance algorithm, a backup long machine is set, and if the backup long machine does not receive heartbeat packet data of the long machine within a preset time, the backup long machine inherits a long machine position, and a new backup long machine is set.
3. The unmanned aerial vehicle cluster obstacle avoidance method of claim 2, wherein in step S3:
Acquiring the current position of the long machine through a sensor And speed/>：
The position of each bureau at the current moment is obtained through a sensorAnd speed/>：
Wherein,And/>The acceleration of the long machine and the acceleration of the wing machine are respectively.
4. A method of unmanned aerial vehicle cluster obstacle avoidance as claimed in claim 3, wherein in step S4:
The long machine updates the target position of the next moment of the long machine to be according to the new formation position information ；
The leader updates the target position of the wing plane at the next moment to be according to the new formation position information。
5. The unmanned aerial vehicle cluster obstacle avoidance method of claim 4, wherein in step S4:
When the distance between the obstacle and the long machine and the distance between the obstacle and the auxiliary machine are respectively smaller than the collision standard distance, the long machine and the auxiliary machine start to execute avoiding actions;
The collision standard distance is as follows: ，
Wherein R 0 is collision avoidance distance, R is unmanned plane safety area, P (t) is current position of unmanned plane, and P m is central position of obstacle.
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