CN111766901A - Multi-unmanned aerial vehicle cooperative target distribution attack method - Google Patents

Multi-unmanned aerial vehicle cooperative target distribution attack method Download PDF

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CN111766901A
CN111766901A CN202010713170.XA CN202010713170A CN111766901A CN 111766901 A CN111766901 A CN 111766901A CN 202010713170 A CN202010713170 A CN 202010713170A CN 111766901 A CN111766901 A CN 111766901A
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unmanned aerial
attack
aerial vehicle
target
function
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CN111766901B (en
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王红滨
宋福平
何鸣
王勇
周连科
王念滨
张毅
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Harbin Engineering University
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Harbin Engineering University
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    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
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Abstract

The invention discloses a multi-unmanned aerial vehicle cooperative target allocation attack method, belongs to the technical field of multi-unmanned aerial vehicle cooperative target allocation, and solves the problem of low target attack accuracy rate caused by environmental influence when the conventional multi-unmanned aerial vehicle cooperative target allocation is used for fighting. After the attack system reaches a designated attack place, the target is reasonably distributed according to the established dominant attack function and the comprehensive consideration of the cost of voyage, time, threat, income and the like, so that the optimal attack position is found, and the ideal attack effect is achieved. The method is suitable for multiple unmanned aerial vehicles to cooperatively attack multiple targets.

Description

Multi-unmanned aerial vehicle cooperative target distribution attack method
Technical Field
The invention belongs to the technical field of multi-unmanned aerial vehicle cooperative target distribution, and particularly relates to a staged multi-unmanned aerial vehicle cooperative target distribution method.
Background
Aiming at the problem of multi-target multi-unmanned aerial vehicle collaborative task planning, currently, models mainly researched comprise multi-vehicle paths (MVRP), multi-dimensional multi-choice backpacks, multi-traveling salesmen (MTSP), Mixed Integer Linear Programming (MILP), Dynamic Network Flow Optimization (DNFO) and the like. The MVRP model describes more time-related constraints in task allocation, without fully considering the dynamics of the drone. And the MTSP model does not discuss the heterogeneity of tasks. The MILP and DNFO models are only applicable to mission planning problems with a small scope, a small number of targets, and a single source of environmental threats. The multimachine cooperation problem proposed by Mahesh and the like is abstracted into a plurality of mutually independent TSP problems, the optimization index is the shortest line length, and then the solution is carried out by utilizing a simulated annealing algorithm.
The multi-drone target allocation problem is one of the important problems for multi-drone mission planning. In the aspect of modeling of a target distribution problem, the influence of an uncertain environment on multi-target distribution problem modeling is realized, the rapid target distribution under the collision avoidance condition is realized, and the existing method only considers the condition that the number of unmanned aerial vehicles is less than the target number and only considers the influence of obstacles on the target distribution, and the consideration on the modeling condition is incomplete. The multi-unmanned aerial vehicle reconnaissance and tracking system realizes the task division by taking multi-unmanned aerial vehicle reconnaissance, tracking and the like as tasks, carries out the research of multi-task allocation and motion planning, and is particularly suitable for the modeling of the target allocation problem in the urban environment. Some people utilize a multi-branch tree structure to analyze the characteristics of the multi-unmanned aerial vehicle target distribution problem, consider the sequence constraint of target execution, and establish an effective multi-unmanned aerial vehicle multi-target distribution mathematical model, but do not research the relation between unmanned aerial vehicles and the target quantity. Still others evaluate targets by target priority or target value and solve the multi-target assignment problem by building an optimization model.
However, the actual operation condition is not considered in the above methods, and in actual use, the accuracy of the target shot by the unmanned aerial vehicle is poor due to the influence of environmental factors such as wind direction and wind speed.
Disclosure of Invention
The invention provides a multi-unmanned aerial vehicle cooperative target allocation attack method, which aims to solve the problem of low target attack accuracy rate caused by environmental influence during the existing multi-unmanned aerial vehicle cooperative target allocation combat.
The invention discloses a multi-unmanned aerial vehicle cooperative target distribution attack method, which comprises the following steps:
acquiring specific positions of the unmanned aerial vehicles and targets to be attacked and the number of the unmanned aerial vehicles and the number of the targets to be attacked;
determining a first-stage task allocation model of multiple unmanned aerial vehicles and multiple preset attack sites according to the positions of the unmanned aerial vehicles and targets to be attacked and the number of the unmanned aerial vehicles and the attack targets;
and step three, when the unmanned aerial vehicle reaches a preset attack place, establishing a combat function according to the positions of the unmanned aerial vehicle and the target to be attacked, determining the flight speed and the flight direction of the second stage of the unmanned aerial vehicle and the position of the unmanned aerial vehicle for launching the missile, and realizing the multi-unmanned aerial vehicle cooperative multi-target attack.
Further, the specific method for determining the first-stage task allocation model of the multiple unmanned aerial vehicles and the multiple predetermined attack sites according to the positions of the unmanned aerial vehicles and the targets to be attacked and the number of the unmanned aerial vehicles and the attack targets in the step two is as follows;
step two, determining preset attack sites of a plurality of unmanned aerial vehicles according to the positions of the unmanned aerial vehicles and the target to be attacked; secondly, establishing a multi-unmanned aerial vehicle flight range cost function and a multi-unmanned aerial vehicle flight time cost function according to the unmanned aerial vehicle and a preset attack place;
step two, carrying out normalization processing on the flight range cost function of the multiple unmanned aerial vehicles and the flight time cost function of the multiple unmanned aerial vehicles to obtain a task planning model of the cooperation of the multiple unmanned aerial vehicles;
step two, establishing a first-stage task constraint condition according to the number of the unmanned aerial vehicles and the attack targets and the limit conditions of the unmanned aerial vehicles;
and step two, acquiring a first-stage task allocation model of the multiple unmanned aerial vehicles according to the first-stage task constraint conditions and the multi-unmanned aerial vehicle collaborative task planning model.
Further, the flight range cost function and the flight time cost function of the multiple unmanned aerial vehicles in the second step are as follows:
the flight range cost function of the multiple unmanned aerial vehicles is as follows:
Figure BDA0002597287410000021
the flight time cost function of the multiple unmanned planes is as follows:
Figure BDA0002597287410000022
X(i,j)is a decision variable, tijRepresenting the flight time when the ith unmanned plane reaches a predetermined attack j target, dijAnd the flight range of the ith unmanned aerial vehicle when the ith unmanned aerial vehicle reaches a preset target of attack j is shown, n is the number of the unmanned aerial vehicles, and m is the number of targets to be attacked.
Further, in the second step, the task planning model for cooperation of multiple unmanned aerial vehicles is as follows:
minf＝ω1L1+ω2L2 (3)
wherein, ω is1And ω2Respectively, the weight ratio of the voyage cost function and the weight ratio of the time cost function, and omega1+ω2＝1。
Further, the first stage task constraints include: unmanned aerial vehicle and target point decision variable constraint conditions and collaborative constraint conditions, wherein the collaborative constraint conditions comprise: a maximum range constraint, a maximum voyage time constraint, a minimum/maximum airspeed constraint, an attack target timing constraint, and a time window constraint.
Further, the unmanned aerial vehicle and target point decision variables are constrained to be:
when the number n of the unmanned aerial vehicles is larger than the number m of the targets, namely n is larger than or equal to m, the task decision is that each unmanned aerial vehicle attacks at least one target point;
when the number n of the unmanned aerial vehicles is smaller than the number m of the targets, namely n is smaller than m, the task decision is that one unmanned aerial vehicle must be allocated to each target;
Figure BDA0002597287410000031
Figure BDA0002597287410000032
further, the specific method for establishing the combat function according to the predetermined attack location and the specific position of the attack target in the third step is as follows:
step three, establishing a coordinate system according to the position of the unmanned aerial vehicle and the position of the target, and obtaining a coordinate matrix of the unmanned aerial vehicle and a coordinate matrix of the target;
thirdly, obtaining the distance d between each unmanned aerial vehicle and the target to be attacked by utilizing the coordinate matrix of the unmanned aerial vehicle and the target coordinate matrixijAnd the depression angle theta of each unmanned aerial vehicle to be attackedij；
Thirdly, according to the distance d between the unmanned aerial vehicle and the target to be attackedijAnd the depression angle theta of the unmanned aerial vehicle to the target to be attackedij(ii) a Establishing an advantage attack function of the unmanned aerial vehicle;
step three, judging whether the dominant attack function value of the unmanned aerial vehicle reaches an attack threshold value, if so, executing the step three, otherwise, executing the step three;
establishing a relation between the unmanned aerial vehicle and the coordinate change of the target and the dominant attack function by using the Jacobi matrix, and acquiring a function of the coordinate change of the unmanned aerial vehicle;
step three, updating the coordinates of the unmanned aerial vehicle to obtain a new coordinate matrix of the unmanned aerial vehicle; returning to execute the third step;
and step seven, establishing a combat function of the unmanned aerial vehicle by using a cost function in the attack process of the unmanned aerial vehicle.
Further, the dominant attack function in step three includes:
dominant attack function of drone with respect to combat distance:
Figure BDA0002597287410000033
in the formula:
Figure BDA0002597287410000034
representing that the target j obtains the dominant attack function of the unmanned aerial vehicle i about the combat distance; r isDRepresenting the radius of the combat attack of the ith unmanned aerial vehicle; dijRepresenting the linear distance between the unmanned plane i and the target j;
dominant attack function of drone with respect to attack angle:
Figure BDA0002597287410000041
in the formula:
Figure BDA0002597287410000042
representing that the target j obtains a dominant attack function of the unmanned aerial vehicle i about the attack angle; thetaiRepresenting the angle with the best attack effect of the unmanned aerial vehicle on the target; thetaijRepresenting a depression angle between the drone and the target;
dominant attack function of drone with respect to attack speed:
Figure BDA0002597287410000043
in the formula:
Figure BDA0002597287410000044
the jth target obtains a dominant attack function of the ith unmanned aerial vehicle about the attack speed; vijRepresenting the relative speed of the ith unmanned aerial vehicle and the jth target; viRepresenting the optimal relative speed of the drone and the target at the time of the attack.
Further, in the third step, the Jacobian matrix is utilized to establish a relation between the coordinate change of the unmanned aerial vehicle and the target and the dominant attack function, and a function of the coordinate change of the unmanned aerial vehicle is obtained;
and establishing a relation between the coordinate change and the dominant attack function by utilizing the Jacobi matrix.
Figure BDA0002597287410000045
Figure BDA0002597287410000046
Figure BDA0002597287410000051
In the formula:
Figure BDA0002597287410000052
representing the spatial coordinates of the 1 st drone;
Figure BDA0002597287410000053
representing the spatial coordinates of the nth drone;
Figure BDA0002597287410000054
representing the distance dominance value obtained by the jth target;
Figure BDA0002597287410000055
representing the distance advantage value required to be obtained by the mth target;
Figure BDA0002597287410000056
representing the angle dominance value obtained by the jth target;
Figure BDA0002597287410000057
indicating the angular dominance value that the mth target needs to obtain,
Figure BDA0002597287410000058
representing the speed advantage value required to be obtained by the jth target;
Figure BDA0002597287410000059
representing the speed advantage value required to be obtained by the mth target; wherein the content of the first and second substances,
Figure BDA00025972874100000510
Figure BDA00025972874100000511
jacobian matrixes corresponding to the three dominant attack functions; the matrix is obtained by performing partial derivation on the space coordinate of the unmanned aerial vehicle in the dominant attack function, and the change function of the coordinate of the unmanned aerial vehicle is as follows:
Figure BDA00025972874100000512
Figure BDA00025972874100000513
Figure BDA00025972874100000514
in the formula: t represents the transposed symbol of the matrix;
Figure BDA00025972874100000515
representing the first derivative of the attack dominance value;
Figure BDA00025972874100000516
represents the first derivative of the coordinates; j. the design is a squarejacobiA Jacobian matrix expressed as partial derivatives;
using JjacobiIts pseudo-inverse solves:
Figure BDA00025972874100000517
in the formula (I), the compound is shown in the specification,
Figure BDA00025972874100000518
is JjacobiCalculating a pseudo inverse; j. the design is a square+Represents a pseudo-inverse; j. the design is a squareTExpressed as a transpose of a jacobian matrix;
Figure BDA00025972874100000519
the capability value that needs to be obtained for target j, j ═ 1,. k, …, m.
Updating the coordinates by the unmanned aerial vehicle, and acquiring the coordinates of the ith unmanned aerial vehicle at the moment k + 1;
Figure BDA00025972874100000520
in the formula:
Figure BDA0002597287410000061
a change value which is an attack dominance value; Δ t represents a time step in the attack process;
Figure BDA0002597287410000062
representing the coordinates of the ith unmanned aerial vehicle at the moment k;
further, the specific method for establishing the combat function of the unmanned aerial vehicle by using the cost function in the attack process of the unmanned aerial vehicle in the step of pseudo-ginseng comprises the following steps:
establishing a combat function by utilizing a flight range cost function, a time cost function, a profit cost function and a threat cost function;
wherein, the flight range cost function is as follows:
c1＝β1di(17)
the flight range cost function refers to the flight cost required by updating a new coordinate position in real time when the unmanned aerial vehicle does not reach the optimal attack position after reaching the specified attack place, wherein β1Is the weight occupied by the flight path, diIs the actual distance from the point of attack to the point of attack specified after the position is updated;
the time cost function is:
c2＝β2tij(18)
the time cost function is the time cost required by updating a new coordinate position in real time when the unmanned aerial vehicle does not reach the optimal attack position after arriving at the appointed attack place; wherein, tijThe time refers to the time for completing the attack after the specified target is reached to the update position;
the revenue cost function is:
c3＝β3(1-Vj.Ai) (19)
the profit cost function is the profit cost generated by the unmanned aerial vehicle i attacking the target j, and the benefit brought by the i-th unmanned aerial vehicle after the attack is recorded as P ═ Vj.AiWherein V isjFor the value of the object itself, AiProbability of destroying a target for the drone;
the threat cost function is:
Figure BDA0002597287410000063
wherein the content of the first and second substances,
Figure BDA0002597287410000064
representing the value of the ith drone; kiProbability of destroying the drone for the target;
summing the flight path cost function, the time cost function, the profit cost function and the threat cost function to Ccost，
Ccost＝c1+c2+c3+c4(21)
And then obtain unmanned aerial vehicle's second stage combat function:
Figure BDA0002597287410000071
the constraints of the function are:
Figure BDA0002597287410000072
in the formula: (24) each drone is required to attack only one target at a time.
β1+β2+β3+β4＝1 (24)
Wherein, β1、β2、β3、β4Representative voyage cost function, time cost function, profitThe cost function and the weight occupied by the threat cost function.
The invention divides the attack of multiple unmanned aerial vehicles on the target into two stages, wherein the first stage is that the unmanned aerial vehicles start from the same base and reach a preset attack place, the speed and course of the unmanned aerial vehicles are controlled by establishing a task distribution model, when the unmanned aerial vehicles start from the base to the appointed attack place, a combat function is established according to the factors of the target returning environment, the wind field and the like, the flight speed and the flight direction of the second stage of the unmanned aerial vehicles and the missile launching direction of the unmanned aerial vehicles are determined, the angle coordinates and the like of the unmanned aerial vehicle attack target are adjusted by adopting a new algorithm, so that the unmanned aerial vehicles hit at a proper visual angle and speed, the optimal attack effect is achieved, and the accuracy of the target attack is effectively improved.
Drawings
FIG. 1 is a diagram of an attack situation of the method of the present invention;
FIG. 2 is a schematic flow diagram of the method of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict.
The first embodiment is as follows: the following describes the present embodiment with reference to fig. 1 and 2, where the method for allocating an attack to a cooperative target by multiple drones in the present embodiment includes:
acquiring specific positions of the unmanned aerial vehicles and targets to be attacked and the number of the unmanned aerial vehicles and the number of the targets to be attacked;
determining a first-stage task allocation model of multiple unmanned aerial vehicles and multiple preset attack sites according to the positions of the unmanned aerial vehicles and targets to be attacked and the number of the unmanned aerial vehicles and the attack targets;
and step three, when the unmanned aerial vehicle reaches a preset attack place, establishing a combat function according to the positions of the unmanned aerial vehicle and the target to be attacked, determining the flight speed and the flight direction of the second stage of the unmanned aerial vehicle and the position of the unmanned aerial vehicle for launching the missile, and realizing the multi-unmanned aerial vehicle cooperative multi-target attack.
The unmanned aerial vehicle comprehensively considers different factors in two stages in the attack battle. In the first stage, the unmanned plane arrives at a designated attack area, and only time and range are considered due to the small probability of threat and damage in the middle. And in the second stage, an advantage attack function is established, each aerial direction is quantized, the attack accuracy of the unmanned aerial vehicle is improved, meanwhile, the threat suffered by the unmanned aerial vehicle in the attack process is comprehensively considered, the income is obtained, and the influence of the environment on the battle is obtained.
Further, the specific method for determining the first-stage task allocation model of the multiple unmanned aerial vehicles and the multiple predetermined attack sites according to the positions of the unmanned aerial vehicles and the targets to be attacked and the number of the unmanned aerial vehicles and the attack targets in the step two is as follows;
step two, determining preset attack sites of a plurality of unmanned aerial vehicles according to the positions of the unmanned aerial vehicles and the target to be attacked; secondly, establishing a multi-unmanned aerial vehicle flight range cost function and a multi-unmanned aerial vehicle flight time cost function according to the unmanned aerial vehicle and a preset attack place;
step two, carrying out normalization processing on the flight range cost function of the multiple unmanned aerial vehicles and the flight time cost function of the multiple unmanned aerial vehicles to obtain a task planning model of the cooperation of the multiple unmanned aerial vehicles;
step two, establishing a first-stage task constraint condition according to the number of the unmanned aerial vehicles and the attack targets and the limit conditions of the unmanned aerial vehicles;
and step two, acquiring a first-stage task allocation model of the multiple unmanned aerial vehicles according to the first-stage task constraint conditions and the multi-unmanned aerial vehicle collaborative task planning model.
Further, the flight range cost function and the flight time cost function of the multiple unmanned aerial vehicles in the second step are as follows:
the flight range cost function of the multiple unmanned aerial vehicles is as follows:
Figure BDA0002597287410000081
the flight time cost function of the multiple unmanned planes is as follows:
Figure BDA0002597287410000082
X(i,j)is a decision variable, tijRepresenting the flight time when the ith unmanned plane reaches a predetermined attack j target, dijAnd the flight range of the ith unmanned aerial vehicle when the ith unmanned aerial vehicle reaches a preset target of attack j is shown, n is the number of the unmanned aerial vehicles, and m is the number of targets to be attacked.
Further, in the second step, the task planning model for cooperation of multiple unmanned aerial vehicles is as follows:
minf＝ω1L1+ω2L2 (3)
wherein, ω is1And ω2Respectively, the weight ratio of the voyage cost function and the weight ratio of the time cost function, and omega1+ω2＝1。
Further, the first stage task constraints include: unmanned aerial vehicle and target point decision variable constraint conditions and collaborative constraint conditions, wherein the collaborative constraint conditions comprise: a maximum range constraint, a maximum voyage time constraint, a minimum/maximum airspeed constraint, an attack target timing constraint, and a time window constraint.
Further, the unmanned aerial vehicle and target point decision variables are constrained to be:
when the number n of the unmanned aerial vehicles is larger than the number m of the targets, namely n is larger than or equal to m, the task decision is that each unmanned aerial vehicle attacks at least one target point;
when the number n of the unmanned aerial vehicles is smaller than the number m of the targets, namely n is smaller than m, the task decision is that one unmanned aerial vehicle must be allocated to each target;
Figure BDA0002597287410000091
Figure BDA0002597287410000092
further, the specific method for establishing the combat function according to the predetermined attack location and the specific position of the attack target in the third step is as follows:
step three, establishing a coordinate system according to the position of the unmanned aerial vehicle and the position of the target, and obtaining a coordinate matrix of the unmanned aerial vehicle and a coordinate matrix of the target;
thirdly, obtaining the distance d between each unmanned aerial vehicle and the target to be attacked by utilizing the coordinate matrix of the unmanned aerial vehicle and the target coordinate matrixijAnd the depression angle theta of each unmanned aerial vehicle to be attackedij；
Thirdly, according to the distance d between the unmanned aerial vehicle and the target to be attackedijAnd the depression angle theta of the unmanned aerial vehicle to the target to be attackedij(ii) a Establishing an advantage attack function of the unmanned aerial vehicle;
step three, judging whether the dominant attack function value of the unmanned aerial vehicle reaches an attack threshold value, if so, executing the step three, otherwise, executing the step three;
establishing a relation between the unmanned aerial vehicle and the coordinate change of the target and the dominant attack function by using the Jacobi matrix, and acquiring a function of the coordinate change of the unmanned aerial vehicle;
step three, updating the coordinates of the unmanned aerial vehicle to obtain a new coordinate matrix of the unmanned aerial vehicle; returning to execute the third step;
and step seven, establishing a combat function of the unmanned aerial vehicle by using a cost function in the attack process of the unmanned aerial vehicle.
Further, the dominant attack function in step three includes:
dominant attack function of drone with respect to combat distance:
Figure BDA0002597287410000101
in the formula:
Figure BDA0002597287410000102
representing that the target j obtains the dominant attack function of the unmanned aerial vehicle i about the combat distance; r isiRepresenting the radius of the combat attack of the ith unmanned aerial vehicle; dijRepresenting the linear distance between the unmanned plane i and the target j;
dominant attack function of drone with respect to attack angle:
Figure BDA0002597287410000103
in the formula:
Figure BDA0002597287410000104
representing that the target j obtains a dominant attack function of the unmanned aerial vehicle i about the attack angle; thetaiRepresenting the angle with the best attack effect of the unmanned aerial vehicle on the target; thetaijRepresenting a depression angle between the drone and the target;
dominant attack function of drone with respect to attack speed:
Figure BDA0002597287410000105
in the formula:
Figure BDA0002597287410000106
the jth target obtains a dominant attack function of the ith unmanned aerial vehicle about the attack speed; vijRepresenting the relative speed of the ith unmanned aerial vehicle and the jth target; viRepresenting the optimal relative speed of the drone and the target at the time of the attack.
Further, in the third step, the Jacobian matrix is utilized to establish a relation between the coordinate change of the unmanned aerial vehicle and the target and the dominant attack function, and a function of the coordinate change of the unmanned aerial vehicle is obtained;
and establishing a relation between the coordinate change and the dominant attack function by utilizing the Jacobi matrix.
Figure BDA0002597287410000107
Figure BDA0002597287410000111
Figure BDA0002597287410000112
In the formula:
Figure BDA0002597287410000113
representing the spatial coordinates of the 1 st drone;
Figure BDA0002597287410000114
representing the spatial coordinates of the nth drone;
Figure BDA0002597287410000115
representing the distance dominance value obtained by the jth target;
Figure BDA0002597287410000116
representing the distance advantage value required to be obtained by the mth target;
Figure BDA0002597287410000117
representing the angle dominance value obtained by the jth target;
Figure BDA0002597287410000118
indicating the angular dominance value that the mth target needs to obtain,
Figure BDA0002597287410000119
representing the speed advantage value required to be obtained by the jth target;
Figure BDA00025972874100001110
representing the speed advantage value required to be obtained by the mth target; wherein the content of the first and second substances,
Figure BDA00025972874100001111
Figure BDA00025972874100001112
jacobian matrixes corresponding to the three dominant attack functions; the matrix is obtained by performing partial derivation on the space coordinate of the unmanned aerial vehicle in the dominant attack function, and the change function of the coordinate of the unmanned aerial vehicle is as follows:
Figure BDA00025972874100001113
in the formula: t represents the transposed symbol of the matrix;
Figure BDA00025972874100001114
representing the first derivative of the attack dominance value;
Figure BDA00025972874100001115
represents the first derivative of the coordinates; j. the design is a squarejacobiA Jacobian matrix expressed as partial derivatives;
using JjacobiIts pseudo-inverse solves:
Figure BDA00025972874100001116
in the formula (I), the compound is shown in the specification,
Figure BDA00025972874100001117
is JjacobiCalculating a pseudo inverse; j. the design is a square+Represents a pseudo-inverse; j. the design is a squareTExpressed as a transpose of a jacobian matrix;
Figure BDA00025972874100001118
the capability value that needs to be obtained for target j, j ═ 1,. k, …, m.
Updating the coordinates by the unmanned aerial vehicle, and acquiring the coordinates of the ith unmanned aerial vehicle at the moment k + 1;
Figure BDA0002597287410000121
in the formula:
Figure BDA0002597287410000122
change in value of dominance for attackA value; Δ t represents a time step in the attack process;
Figure BDA0002597287410000123
representing the coordinates of the ith unmanned aerial vehicle at the moment k;
further, the specific method for establishing the combat function of the unmanned aerial vehicle by using the cost function in the attack process of the unmanned aerial vehicle in the step of pseudo-ginseng comprises the following steps:
establishing a combat function by utilizing a flight range cost function, a time cost function, a profit cost function and a threat cost function;
wherein, the flight range cost function is as follows:
c1＝β1di(17)
the flight range cost function refers to the flight cost required by updating a new coordinate position in real time when the unmanned aerial vehicle does not reach the optimal attack position after reaching the specified attack place, wherein β1Is the weight occupied by the flight path, diIs the actual distance from the point of attack to the point of attack specified after the position is updated;
the time cost function is:
c2＝β2tij(18)
the time cost function is the time cost required by updating a new coordinate position in real time when the unmanned aerial vehicle does not reach the optimal attack position after arriving at the appointed attack place; wherein, tijThe time refers to the time for completing the attack after the specified target is reached to the update position;
the revenue cost function is:
c3＝β3(1-Vj.Ai) (19)
the profit cost function is the profit cost generated by the unmanned aerial vehicle i attacking the target j, and the benefit brought by the i-th unmanned aerial vehicle after the attack is recorded as P ═ Vj.AiWherein V isjFor the value of the object itself, AiProbability of destroying a target for the drone;
the threat cost function is:
Figure BDA0002597287410000131
wherein the content of the first and second substances,
Figure BDA0002597287410000132
representing the value of the ith drone; kiProbability of destroying the drone for the target;
summing the flight path cost function, the time cost function, the profit cost function and the threat cost function to Ccost，
Ccost＝c1+c2+c3+c4(21)
And then obtain unmanned aerial vehicle's second stage combat function:
Figure BDA0002597287410000133
the constraints of the function are:
Figure BDA0002597287410000134
in the formula: (24) each drone is required to attack only one target at a time.
β1+β2+β3+β4＝1 (24)
Wherein, β1、β2、β3、β4Representing the weight occupied by the travel distance cost function, the time cost function, the income cost function and the threat cost function.
The cooperative target distribution modeling method is mainly used for carrying out cooperative target distribution modeling by taking attack combat tasks of a plurality of targets under complex constraint conditions of a plurality of unmanned aerial vehicles as backgrounds. Firstly, in the algorithm, considering the attack range, the attack speed, the attack angle, the wind field factor and the like of the unmanned aerial vehicle in the attack process, a dominant attack function is established, and the unmanned aerial vehicle in the air is enabled. Secondly, the algorithm uses a jacobian matrix in mathematics to link the motion change of the unmanned aerial vehicle with the dominant attack function. In general, the model is divided into two combat phases, the first part of unmanned aerial vehicles arrive at the attack area of the known unmanned aerial vehicle from the same place, and time cost and flight cost are mainly considered. After the attack system reaches a designated attack place, the target is reasonably distributed according to the established dominant attack function and the comprehensive consideration of the cost of voyage, time, threat, income and the like, so that the optimal attack position is found, and the ideal attack effect is achieved.
Although the invention herein has been described with reference to particular embodiments, it is to be understood that these embodiments are merely illustrative of the principles and applications of the present invention. It is therefore to be understood that numerous modifications may be made to the illustrative embodiments and that other arrangements may be devised without departing from the spirit and scope of the present invention as defined by the appended claims. It should be understood that features described in different dependent claims and herein may be combined in ways different from those described in the original claims. It is also to be understood that features described in connection with individual embodiments may be used in other described embodiments.

Claims (10)

1. The multi-unmanned aerial vehicle cooperative target allocation attack method is characterized by specifically comprising the following steps:
acquiring specific positions of the unmanned aerial vehicles and targets to be attacked and the number of the unmanned aerial vehicles and the number of the targets to be attacked;
determining a first-stage task allocation model of the multiple unmanned aerial vehicles according to the positions and the number of the unmanned aerial vehicles and the targets to be attacked;
and step three, determining a plurality of preset attack places according to the first-stage task allocation model of the multiple unmanned aerial vehicles, establishing a combat function according to the positions of the unmanned aerial vehicles and the target to be attacked, determining the flight speed and the flight direction of the second stage of the unmanned aerial vehicles and the position of the unmanned aerial vehicles for launching the missile, and realizing the multi-unmanned aerial vehicle cooperative multi-target attack.
2. The multi-unmanned aerial vehicle cooperative target distribution attack method according to claim 1, wherein in the second step, the specific method for determining the first-stage task distribution model of the multi-unmanned aerial vehicle according to the positions and the number of the unmanned aerial vehicles and the targets to be attacked is;
step two, determining preset attack sites of a plurality of unmanned aerial vehicles according to the positions of the unmanned aerial vehicles and the target to be attacked;
secondly, establishing a multi-unmanned aerial vehicle flight range cost function and a multi-unmanned aerial vehicle flight time cost function according to the unmanned aerial vehicle and a preset attack place;
step two, carrying out normalization processing on the flight range cost function of the multiple unmanned aerial vehicles and the flight time cost function of the multiple unmanned aerial vehicles to obtain a task planning model of the cooperation of the multiple unmanned aerial vehicles;
step two, establishing a first-stage task constraint condition according to the number of the unmanned aerial vehicles and the attack targets and the limit conditions of the unmanned aerial vehicles;
and step two, acquiring a first-stage task allocation model of the multiple unmanned aerial vehicles according to the first-stage task constraint conditions and the multi-unmanned aerial vehicle collaborative task planning model.
3. The multi-unmanned aerial vehicle cooperative target distribution attack method according to claim 2, wherein the flight range cost function of the multi-unmanned aerial vehicle in the second step is:
Figure FDA0002597287400000011
the flight time cost function of the multiple unmanned planes is as follows:
Figure FDA0002597287400000012
wherein, X(i,j)Is a decision variable, tijRepresenting the flight time when the ith unmanned plane reaches a predetermined attack j target, dijAnd the flight range of the ith unmanned aerial vehicle when the ith unmanned aerial vehicle reaches a preset target of attack j is shown, n is the number of the unmanned aerial vehicles, and m is the number of targets to be attacked.
4. The multi-unmanned aerial vehicle cooperative target distribution attack method according to claim 3, wherein the task planning model of multi-unmanned aerial vehicle cooperation in the second step and the third step is as follows:
minf＝ω1L1+ω2L2 (3)
wherein, ω is1And ω2Respectively, the weight ratio of the voyage cost function and the weight ratio of the time cost function, and omega1+ω2＝1。
5. The cooperative multi-drone target distribution attack method according to claim 4, wherein the first-stage task constraints in the second and fourth steps include: unmanned aerial vehicle and target point decision variable constraint conditions and collaborative constraint conditions, wherein the collaborative constraint conditions comprise: a maximum range constraint, a maximum voyage time constraint, a minimum/maximum airspeed constraint, an attack target timing constraint, and a time window constraint.
6. The multi-drone cooperative target distribution attack method according to claim 5, wherein the drone and target point decision variables are constrained as:
when the number n of the unmanned aerial vehicles is larger than the number m of the targets, namely n is larger than or equal to m, the task decision is that each unmanned aerial vehicle attacks at least one target point;
when the number n of the unmanned aerial vehicles is smaller than the number m of the targets, namely n is smaller than m, the task decision is that one unmanned aerial vehicle must be allocated to each target;
Figure FDA0002597287400000021
Figure FDA0002597287400000022
7. the multi-unmanned aerial vehicle cooperative target distribution attack method according to claim 6, wherein the specific method for establishing the combat function according to the predetermined attack site and the specific position of the attack target in the third step is as follows:
step three, establishing a coordinate system according to the position of the unmanned aerial vehicle and the position of the target, and obtaining a coordinate matrix of the unmanned aerial vehicle and a coordinate matrix of the target;
thirdly, obtaining the distance d between each unmanned aerial vehicle and the target to be attacked by utilizing the coordinate matrix of the unmanned aerial vehicle and the target coordinate matrixijAnd the depression angle theta of each unmanned aerial vehicle to be attackedij；
Thirdly, according to the distance d between the unmanned aerial vehicle and the target to be attackedijAnd the depression angle theta of the unmanned aerial vehicle to the target to be attackedij(ii) a Establishing an advantage attack function of the unmanned aerial vehicle;
step three, judging whether the dominant attack function value of the unmanned aerial vehicle reaches an attack threshold value, if so, executing the step three, otherwise, executing the step three;
establishing a relation between the unmanned aerial vehicle and the coordinate change of the target and the dominant attack function by using the Jacobi matrix, and acquiring a function of the coordinate change of the unmanned aerial vehicle;
step three, updating the coordinates of the unmanned aerial vehicle to obtain a new coordinate matrix of the unmanned aerial vehicle; returning to execute the third step;
and step seven, establishing a combat function of the unmanned aerial vehicle by using a cost function in the attack process of the unmanned aerial vehicle.
8. The multi-drone cooperative target distribution attack method according to claim 7, wherein the dominance attack function in step three includes:
dominant attack function of drone with respect to combat distance:
Figure FDA0002597287400000031
in the formula:
Figure FDA0002597287400000032
representing that the target j obtains the dominant attack function of the unmanned aerial vehicle i about the combat distance; r isiShow that I frame unmanned aerial vehicle's combat is attackedThe radius of attack; dijRepresenting the linear distance between the unmanned plane i and the target j;
dominant attack function of drone with respect to attack angle:
Figure FDA0002597287400000033
in the formula:
Figure FDA0002597287400000034
representing that the target j obtains a dominant attack function of the unmanned aerial vehicle i about the attack angle; thetaiRepresenting the angle with the best attack effect of the unmanned aerial vehicle on the target; thetaijRepresenting a depression angle between the drone and the target;
dominant attack function of drone with respect to attack speed:
Figure FDA0002597287400000035
in the formula:
Figure FDA0002597287400000036
the jth target obtains a dominant attack function of the ith unmanned aerial vehicle about the attack speed; vijRepresenting the relative speed of the ith unmanned aerial vehicle and the jth target; viRepresenting the optimal relative speed of the drone and the target at the time of the attack.
9. The multi-unmanned aerial vehicle cooperative target distribution attack method according to claim 8, wherein the third and fourth steps establish a connection between the unmanned aerial vehicle and the coordinate change of the target and the dominant attack function by using a jacobian matrix, and obtain a function of the coordinate change of the unmanned aerial vehicle;
and (3) establishing a relation between the coordinate change and the dominant attack function by utilizing a Jacobian matrix:
Figure FDA0002597287400000037
Figure FDA0002597287400000041
Figure FDA0002597287400000042
in the formula:
Figure FDA0002597287400000043
representing the spatial coordinates of the 1 st drone;
Figure FDA0002597287400000044
representing the spatial coordinates of the nth drone;
Figure FDA0002597287400000045
representing the distance dominance value obtained by the jth target;
Figure FDA0002597287400000046
representing the distance advantage value required to be obtained by the mth target;
Figure FDA0002597287400000047
representing the angle dominance value obtained by the jth target;
Figure FDA0002597287400000048
indicating the angular dominance value that the mth target needs to obtain,
Figure FDA0002597287400000049
representing the speed advantage value required to be obtained by the jth target;
Figure FDA00025972874000000410
representing the speed advantage value required to be obtained by the mth target; wherein the content of the first and second substances,
Figure FDA00025972874000000411
Figure FDA00025972874000000412
jacobian matrixes corresponding to the three dominant attack functions; the matrix is obtained by performing partial derivation on the space coordinate of the unmanned aerial vehicle in the dominant attack function, and the change function of the coordinate of the unmanned aerial vehicle is as follows:
Figure FDA00025972874000000413
Figure FDA00025972874000000414
Figure FDA00025972874000000415
in the formula: t represents the transposed symbol of the matrix;
Figure FDA00025972874000000416
representing the first derivative of the attack dominance value;
Figure FDA00025972874000000417
represents the first derivative of the coordinates; j. the design is a squarejacobiA Jacobian matrix expressed as partial derivatives;
using JjacobiIts pseudo-inverse solves:
Figure FDA00025972874000000418
in the formula (I), the compound is shown in the specification,
Figure FDA00025972874000000419
is JjacobiCalculating a pseudo inverse; j. the design is a square+Represents a pseudo-inverse; j. the design is a squareTExpressed as a transpose of a jacobian matrix;
Figure FDA00025972874000000420
is a target ofj requires the capability value obtained, j ═ {1,. k, …, m };
updating the coordinates by the unmanned aerial vehicle, and acquiring the coordinates of the ith unmanned aerial vehicle at the moment k + 1;
Figure FDA0002597287400000051
in the formula:
Figure FDA0002597287400000052
a change value which is an attack dominance value; Δ t represents a time step in the attack process;
Figure FDA0002597287400000053
representing the coordinates of the ith drone at time k.
10. The multi-unmanned aerial vehicle cooperative target allocation attack method according to claim 9, wherein the specific method for establishing the combat function of the unmanned aerial vehicle by using the cost function in the unmanned aerial vehicle attack process in the step of pseudo-ginseng comprises the following steps:
establishing a combat function by utilizing a flight range cost function, a time cost function, a profit cost function and a threat cost function;
wherein, the flight range cost function is as follows:
c1＝β1di(17)
the flight range cost function refers to the flight cost required by updating a new coordinate position in real time when the unmanned aerial vehicle does not reach the optimal attack position after reaching the specified attack place, wherein β1Is the weight occupied by the flight path, diIs the actual distance from the point of attack to the point of attack specified after the position is updated;
the time cost function is:
c2＝β2tij(18)
the time cost function is the time required for updating a new coordinate position in real time when the unmanned aerial vehicle does not reach the optimal attack position after reaching the specified attack positionA cost; wherein, tijThe time refers to the time for completing the attack after the specified target is reached to the update position;
the revenue cost function is:
c3＝β3(1-Vj.Ai) (19)
the profit cost function is the profit cost generated by the unmanned aerial vehicle i attacking the target j, and the benefit brought by the i-th unmanned aerial vehicle after the attack is recorded as P ═ Vj.AiWherein V isjFor the value of the object itself, AiProbability of destroying a target for the drone;
the threat cost function is:
Figure FDA0002597287400000054
wherein the content of the first and second substances,
Figure FDA0002597287400000055
representing the value of the ith drone; kiProbability of destroying the drone for the target;
summing the flight path cost function, the time cost function, the profit cost function and the threat cost function to Ccost；
Ccost＝c1+c2+c3+c4(21)
And then obtain unmanned aerial vehicle's second stage combat function:
Figure FDA0002597287400000061
the constraints of the function are:
Figure FDA0002597287400000062
in the formula: each unmanned aerial vehicle is required to attack only one target at the same time;
β1+β2+β3+β4＝1 (24)
wherein, β1、β2、β3、β4Representing the weight occupied by the travel distance cost function, the time cost function, the income cost function and the threat cost function.
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