CN-115951719-B - Unmanned aerial vehicle cluster ground task collaborative planning method

CN115951719BCN 115951719 BCN115951719 BCN 115951719BCN-115951719-B

Abstract

The application discloses a collaborative planning method for unmanned aerial vehicle clusters to ground tasks, which comprises the steps of S1, randomly distributing unmanned aerial vehicle clusters in the upper air within a set area range, carrying out situation assessment on the relative states of unmanned aerial vehicles and ground targets, S2, carrying out target distribution on clusters formed by m unmanned aerial vehicles and scenes of n ground targets according to a task income calculation method, S3, planning flight tracks from each unmanned aerial vehicle to the targets by adopting Dubin path planning rules based on situation assessment results and target distribution schemes, and S4, carrying out re-planning on threat paths by considering obstacle threat conditions. The method solves the problems of how to evaluate situation, distribute corresponding tasks and plan the shortest path when the unmanned aerial vehicle executes the ground task.

Inventors

ZHAO YANLI
LIU XINGYU
ZHEN ZIYANG
DU JIAWEI
LUO JIA
CUI CHEN
GUO JIN
WANG JUN

Assignees

中国人民解放军63891部队
南京航空航天大学

Dates

Publication Date: 20260505
Application Date: 20220829

Claims (5)

1. The unmanned aerial vehicle cluster ground task collaborative planning method is characterized by comprising the following steps of: S1, randomly distributing unmanned aerial vehicle clusters in the upper space of a set area range, and carrying out situation assessment on the relative states of unmanned aerial vehicles and ground targets; S2, performing target distribution on a cluster formed by m unmanned aerial vehicles and scenes of n ground targets according to a task income calculation method; S3, based on situation assessment results and a target allocation scheme, drawing out flight tracks from each unmanned aerial vehicle to a target by adopting Dubin path planning rules, wherein the S3 specifically comprises: s31, dividing a path into an arc with a fixed curvature and a straight line under the condition of not considering an obstacle, wherein C represents an arc section, and S represents a straight line section; s32 determining a start circle and a stop circle from the position and speed information of the start point and the end point 、、、 Determining a starting circle And end point circle Center coordinates of (2) 、 ; S33 determining an entry point of the Dubin path And a cut-out point ; S34, acquiring the total path and the length of the total path, namely connection Obtaining a straight line segment of the Dubin path, and selecting two segments of minor arcs to connect And Obtaining two arc sections, and adding the lengths of the three sections to obtain a total path and the length of the total path; S4, considering the obstacle threat situation, and re-planning the threatened path.
2. The unmanned aerial vehicle cluster-to-ground task collaborative planning method according to claim 1, wherein the S1 is specifically: s11, randomly distributing unmanned aerial vehicle clusters in the upper space of a set area range, and setting the quantity and position information of the known ground target areas of an unmanned aerial vehicle command system; S12, obtaining a comprehensive advantage index based on the angle advantage index, the speed advantage index and the distance advantage index, and establishing a situation advantage model of the unmanned aerial vehicle on a ground target, wherein the situation advantage model is the first in the cluster Ground alignment of unmanned aerial vehicle Situational dominance model of individual task targets Linear weighting based on importance for various dominance indexes: (1) In the formula, Evaluating a weight vector for dominance, wherein 、 And (d) sum Respectively the angle, speed and distance efficiency dominance index weight, S 、S 、S The angle, the speed and the distance efficiency dominance indexes respectively are satisfied ; S13, evaluating situation advantages of the estimated unmanned aerial vehicle on the ground target by adopting an angle advantage index, a speed advantage index and a distance advantage index; The angle dominance index is: (2) In the formula, The azimuth angle of the unmanned aerial vehicle to the ground target is the smaller the included angle between the speed direction of the unmanned aerial vehicle and the double-vehicle target line is, the greater the angle advantage is; the speed dominance index is: (3) In the formula, And The speeds of the unmanned aerial vehicle and the ground target are respectively, and the greater the speed of the unmanned aerial vehicle relative to the ground target is, the greater the speed advantage is; the distance dominance index is related to the distance between the unmanned aerial vehicle and the target, the maximum range of the guided missile carried by the unmanned aerial vehicle and the maximum detection distance of the airborne radar, and is designed as follows: (4) In the formula, For the distance between the unmanned aerial vehicle and the ground target distance, Is the maximum range of the guided missile carried by the unmanned aerial vehicle, For the maximum detection distance of the unmanned aerial vehicle airborne radar, the closer the unmanned aerial vehicle is to a ground target, the greater the distance advantage.
3. The unmanned aerial vehicle cluster-to-ground task collaborative planning method according to claim 1, wherein the S2 is specifically: S21 is directed to Cluster formed by unmanned aerial vehicle The scene of the individual ground targets is subject to target allocation, Calculating autonomous priority, and determining a dominance assessment matrix between the unmanned aerial vehicle and the target Obtaining the ground target of the unmanned aerial vehicle cluster through advantage evaluation The dimensional dominance assessment matrix is as follows: (5) In the formula, Is the first Line (1) Column elements, the size of which represents the drone To ground targets Is a comprehensive advantage of (a); s22 is a task profit matrix Unmanned aerial vehicle in design cluster To ground targets The task profit calculation method of (1) comprises the following steps: (6) In the formula, And Respectively, are ground targets The survival probability and value of (2); As the weight coefficient of the light-emitting diode, The larger the unmanned aerial vehicle, the more the unmanned aerial vehicle tends to preserve the greatest advantage of the unmanned aerial vehicle The smaller the unmanned aerial vehicle, the more the unmanned aerial vehicle tends to ensure that the task completion benefit is maximized; S23, calculating benefit evaluation indexes of unmanned aerial vehicles in the cluster, and enabling unmanned aerial vehicles to perform Benefit evaluation index of (a) The definition is as follows: (7) In the formula, Representation unmanned aerial vehicle Maximum benefit value to ground target; S24, benefit evaluation index of each unmanned aerial vehicle in cluster The order of the materials is put into a descending order, Obtaining ordered vectors ; S25 ranking based Sequentially record Middle element Corresponding unmanned aerial vehicle Sequence number of (2) In the following Matrix No Selecting the closest of the columns Elements of (2) Record line number Targets are to Assigned to unmanned aerial vehicle ; S26, after being distributed, the distributed materials are distributed From the slave Is removed from the middle part, thereby Is employed in the next allocation and will The first in the matrix Line (1) Column shift out of matrix to obtain new The matrix is as follows: (8) s27, when all the ground targets are allocated as targets corresponding to the unmanned aerial vehicle, allocation is completed, otherwise, the steps S23 to S26 are repeated.
4. The unmanned aerial vehicle cluster-to-ground task collaborative planning method according to claim 1, wherein, The path comprises six structures, which are classified into LSL, RSR, LSR, RSL, RLR, LRL types, wherein L represents a left-turning arc, S represents a straight line, and R represents a right-turning arc.
5. The collaborative planning method for the unmanned aerial vehicle cluster to ground task according to claim 1, wherein the re-planning divides the whole stage into two processes, namely an obstacle avoidance process for guaranteeing safety and an execution process for completing the task, specifically: S41, finding an original path which is threatened by an obstacle, acquiring two intersection points of the original path and a threat ring and a course angle at the moment, connecting the two intersection points by using a straight line, drawing a vertical line through the center of the obstacle threat, taking the two intersection points of the vertical line and the threat ring as intermediate course points to be selected, keeping the course angle unchanged, and determining a new intermediate course point according to the shortest path length principle; S42, aiming at the obstacle avoidance process, selecting a starting point as a starting position of the unmanned aerial vehicle, selecting an end point as a new middle route point, and re-planning the path by adopting a Dubins path planning method to enable the unmanned aerial vehicle to bypass the obstacle; s43, aiming at the task execution process, the starting point is selected as a new middle route point, the end point is selected as a ground target position, and the path is re-planned by adopting a Dubins path planning method so as to complete the ground task.

Description

Unmanned aerial vehicle cluster ground task collaborative planning method Technical Field The invention belongs to a multi-unmanned aerial vehicle cooperative control technology, and particularly relates to a cooperative planning method for a ground task of an unmanned aerial vehicle cluster. Background By utilizing the advantages of high flying speed, good concealment and low cost of the unmanned aerial vehicle, how to quickly and accurately execute ground tasks in a complex environment becomes a key point of cooperative control of multiple unmanned aerial vehicles. The problem of collaborative planning of the unmanned aerial vehicle cluster to the ground task is that after unmanned aerial vehicles at different positions in the air receive the ground task, numerous ground tasks are distributed to the unmanned aerial vehicles, so that the unmanned aerial vehicles avoid obstacles, the shortest paths from the unmanned aerial vehicles to the targets are planned, and the task effect is optimal. Due to the limited capability of a single unmanned aerial vehicle, the cooperative control of multiple unmanned aerial vehicles becomes a main form of future unmanned aerial vehicle application, such as monitoring, reconnaissance, interference, striking and other tasks in the military aspect, aeroperformance in the civil aspect, lamplight show in the civil aspect, cooperative operation unmanned aerial vehicle earth observation, unmanned aerial vehicle logistics express, geological survey, forest fire prevention and the like. The collaborative planning of the ground-to-ground tasks of the unmanned aerial vehicle cluster is a key link of the unmanned aerial vehicle cluster system to execute the ground-to-ground tasks, the efficient and safe execution of the ground-to-ground tasks is important, and how to execute the ground-to-ground tasks by multiple unmanned aerial vehicles becomes a technical bottleneck for the development of the field. Disclosure of Invention Aiming at the problem of collaborative planning of unmanned aerial vehicle clusters on-ground tasks, the patent provides a method combining the technologies of situation awareness, target distribution, track planning, obstacle avoidance and the like. According to the method, situation assessment is firstly carried out based on state information of unmanned aerial vehicle clusters and ground task information, task targets are allocated to the unmanned aerial vehicle clusters according to assessment results and value estimation of ground tasks, and finally a flight path planning process is divided into an obstacle avoidance process and a task execution process, and the shortest safe flight path is planned by adopting Dubin curve aiming at the two processes. The invention has the following three main technical points. (1) And a situation assessment algorithm for ground tasks. The situation advantage of the unmanned aerial vehicle cluster on the ground target is evaluated by adopting an angle advantage index, a speed advantage index, a distance advantage index and the like, and the comprehensive advantage of the unmanned aerial vehicle cluster on the ground task is obtained through weighted calculation, so that an advantage function matrix is formed. (2) Matrix method-based target allocation algorithm. Finding out the element which is most in line with the condition from the dominant function matrix according to the selection rule of the maximum benefit, and pairing the unmanned aerial vehicle cluster corresponding to the element with the ground task. Taking the principle of cooperative control into consideration, performing cooperative priority sequencing, and selecting a target allocation scheme which is most favorable for unmanned aerial vehicle cluster task cooperation (3) Track planning techniques based on Dubins curves. And searching a safe flight track from the starting point to the target point to meet the mobility constraint condition, the advancing direction and the speed direction of the unmanned aerial vehicle according to the cluster position and the course angle of the unmanned aerial vehicle, the ground target position and the course angle and the obstacle threat area. (4) Track re-planning techniques. When the original track passes through the obstacle area, the safety of the unmanned aerial vehicle flight can be threatened, the flight path with collision danger needs to be re-planned, the unmanned aerial vehicle is guided to avoid the obstacle threat, and finally the cooperative ground alignment task is safely executed. Effects of the invention The beneficial effects of the invention are as follows: (1) In the situation assessment and evaluation stage, the search radius and attack radius of the unmanned aerial vehicle are fully considered, and the comprehensive situation of each unmanned aerial vehicle in the cluster on each ground target can be evaluated under the ground task by combining the angle advantage, the speed advantage and the distance advan