CN112733421A - Task planning method for cooperative fight against earth by unmanned aerial vehicle - Google Patents

Abstract

The invention discloses a mission planning method for cooperative combat to the ground of unmanned aerial vehicles, which relates to the field of unmanned aerial vehicle mission planning and comprises the following steps: s1, battlefield environment modeling: fusing original topographic features and threat information of the combat space into comprehensive topographic information by adopting a digital map information fusion principle; s2, acquiring the number of the unmanned aerial vehicles, the number of the unmanned aerial vehicles and the number of targets: the ground-to-ground strike formation comprises 1 unmanned aerial vehicle and m unmanned aerial vehicles (v)₁,v₂,…,v_m) N targets to be hit are found in total (t)₁,t₂,…,t_n) (ii) a Establishing a task allocation objective function: establishment of the objective function takes into account three factors, the packageIncluding the hit value of the target, the track cost of traveling to the target, and the threat cost incurred during cruising. The A-star algorithm is adopted to carry out the solution of the plane planning part and the height planning part, and a task planning scheme meeting the requirements can be provided for the cooperative combat of the manned unmanned aerial vehicle to the ground.

Description

Task planning method for cooperative fight against earth by unmanned aerial vehicle

Technical Field

The invention relates to the technical field of unmanned aerial vehicle mission planning, in particular to a mission planning method for cooperative fight against the ground of unmanned aerial vehicles.

Background

Compared with a man-machine, the unmanned aerial vehicle has obvious advantages in the aspects of cost, convenience, maneuverability, adaptability and the like, and is also favored by multi-country military. The advantages of each warplane in the aspects of airborne weaponry and tactics of multi-machine cooperative combat are complementary and matched with each other, and the efficiency of 1+1>2 can be achieved. In recent years, China carries out more theoretical researches on cooperative formation of multiple unmanned aerial vehicles, but is limited by the intelligent development degree of the existing unmanned aerial vehicles and a combat system, the hardware capability is not yet higher than the theoretical height, complete autonomous combat by mutual cooperation of pure unmanned aerial vehicles is not practical in a short time, manual control is needed, and the combat process is controlled and supervised to ensure task completion and use safety. Thus, there is still a need to preferentially develop manned/unmanned aerial vehicle synergies.

The cooperative operation of the manned/unmanned aerial vehicle comprises a plurality of key technologies, including a man-machine/unmanned aerial vehicle cooperative control technology, a situation perception and evaluation technology, a cooperative mission planning technology, a formation flight and tracking control technology, a battlefield intelligent decision technology, a target hitting efficiency evaluation technology and the like. Many links are closely matched to obtain an ideal battle effect. The mission planning technology is used as a center and an important link of a manned/unmanned aerial vehicle cooperative combat technology, and the winning probability of the combat is determined to a great extent.

Currently, there is less research on mission planning of a manned/unmanned aerial vehicle, and most of the currently published research is research on a two-dimensional plane. The method is researched aiming at the manned/unmanned aerial vehicle cooperative attack mission planning in the three-dimensional scene and aiming at the manned/unmanned aerial vehicle cooperative finite concentrated distributed control mode. The specific scenario is described as follows: before the ground striking task is started, a person obtains necessary information transmitted back by a reconnaissance and detection unmanned aerial vehicle, wherein the necessary information comprises information such as battlefield threats, environments, airspaces, enemy target numbers and the like, the person distributes targets to be struck for each unmanned aerial vehicle, the process of the unmanned aerial vehicle for executing the task is supervised during the period, each unmanned aerial vehicle has inherent characteristics (such as flight performance, maneuvering performance and the like) and is provided with equipment such as an onboard computer, a payload, a sensor, a data chain and the like, and limited knowledge about the environment and other aircrafts embedded in onboard equipment on each aircraft is set. Given the target list, the drone needs to execute under certain constraints (flight, fuel, etc. limits).

Disclosure of Invention

The invention aims to provide a task planning method for cooperative fight against the ground by unmanned aerial vehicles, which aims to solve the problems in the background technology.

In order to achieve the purpose, the invention provides the following technical scheme:

a mission planning method for cooperative fight against earth by unmanned aerial vehicles comprises the following steps:

s1, battlefield environment modeling: fusing original topographic features and threat information of the combat space into comprehensive topographic information by adopting a digital map information fusion principle;

s2, acquiring the number of the unmanned aerial vehicles, the number of the unmanned aerial vehicles and the number of targets: the ground-to-ground strike formation comprises 1 unmanned aerial vehicle and m unmanned aerial vehicles (v)₁,v₂,…,v_m) N targets to be hit are found in total (t)₁,t₂,…,t_n)；

S3, establishing a task distribution objective function: the establishment of the target function comprehensively considers three factors, including the hitting value of the target, the track cost of going to the target and the threat cost in the cruising process;

strike value: the target striking value reflects the importance degree of the target, and the unmanned aerial vehicle strikes the target with high value preferentially by V (t)_j) Representing a target t_jThe strike value of;

V(t_j)＝P_jR_j

wherein, P_jIs a target t_jProbability function attacked, R_jIs a target t_jIs attacked priority value, P_ijIs unmanned plane v_iHit the target t_jEfficiency of, i.e. v_iCan successfully hit t_jThe probability of (d);

flight path cost: keeping and estimating the track distance from the unmanned aerial vehicle to the target, namely the track cost according to the three-dimensional terrain following and the flight of the unmanned aerial vehicle;

third, threat cost: unmanned aerial vehicle is at the flight in-process, and the longer the probability of being destroyed that exposes under enemy's threat is just bigger, and it is along L flight in-process to establish unmanned aerial vehicle, receives the threat probability in the task scene and can show as:

wherein the content of the first and second substances,

representing the threat probability of the drone being threatened by the ith radar in the mission scenario,

the probability of threat of the jth ground-air missile in a task scene on the unmanned aerial vehicle is represented, the formula is represented as an integral form of a flight path L, and in an actual situation, a plurality of points can be sampled on a flight path point to save time in a discretization mode according to the calculation time requirement;

s4, setting task allocation constraint conditions:

task allocation balance constraint: the task allocation can allocate a plurality of targets for one unmanned aerial vehicle, and also can allocate one target to a plurality of unmanned aerial vehicles for joint execution, so that the task allocation is uniformly restricted for improving the execution efficiency of the task;

wherein n is_maxV to nobody_iMaximum number of targets, m, allocated_maxTo give a target t_jA maximum number of allocated drones;

the maximum range of the unmanned aerial vehicle is as follows: the range of the unmanned aerial vehicle is not greater than the maximum range:

L≤L_max

wherein L is_maxIs the maximum range of the single unmanned plane;

③ striking the target without repetition

Wherein, theta_iAnd theta_jIs a target set allocated to the ith and jth unmanned planes;

s5, solving task allocation based on a contract network algorithm;

the method comprises the steps that a drone is used as a host bidder in a task allocation process based on a contract net algorithm, after a batting target is determined and battlefield situation information is obtained, the bidder randomly generates a bidding sequence for each unmanned aerial vehicle and publishes a target list needing batting, auction starts, each unmanned aerial vehicle constructs all possible target batting plans according to the target list and calculates profits of the plans, when the bidding is completed, the unmanned aerial vehicle selects an optimal plan from the target attack plan list according to a greedy principle and plays a bidding as a batting plan of the unmanned aerial vehicle, all the unmanned aerial vehicles play the bidding according to the bidding sequence, after all the unmanned aerial vehicles play the bidding, the bidding in the current round is completed, a target allocation plan for unmanned aerial vehicle formation is obtained, if time or resource limitation is not reached, a new bidding sequence randomly generated by the bidder performs a new round of bidding so as to seek a better solution, when the time is out or the resource exceeds the limit, the algorithm stops;

s6, consider the population initializer gene codes: selecting a real number coding mode, and expressing chromosome genes by using track point coordinates;

s7, according to the task distribution result, each unmanned aerial vehicle carries out flight path planning, and a flight path cost function is established:

if the unmanned aerial vehicle starts from the initial position O and reaches the target position G after passing through N-1 nodes, the track cost of the unmanned aerial vehicle passing through the track can be expressed as follows:

J＝ω₁f_l+ωf_h+ω₃f_d

wherein the content of the first and second substances,

representing the flight cost as the sum of the distances of adjacent flight path segments, d_i,i+1Representing the distance between two adjacent track points;

f_h═ hdl denotes the altitude penalty, which denotes the integral of the altitude over the flight path, in order to make the drone fly as close to the ground as possible,

representing threat cost, wherein the position of the ith track point is subjected to the threat cost of the jth threat point, and carrying out gene coding; selecting a real number coding mode, and expressing chromosome genes by using track point coordinates;

s8, setting a track planning constraint condition:

the minimum flight path segment constraint: in order to avoid the waste of oil consumption caused by frequent turning and fluctuation of the unmanned aerial vehicle, the minimum flight path section, namely the distance that the unmanned aerial vehicle must keep flying before changing the current attitude, is limited, and p is used_iRepresents the ith track segment, l_iIs the length of flight path segment l of the ith flight path segment of the unmanned plane_minIs the minimum track segment length;

l_i≥l_min,i＝1,2,3…n

the lowest flight height constraint: the unmanned plane is flying at a position close to the ground as far as possible, but the height of the unmanned plane is not too low to cause impact on the ground, h_iRepresents the flight height h of the ith flight path_minIndicating the lowest flight height, then

h_i≥h_min,i＝1,2,3…n

Third, maximum turning angle constraint: because the restriction of self mobility, unmanned aerial vehicle can only turn certain angle when turning, so will carry out the restraint of maximum turn angle, be not more than maximum turn angle just can realize flying to next track point, the turn angle is the smaller also relatively steady of flying, and the horizontal projection of establishing ith section track is a_i＝(x_i-x_i-1,y_i-y_i-1) And the maximum turning angle of the unmanned aerial vehicle is theta:

fourthly, maximum climbing angle constraint: similar to the maximum turning angle, because self climbing and diving performance constraint limit, the maximum angle of the unmanned aerial vehicle is limited when climbing or descending, and the height difference of the flight path section i in the longitudinal direction is set to be | z_i-z_i-1L, maximum pitch angle of unmanned aerial vehicle is

Then:

s9, selecting a parent individual by adopting a roulette method;

s10, crossover operation: the crossover operator operation divides the two individuals into two parts at random, the first half part of the individual 1 is combined with the second half part of the individual 2, and the second half part of the individual 1 is combined with the first half part of the individual 1 to generate two brand new individuals;

s11, mutation operation: the mutation operation refers to randomly selecting a single or multiple gene positions in the chromosome for the filial generation after crossing, and performing mutation on the gene values of the positions, wherein the mutation operation can improve the local search capability of the algorithm, and when the crossed individuals approach the optimal solution of the problem, the mutation action is needed to adjust the gene position values of the individual parts, so that the individuals approach the optimal solution; secondly, the phenomenon of premature population is prevented, the diversity of the population is kept, a new individual coding structure can be generated through variation, premature is effectively avoided, the variation inside the flight path from the perspective of the flight path is changed, namely the coordinate value is changed, and the variation operation can assist to generate new individuals and influence the local searching capability of the genetic algorithm;

inserting an operator: when the track section passes through a dangerous area or violates the lowest track height, randomly inserting a new track node between two adjacent nodes in the track;

operation operator is deleted: if the unmanned aerial vehicle flight path does not meet the flight constraint, deleting the intermediate node of the flight path;

exchanging operation operators: the sequence of any two adjacent nodes in the switching flight path can be reduced, the turning angle can be reduced through a 2-opt algorithm of local search, and if the fitness of a new path obtained after the switching operation is greater than that of an original path, the path is updated;

fourthly, disturbance operator: randomly changing the coordinate value of a track node, determining the disturbance range according to whether the original track is feasible, and if the original track is feasible, carrying out small-range disturbance to ensure that the track is still feasible after operation; otherwise, the disturbance range should be properly enlarged, and the flight path enters a feasible region through disturbance operation, so that the fitness of the flight path can be improved;

smooth operation operator: smoothing nodes of which the flight path transit angles do not meet the unmanned aerial vehicle yaw angle constraint, namely selecting a certain node in the flight path, inserting a new node into two adjacent flight path sections of the node to replace the original node, and removing sharp angles of the flight path through smoothing operation;

s12, population updating is carried out through steps S10 and S11:

replacing parent individuals by filial generations generated through cross variation, and storing individuals with high fitness in the parent to complete population updating;

s13, looping steps S6-S12, and outputting an optimal track when the iteration times are met;

s14, local track planning is carried out on the new threat appearing in the environment;

in case of sudden threat, firstly erasing local flight paths exposed in an environment change area, performing two-dimensional flight path planning, then performing height planning to process the local flight paths, and splicing the local flight paths with original flight paths to obtain adjusted flight paths;

s15, performing two-dimensional flight path planning of flight path re-planning by an A-star algorithm, and limiting a search space;

let the minimum step length be l_minThe maximum turning angle is theta, the expansion area of the sparse A-star algorithm is a fan-shaped area, the expansion angle is 2 theta, and the expansion radius is l_minIf the expansion area is divided into N equal parts, the expansion point is N + 1;

s16, performing two-dimensional flight path planning of flight path re-planning by an A-star algorithm, and establishing a cost function;

f(n)＝g(n)+h(n)

wherein n is a node to be expanded, g (n) is the real cost from the starting point to the current node, h (n) is a heuristic function representing the cost estimation value from the current node n to the target node, and f (n) is an evaluation function of the node to be expanded, representing the cost estimation required by a certain route passing through the track node n;

s17, performing two-dimensional path planning of path re-planning by an A-algorithm, and expanding the next node with the minimum cost according to the steps S14 and S15 until the target is selected as an expanded node;

s18, endowing each track point in the re-planned two-dimensional track with a proper height value to obtain a feasible three-dimensional track;

s19: analyzing and contrasting the obtained three-dimensional flight path to obtain three groups of contrast analysis graphs, listing differences in the three groups of contrast analysis graphs, and analyzing the differences to obtain an optimal three-dimensional flight path;

further, in S1, the method for fusing the original topographic features and the threat information of the battle space into the integrated topographic information by using the digital map information fusion principle includes:

selecting a real terrain from a digital terrain elevation database by adopting a digital elevation map with a regular network structure, and obtaining an original digital elevation map through interpolation processing;

the method is equivalent to a three-dimensional threat source map aiming at radar, air defense fire and zones which cannot be crossed;

carrying out information fusion on the original digital map and the threat equivalent digital map to generate an equivalent digital map;

further, in S3, the estimating the flight path by using the terrain following and the drone flight maintaining relay includes:

the flight limitation is not considered, and an approximate three-dimensional flight path is obtained by utilizing terrain information following and unmanned aerial vehicle flight maintaining on a vertical section where the unmanned aerial vehicle and the target are positioned, namely the unmanned aerial vehicle v passes through on a three-dimensional comprehensive equivalent map_iLocation and target t_jAnd (3) making a tangent plane perpendicular to a horizontal plane at the position, and planning a flight path meeting the terrain following and flight height limit by taking a line of the tangent plane intersected with the terrain as a reference of an estimated flight path, wherein the length of the flight path is the flight path cost.

Further, in S3, the task allocation using the contract net algorithm includes:

the target attack plan of the unmanned aerial vehicle is defined as an ordered set of targets, the targets are attacked in sequence by the unmanned aerial vehicle, the track cost and the threat cost between the unmanned aerial vehicle and different target points are different, the benefits of the unmanned aerial vehicle executing the tasks in different sequences are also different, and in the task allocation process, each computing node makes a decision based on local information, so that each unmanned aerial vehicle sorts the task sequence according to the principle of maximizing self effectiveness, and t { t ═ t is set for the initial targets₁,t₂…,t_nBased on the limits of the most quota task, we can build all target attack plans, e.g., if v_iCan attack 2 targets at maximum, then the attack plan sequence can be constructed as { { t { [ T ]₁},{t₂}…{t_n},{t₁,t₂},{t₂,t₁}…,{t_n-1,t_n}}。

In the process of constructing the attack plan, in order to avoid excessive calculation and screen out some unrealistic plans, the set of task ordering schemes is assumed as

v_iScheme M selected based on the above principle_i＝{t_i1,t_i2,…,t_ilThe effectiveness of the method is as follows:

in the auction process, after an unmanned aerial vehicle bids for an attack plan of the unmanned aerial vehicle according to the bidding sequence, the value of the allocated target is reduced, other unmanned aerial vehicles need to update the current values of all targets and recalculate the bidding validity function, then the unmanned aerial vehicle bids the attack plan according to the updated income, the situation that the unmanned aerial vehicle attacks the same target excessively can be avoided, higher global target income can be obtained, and the situation that the unmanned aerial vehicle v attacks the same target is assumed_iThe target t is obtained in this round of auction_jThen each drone offering then follows the formula:

Value_new(t_j)＝(1-P_ij)*Value_old(t_j)

updating t_jAnd when it is a turn to bidding to obtain a more reasonable attack plan, bidding using the efficacy function calculated with the new target value.

Further, in S16, the designing g (n) of the heuristic function includes:

g (n) represents the actual cost of the drone at the spatial current node n:

g(n)＝ω_LL_n+ω_TT_n

representing the flight distance, which is the sum of the distances of the adjacent flight path segments, assuming that the starting point S is the 0 th starting point, the current node is the Nth route point, d_i,i+1Is the distance between two adjacent track points.

Representing the threat cost, the threat cost of the ith track point position being suffered by the jth threat point, omega₁And ω₁Weight representing flight distance cost and threat cost, and meeting omega₁+ω₂＝1；

Setting a heuristic function h (n) as a current node n (x) of the unmanned aerial vehicle_n,y_n) To the target node G (x)_G,y_G) The Euclidean distance of;

further, in S18, the planning of the altitude includes:

assume that the reference track overlapping the environment change region is S_3D(s₁,s₂,…,s_N) The number of track points is N, and the height corresponding to the ith track point is H_iThe two-dimensional track re-planned by the bypass threat is P_2D＝(p₁,p₂,…,p_n) The number of track points is n, and the height corresponding to the jth track point is h_j，h_jThe values of (A) are as follows:

wherein

The expression is to be taken to the whole,

t ═ i% k represents the surplus, and for making the flight path flyable, consider the maximum angle limit of climbing of unmanned aerial vehicle, want to guarantee

Wherein s is the step length of the A-x algorithm, and alpha is the maximum climbing angle limit of the unmanned aerial vehicle.

Further, in S19, the three-dimensional trajectory is subjected to a comparison analysis by software, marked on the software, and the marked three-dimensional trajectory is printed.

Compared with the prior art, the invention has the beneficial effects that:

on the basis of analyzing advantages and disadvantages of three typical manned/unmanned aerial vehicle collaborative control modes, namely, complete centralized type, limited centralized distributed type and centerless distributed type, the invention provides a control mode of a distributed manned/unmanned aerial vehicle formation in a limited set as a basic mode for research, and a manned/unmanned aerial vehicle mission planning system is divided into two aspects of mission allocation and flight path planning to be researched respectively. According to the characteristics of a distributed control mode in a finite set, the invention designs a task distribution objective function, wherein a decision of a pilot for participating in target priority is introduced; constructing an equivalent three-dimensional digital map of a battle space fusing terrain and ground threats; a three-dimensional estimated voyage mode is used, and a three-dimensional estimated voyage which is closer to a real battlefield environment is obtained by adopting a tangent plane terrain following method; on the basis, a task allocation solving method based on a contract network algorithm is used, so that a person serves as a host bidding person for task allocation to supervise and authorize a task allocation process, an actual combat mode is met, a track planning task is decomposed into a reference track planning part and an online track re-planning part, and the real-time requirement of the reference track planning is low.

The invention provides an unmanned aerial vehicle benchmark track planning method by introducing unmanned aerial vehicle maneuvering performance constraint in a mutation operator on the basis of a genetic algorithm capable of realizing global optimization; aiming at the characteristic of high real-time requirement in online flight path planning, the re-planning method divides the re-planning into two independent modules of two-dimensional flight path planning and height planning, and compared with the method for directly planning a three-dimensional space, the re-planning method reduces the search space and greatly saves time.

Drawings

FIG. 1 illustrates a finite concentrated distributed lower combat organizational structure in accordance with the present invention;

FIG. 2 is a schematic view of the range estimation section in step S3 according to the present invention;

FIG. 3 is a flowchart of the task assignment algorithm in step S2 of the present invention;

FIG. 4 is a schematic diagram illustrating the interleaving operation of step S10 in the embodiment of the present invention;

FIG. 5 is a schematic diagram illustrating a variation operation of step S11 according to an embodiment of the present invention;

FIG. 6 is a schematic diagram of a genetic algorithm for track planning in an embodiment of the invention;

FIG. 7 is an expanded view of step S15 in the embodiment of the present invention;

FIG. 8 illustrates an original three-dimensional topographical view of an embodiment of the present invention;

FIG. 9 illustrates a threat equivalence map in an embodiment of the invention;

FIG. 10 illustrates a three-dimensional terrain map after data fusion in an embodiment of the present invention;

FIG. 11 is a diagram illustrating task allocation in an embodiment of the present invention;

FIG. 12 is a diagram illustrating overall performance of task allocation in an embodiment of the present invention;

FIG. 13 is a graph illustrating the overall performance variation in an embodiment of the present invention;

FIG. 14 illustrates a raw track schematic in an embodiment of the present invention;

FIG. 15 is a schematic diagram illustrating a threat outbreak in an embodiment of the present invention;

FIG. 16 is a schematic diagram illustrating a re-planning of a flight path in an embodiment of the 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.

In order to implement a mission planning method aiming at cooperative combat of manned unmanned aerial vehicles on the ground, a PC (personal computer) with an internal memory of Intel Core i52.7GHz and 8G in an operating environment is matched, and a simulation platform is MATLAB2016 a.

A mission planning method for cooperative fight against earth by unmanned aerial vehicles comprises the following steps:

s1, battlefield environment modeling: fusing original topographic features and threat information of the combat space into comprehensive topographic information by adopting a digital map information fusion principle;

s2, acquiring the number of the unmanned aerial vehicles, the number of the unmanned aerial vehicles and the number of targets: the ground-to-ground strike formation comprises 1 unmanned aerial vehicle and m unmanned aerial vehicles (v)₁,v₂,…,v_m) N targets to be hit are found in total (t)₁,t₂,…,t_n)；

S3, establishing a task distribution objective function: the establishment of the target function comprehensively considers three factors, including the hitting value of the target, the track cost of going to the target and the threat cost in the cruising process;

strike value: the target striking value reflects the importance degree of the target, and the unmanned aerial vehicle strikes the target with high value preferentially by V (t)_j) Representing a target t_jThe strike value of;

V(t_j)＝P_jR_j

wherein, P_jIs a target t_jProbability function attacked, R_jIs a target t_jIs attacked priority value, P_ijIs unmanned plane v_iHit the target t_jEfficiency of, i.e. v_iCan successfully hit t_jThe probability of (d);

flight path cost: keeping and estimating the track distance from the unmanned aerial vehicle to the target, namely the track cost according to the three-dimensional terrain following and the flight of the unmanned aerial vehicle;

third, threat cost: unmanned aerial vehicle is at the flight in-process, and the longer the probability of being destroyed that exposes under enemy's threat is just bigger, and it is along L flight in-process to establish unmanned aerial vehicle, receives the threat probability in the task scene and can show as:

wherein the content of the first and second substances,

representing the threat probability of the drone being threatened by the ith radar in the mission scenario,

the probability of threat of the jth ground-air missile in a task scene on the unmanned aerial vehicle is represented, the formula is represented as an integral form of a flight path L, and in an actual situation, a plurality of points can be sampled on a flight path point to save time in a discretization mode according to the calculation time requirement;

s4, setting task allocation constraint conditions:

task allocation balance constraint: the task allocation can allocate a plurality of targets for one unmanned aerial vehicle, and also can allocate one target to a plurality of unmanned aerial vehicles for joint execution, so that the task allocation is uniformly restricted for improving the execution efficiency of the task;

wherein n is_maxV to nobody_iMaximum number of targets, m, allocated_maxTo give a target t_jA maximum number of allocated drones;

the maximum range of the unmanned aerial vehicle is as follows: the range of the unmanned aerial vehicle is not greater than the maximum range:

L≤L_max

wherein L is_maxIs the maximum range of the single unmanned plane;

③ striking the target without repetition

Wherein, theta_iAnd theta_jIs a target set allocated to the ith and jth unmanned planes;

s5, solving task allocation based on a contract network algorithm;

the method comprises the steps that a drone is used as a host bidder in a task allocation process based on a contract net algorithm, after a batting target is determined and battlefield situation information is obtained, the bidder randomly generates a bidding sequence for each unmanned aerial vehicle and publishes a target list needing batting, auction starts, each unmanned aerial vehicle constructs all possible target batting plans according to the target list and calculates profits of the plans, when the bidding is completed, the unmanned aerial vehicle selects an optimal plan from the target attack plan list according to a greedy principle and plays a bidding as a batting plan of the unmanned aerial vehicle, all the unmanned aerial vehicles play the bidding according to the bidding sequence, after all the unmanned aerial vehicles play the bidding, the bidding in the current round is completed, a target allocation plan for unmanned aerial vehicle formation is obtained, if time or resource limitation is not reached, a new bidding sequence randomly generated by the bidder performs a new round of bidding so as to seek a better solution, when the time is out or the resource exceeds the limit, the algorithm stops;

s6, consider the population initializer gene codes: selecting a real number coding mode, and expressing chromosome genes by using track point coordinates;

s7, according to the task distribution result, each unmanned aerial vehicle carries out flight path planning, and a flight path cost function is established:

if the unmanned aerial vehicle starts from the initial position O and reaches the target position G after passing through N-1 nodes, the track cost of the unmanned aerial vehicle passing through the track can be expressed as follows:

J＝ω₁f_l+ωf_h+ω₃f_d

wherein the content of the first and second substances,

representing the flight cost as the sum of the distances of adjacent flight path segments, d_i,i+1Representing the distance between two adjacent track points;

f_h═ hdl denotes the altitude penalty, which denotes the integral of the altitude over the flight path, in order to make the drone fly as close to the ground as possible,

representing threat cost, wherein the position of the ith track point is subjected to the threat cost of the jth threat point, and carrying out gene coding; selecting a real number coding mode, and expressing chromosome genes by using track point coordinates;

s8, setting a track planning constraint condition:

the minimum flight path segment constraint: in order to avoid the waste of oil consumption caused by frequent turning and fluctuation of the unmanned aerial vehicle, the minimum flight path section, namely the distance that the unmanned aerial vehicle must keep flying before changing the current attitude, is limited, and p is used_iRepresents the ith track segment, l_iIs the length of flight path segment l of the ith flight path segment of the unmanned plane_minIs the minimum track segment length;

l_i≥l_min,i＝1,2,3…n

the lowest flight height constraint: the unmanned plane is flying at a position close to the ground as far as possible, but the height of the unmanned plane is not too low to cause impact on the ground, h_iRepresents the flight height h of the ith flight path_minIndicating the lowest flight height, then

h_i≥h_min,i＝1,2,3…n

Third, maximum turning angle constraint: because the restriction of self mobility, unmanned aerial vehicle can only turn certain angle when turning, so will carry out the restraint of maximum turn angle, be not more than maximum turn angle just can realize flying to next track point, the turn angle is the smaller also relatively steady of flying, and the horizontal projection of establishing ith section track is a_i＝(x_i-x_i-1,y_i-y_i-1) And the maximum turning angle of the unmanned aerial vehicle is theta:

maximum climbing angleBundling: similar to the maximum turning angle, because self climbing and diving performance constraint limit, the maximum angle of the unmanned aerial vehicle is limited when climbing or descending, and the height difference of the flight path section i in the longitudinal direction is set to be | z_i-z_i-1L, maximum pitch angle of unmanned aerial vehicle is

Then:

s9, selecting a parent individual by adopting a roulette method;

s10, crossover operation: the crossover operator operation divides the two individuals into two parts at random, the first half part of the individual 1 is combined with the second half part of the individual 2, and the second half part of the individual 1 is combined with the first half part of the individual 1 to generate two brand new individuals;

s11, mutation operation: the mutation operation refers to randomly selecting a single or multiple gene positions in the chromosome for the filial generation after crossing, and performing mutation on the gene values of the positions, wherein the mutation operation can improve the local search capability of the algorithm, and when the crossed individuals approach the optimal solution of the problem, the mutation action is needed to adjust the gene position values of the individual parts, so that the individuals approach the optimal solution; secondly, the phenomenon of premature population is prevented, the diversity of the population is kept, a new individual coding structure can be generated through variation, premature is effectively avoided, the variation inside the flight path from the perspective of the flight path is changed, namely the coordinate value is changed, and the variation operation can assist to generate new individuals and influence the local searching capability of the genetic algorithm;

inserting an operator: when the track section passes through a dangerous area or violates the lowest track height, randomly inserting a new track node between two adjacent nodes in the track;

operation operator is deleted: if the unmanned aerial vehicle flight path does not meet the flight constraint, deleting the intermediate node of the flight path;

exchanging operation operators: the sequence of any two adjacent nodes in the switching flight path can be reduced, the turning angle can be reduced through a 2-opt algorithm of local search, and if the fitness of a new path obtained after the switching operation is greater than that of an original path, the path is updated;

fourthly, disturbance operator: randomly changing the coordinate value of a track node, determining the disturbance range according to whether the original track is feasible, and if the original track is feasible, carrying out small-range disturbance to ensure that the track is still feasible after operation; otherwise, the disturbance range should be properly enlarged, and the flight path enters a feasible region through disturbance operation, so that the fitness of the flight path can be improved;

smooth operation operator: smoothing nodes of which the flight path transit angles do not meet the unmanned aerial vehicle yaw angle constraint, namely selecting a certain node in the flight path, inserting a new node into two adjacent flight path sections of the node to replace the original node, and removing sharp angles of the flight path through smoothing operation;

s12, population updating is carried out through steps S10 and S11:

replacing parent individuals by filial generations generated through cross variation, and storing individuals with high fitness in the parent to complete population updating;

s13, looping steps S6-S12, and outputting an optimal track when the iteration times are met;

s14, local track planning is carried out on the new threat appearing in the environment;

in case of sudden threat, firstly erasing local flight paths exposed in an environment change area, performing two-dimensional flight path planning, then performing height planning to process the local flight paths, and splicing the local flight paths with original flight paths to obtain adjusted flight paths;

s15, performing two-dimensional flight path planning of flight path re-planning by an A-star algorithm, and limiting a search space;

let the minimum step length be l_minThe maximum turning angle is theta, the expansion area of the sparse A-star algorithm is a fan-shaped area, the expansion angle is 2 theta, and the expansion radius is l_minIf the expansion area is divided into N equal parts, the expansion point is N + 1;

s16, performing two-dimensional flight path planning of flight path re-planning by an A-star algorithm, and establishing a cost function;

f(n)＝g(n)+h(n)

wherein n is a node to be expanded, g (n) is the real cost from the starting point to the current node, h (n) is a heuristic function representing the cost estimation value from the current node n to the target node, and f (n) is an evaluation function of the node to be expanded, representing the cost estimation required by a certain route passing through the track node n;

s17, performing two-dimensional path planning of path re-planning by an A-algorithm, and expanding the next node with the minimum cost according to the steps S14 and S15 until the target is selected as an expanded node;

s18, endowing each track point in the re-planned two-dimensional track with a proper height value to obtain a feasible three-dimensional track;

s19: analyzing and contrasting the obtained three-dimensional flight path to obtain three groups of contrast analysis graphs, listing differences in the three groups of contrast analysis graphs, and analyzing the differences to obtain an optimal three-dimensional flight path;

synthetic terrain information comprising:

selecting a real terrain from a digital terrain elevation database by adopting a digital elevation map with a regular network structure, and obtaining an original digital elevation map through interpolation processing;

the method is equivalent to a three-dimensional threat source map aiming at radar, air defense fire and zones which cannot be crossed;

and carrying out information fusion on the original digital map and the threat equivalent digital map to generate the equivalent digital map.

In S3, the estimating the track using the terrain following and the unmanned aerial vehicle flight maintaining relay includes:

the flight limitation is not considered, and an approximate three-dimensional flight path is obtained by utilizing terrain information following and unmanned aerial vehicle flight maintaining on a vertical section where the unmanned aerial vehicle and the target are positioned, namely the unmanned aerial vehicle v passes through on a three-dimensional comprehensive equivalent map_iLocation and target t_jAnd (3) making a tangent plane perpendicular to a horizontal plane at the position, and planning a flight path meeting the terrain following and flight height limit by taking a line of the tangent plane intersected with the terrain as a reference of an estimated flight path, wherein the length of the flight path is the flight path cost.

In S3, the task allocation using the contract network algorithm includes:

unmanned aerial vehicleThe target attack plan is defined as an ordered set of targets, which represents that the unmanned aerial vehicle attacks the targets in sequence, because the track cost and the threat cost from the unmanned aerial vehicle to different target points are different, the benefits of the unmanned aerial vehicle executing the tasks in different sequences are also different, and in the task allocation process, each computing node makes a decision based on local information, so that each unmanned aerial vehicle sorts the task sequence according to the principle of maximizing the effectiveness of the unmanned aerial vehicle, and sets t as { t ═ t for the initial targets₁,t₂…,t_nBased on the limits of the most quota task, we can build all target attack plans, e.g., if v_iCan attack 2 targets at maximum, then the attack plan sequence can be constructed as { { t { [ T ]₁},{t₂}…{t_n},{t₁,t₂},{t₂,t₁}…,{t_n-1,t_n}}。

In the process of constructing the attack plan, in order to avoid excessive calculation and screen out some unrealistic plans, the set of task ordering schemes is assumed as

v_iScheme M selected based on the above principle_i＝{t_i1,t_i2,…,t_ilThe effectiveness of the method is as follows:

in the auction process, after an unmanned aerial vehicle bids for an attack plan of the unmanned aerial vehicle according to the bidding sequence, the value of the allocated target is reduced, other unmanned aerial vehicles need to update the current values of all targets and recalculate the bidding validity function, then the unmanned aerial vehicle bids the attack plan according to the updated income, the situation that the unmanned aerial vehicle attacks the same target excessively can be avoided, higher global target income can be obtained, and the situation that the unmanned aerial vehicle v attacks the same target is assumed_iThe target t is obtained in this round of auction_jThen each drone offering then follows the formula:

Value_new(t_j)＝(1-P_ij)*Value_old(t_j)

updating t_jAnd when it is a turn to bidding to obtain a more reasonable attack plan, bidding using the efficacy function calculated with the new target value.

In S16, the design of g (n) of the heuristic function includes:

g (n) represents the actual cost of the drone at the spatial current node n:

g(n)＝ω_LL_n+ω_TT_n

representing the flight distance, which is the sum of the distances of the adjacent flight path segments, assuming that the starting point S is the 0 th starting point, the current node is the Nth route point, d_i,i+1Is the distance between two adjacent track points.

Representing the threat cost, the threat cost of the ith track point position being suffered by the jth threat point, omega₁And ω₁Weight representing flight distance cost and threat cost, and meeting omega₁+ω₂＝1；

Setting a heuristic function h (n) as a current node n (x) of the unmanned aerial vehicle_n,y_n) To the target node G (x)_G,y_G) The Euclidean distance of;

in S18, the planning of the altitude includes:

assume that the reference track overlapping the environment change region is S_3D(s₁,s₂,…,s_N) The number of track points is N, and the height corresponding to the ith track point is H_iThe two-dimensional track re-planned by the bypass threat is P_2D＝(p₁,p₂,…,p_n) The number of track points is n, and the height corresponding to the jth track point is h_j，h_jThe values of (A) are as follows:

wherein

The expression is to be taken to the whole,

t ═ i% k represents the surplus, and for making the flight path flyable, consider the maximum angle limit of climbing of unmanned aerial vehicle, want to guarantee

Wherein s is the step length of the A-x algorithm, and alpha is the maximum climbing angle limit of the unmanned aerial vehicle.

In S19, the three-dimensional trajectory is subjected to a comparison analysis by software, marked on the software, and printed.

In this embodiment, a real terrain is selected from the digital terrain elevation database, the planning space is 200km by 200km, and interpolation processing is performed to obtain a graph shown in fig. 8; simplifying a radar and ground-air missile threat model, and expressing the model by Gaussian distribution, wherein the expression is as follows:

wherein x and y represent coordinate values of the threat projected onto a horizontal plane, z_iIs the elevation value corresponding to the threat; x is the number of_iAnd y_iCoordinates, x, representing the ith center of threat_siAnd y_siRepresents the attenuation, h, of the ith threat along the x-axis and y-axis_iRepresenting the action intensity of the ith threat;

the anti-aircraft gun is equivalent to a hemisphere, and the threat equivalent map is as shown in fig. 9; carrying out information fusion on the original digital map and the threat equivalent digital map to generate a three-dimensional topographic map after data fusion, as shown in FIG. 10;

human-machine, unmanned aerial vehicle and target number, human-machine position and target position are obtained according to the task,

TABLE 1 initial position coordinates of unmanned aerial vehicle

TABLE 2 initial position coordinates of the target

Calculating the threat cost of the unmanned aerial vehicle, wherein the unmanned aerial vehicle is in the no-fly zone R_minThe probability of being found when the outer range radar antenna is at a distance R decays at a rate of 4 times the distance, with the probability approximating:

wherein K is a probability attenuation coefficient;

the effective killing probability formula of the ground-air missile is approximately expressed as follows:

P_M＝K₀(Δh/R)

wherein K₀The method comprises the steps of representing the killing probability of a missile under the clear weather condition, generally regarding as a constant, and representing the height of an unmanned aerial vehicle relative to an air-ground missile position; r represents the radial distance between the unmanned aerial vehicle and the missile site;

calculating a target function of task allocation;

setting the maximum auction frequency of task allocation as 20 times, and carrying out contract network algorithm solution to obtain a task allocation schematic diagram as shown in FIG. 11 and a task allocation overall efficiency schematic diagram as shown in FIG. 12; as shown in fig. 13, the overall efficiency variation curve of the task allocation scheme shows that the overall efficiency is continuously increased along with the increase of the number of bidding times, after 15 bidding times, the overall efficiency is close to a fixed value, the whole process takes 2.651 seconds, and after the task allocation, each target finds the most suitable unmanned aerial vehicle to complete, the overall efficiency is maximized by maximizing each local efficiency, the time consumption is short, and the requirement of a rapid battlefield is met.

Setting the size of an initial main population to be 100, the size of an auxiliary population to be 100 and the size of a breeding pond to be 40 in the flight path planning; the cross probability is 0.7, and the mutation probability is 0.1; the maximum number of evolutions is 500; the maximum turning angle is 45 degrees, and the maximum pitch angle is 45 degrees; the minimum flying height is 10 meters.

The evolution operator and the fitness function constructed according to the method meet the feasibility of the unmanned aerial vehicle flight path, the flight height is not too high, the threat is avoided, the safety requirement is met, the objective function is gradually converged along with the increase of the iteration times, and the high flight path precision is obtained due to the high dimensionality of the initialized population.

In order to verify the performance of the mission re-planning of the flight path, an unmanned aerial vehicle is arranged to execute a certain target, the unmanned aerial vehicle firstly flies along a reference flight path marked by a genetic algorithm, two new threats are newly added on the flight path, the threat 1 coordinate is (69,88) and the threat radius is 30, and the threat 2 coordinate is (143,177) and the threat radius is 40; the original track schematic diagram is shown in FIG. 14, the burst track schematic diagram is shown in FIG. 15, and the track re-planning schematic diagram is shown in FIG. 16.

The simulation graph shows that the flight path re-planning method provided by the invention can enable the unmanned aerial vehicle to avoid the threat, re-plan a flight path which bypasses the threat from the side, and after the unmanned aerial vehicle reaches the locally planned target point, the unmanned aerial vehicle returns to the original reference flight path again to continue flying to reach the final target point, thereby realizing the on-line flight path planning; and the planning takes 1.110 seconds, the three-dimensional sparse A-star algorithm is used for planning the flight path under the same condition, the time is taken for 2.335 seconds, the time is saved, and the requirement of the re-planning on the speed is met.

It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.

Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.

Claims (7)

1. A mission planning method for cooperative fight against the ground by unmanned aerial vehicles is characterized by comprising the following steps:

s1, battlefield environment modeling: fusing original topographic features and threat information of the combat space into comprehensive topographic information by adopting a digital map information fusion principle;

s2, acquiring the number of the unmanned aerial vehicles, the number of the unmanned aerial vehicles and the number of targets: the ground-to-ground strike formation comprises 1 unmanned aerial vehicle and m unmanned aerial vehicles (v)₁,v₂,…,v_m) In total, m targets (t) to be hit are found₁,t₂,…,t_n)；

S3, establishing a task distribution objective function: the establishment of the target function comprehensively considers three factors, including the hitting value of the target, the track cost of going to the target and the threat cost in the cruising process;

strike value: the target striking value reflects the importance degree of the target, and the unmanned aerial vehicle strikes the target with high value preferentially by V (t)_j) Representing a target t_jThe strike value of;

V(t_j)＝P_jR_j

wherein, P_jIs a target t_jProbability function attacked, R_jIs a target t_jIs attacked priority value, P_ijIs unmanned plane v_iHit the target t_jEfficiency of, i.e. v_iCan successfully hit t_jThe probability of (d);

flight path cost: keeping and estimating the track distance from the unmanned aerial vehicle to the target, namely the track cost according to the three-dimensional terrain following and the flight of the unmanned aerial vehicle;

third, threat cost: unmanned aerial vehicle is at the flight in-process, and the longer the probability of being destroyed that exposes under enemy's threat is just bigger, and it is along L flight in-process to establish unmanned aerial vehicle, receives the threat probability in the task scene and can show as:

wherein the content of the first and second substances,

representing the threat probability of the drone being threatened by the ith radar in the mission scenario,

the probability of threat of the jth ground-air missile in a task scene on the unmanned aerial vehicle is represented, the formula is represented as an integral form of a flight path L, and in an actual situation, a plurality of points can be sampled on a flight path point to save time in a discretization mode according to the calculation time requirement;

s4, setting task allocation constraint conditions:

task allocation balance constraint: the task allocation can allocate a plurality of targets for one unmanned aerial vehicle, and also can allocate one target to a plurality of unmanned aerial vehicles for joint execution, so that the task allocation is uniformly restricted for improving the execution efficiency of the task;

wherein n is_maxV to nobody_iMaximum number of targets, m, allocated_maxTo give a target t_jA maximum number of allocated drones;

the maximum range of the unmanned aerial vehicle is as follows: the range of the unmanned aerial vehicle is not greater than the maximum range:

L≤L_max

wherein L is_maxIs the maximum range of the single unmanned plane;

③ striking the target without repetition

Wherein, theta_iAnd theta_jIs a target set allocated to the ith and jth unmanned planes;

s5, solving task allocation based on a contract network algorithm;

the method comprises the steps that a drone is used as a host bidder in a task allocation process based on a contract net algorithm, after a batting target is determined and battlefield situation information is obtained, the bidder randomly generates a bidding sequence for each unmanned aerial vehicle and publishes a target list needing batting, auction starts, each unmanned aerial vehicle constructs all possible target batting plans according to the target list and calculates profits of the plans, when the bidding is completed, the unmanned aerial vehicle selects an optimal plan from the target attack plan list according to a greedy principle and plays a bidding as a batting plan of the unmanned aerial vehicle, all the unmanned aerial vehicles play the bidding according to the bidding sequence, after all the unmanned aerial vehicles play the bidding, the bidding in the current round is completed, a target allocation plan for unmanned aerial vehicle formation is obtained, if time or resource limitation is not reached, a new bidding sequence randomly generated by the bidder performs a new round of bidding so as to seek a better solution, when the time is out or the resource exceeds the limit, the algorithm stops;

s6, consider the population initializer gene codes: selecting a real number coding mode, and expressing chromosome genes by using track point coordinates;

s7, according to the task distribution result, each unmanned aerial vehicle carries out flight path planning, and a flight path cost function is established:

if the unmanned aerial vehicle starts from the initial position O and reaches the target position G after passing through N-1 nodes, the track cost of the unmanned aerial vehicle passing through the track can be expressed as follows:

J＝ω₁f_l+ωf_h+ω₃f_d

wherein the content of the first and second substances,

representing the flight cost as the sum of the distances of adjacent flight path segments, d_i,i+1Representing the distance between two adjacent track points;

f_h═ hdl denotes the altitude penalty, which denotes the integral of the altitude over the flight path, in order to make the drone fly as close to the ground as possible,

representing threat cost, wherein the position of the ith track point is subjected to the threat cost of the jth threat point, and carrying out gene coding; selecting a real number coding mode, and expressing chromosome genes by using track point coordinates;

s8, setting a track planning constraint condition:

the minimum flight path segment constraint: in order to avoid the waste of oil consumption caused by frequent turning and fluctuation of the unmanned aerial vehicle, the minimum flight path section, namely the distance that the unmanned aerial vehicle must keep flying before changing the current attitude, is limited, and p is used_iRepresents the ith track segment, l_iIs the length of flight path segment l of the ith flight path segment of the unmanned plane_minIs the minimum track segment length;

l_i≥l_min,i＝1,2,3…n

the lowest flight height constraint: the unmanned plane is flying at a position close to the ground as far as possible, but the height of the unmanned plane is not too low to cause impact on the ground, h_iRepresents the flight height h of the ith flight path_minRepresents the lowestThe flying height of is

h_i≥h_min,i＝1,2,3…n

Third, maximum turning angle constraint: because the restriction of self mobility, unmanned aerial vehicle can only turn certain angle when turning, so will carry out the restraint of maximum turn angle, be not more than maximum turn angle just can realize flying to next track point, the turn angle is the smaller also relatively steady of flying, and the horizontal projection of establishing ith section track is a_i＝(x_i-x_i-1,y_i-y_i-1) And the maximum turning angle of the unmanned aerial vehicle is theta:

fourthly, maximum climbing angle constraint: similar to the maximum turning angle, because self climbing and diving performance constraint limit, the maximum angle of the unmanned aerial vehicle is limited when climbing or descending, and the height difference of the flight path section i in the longitudinal direction is set to be | z_i-z_i-1L, maximum pitch angle of unmanned aerial vehicle is

Then:

s9, selecting a parent individual by adopting a roulette method;

s10, crossover operation: the crossover operator operation divides the two individuals into two parts at random, the first half part of the individual 1 is combined with the second half part of the individual 2, and the second half part of the individual 1 is combined with the first half part of the individual 1 to generate two brand new individuals;

s11, mutation operation: the mutation operation refers to randomly selecting a single or multiple gene positions in the chromosome for the filial generation after crossing, and performing mutation on the gene values of the positions, wherein the mutation operation can improve the local search capability of the algorithm, and when the crossed individuals approach the optimal solution of the problem, the mutation action is needed to adjust the gene position values of the individual parts, so that the individuals approach the optimal solution; secondly, the phenomenon of premature population is prevented, the diversity of the population is kept, a new individual coding structure can be generated through variation, premature is effectively avoided, the variation inside the flight path from the perspective of the flight path is changed, namely the coordinate value is changed, and the variation operation can assist to generate new individuals and influence the local searching capability of the genetic algorithm;

inserting an operator: when the track section passes through a dangerous area or violates the lowest track height, randomly inserting a new track node between two adjacent nodes in the track;

operation operator is deleted: if the unmanned aerial vehicle flight path does not meet the flight constraint, deleting the intermediate node of the flight path;

exchanging operation operators: the sequence of any two adjacent nodes in the switching flight path can be reduced, the turning angle can be reduced through a 2-opt algorithm of local search, and if the fitness of a new path obtained after the switching operation is greater than that of an original path, the path is updated;

fourthly, disturbance operator: randomly changing the coordinate value of a track node, determining the disturbance range according to whether the original track is feasible, and if the original track is feasible, carrying out small-range disturbance to ensure that the track is still feasible after operation; otherwise, the disturbance range should be properly enlarged, and the flight path enters a feasible region through disturbance operation, so that the fitness of the flight path can be improved;

smooth operation operator: smoothing nodes of which the flight path transit angles do not meet the unmanned aerial vehicle yaw angle constraint, namely selecting a certain node in the flight path, inserting a new node into two adjacent flight path sections of the node to replace the original node, and removing sharp angles of the flight path through smoothing operation;

s12, population updating is carried out through steps S10 and S11:

replacing parent individuals by filial generations generated through cross variation, and storing individuals with high fitness in the parent to complete population updating;

s13, looping steps S6-S12, and outputting an optimal track when the iteration times are met;

s14, local track planning is carried out on the new threat appearing in the environment;

in case of sudden threat, firstly erasing local flight paths exposed in an environment change area, performing two-dimensional flight path planning, then performing height planning to process the local flight paths, and splicing the local flight paths with original flight paths to obtain adjusted flight paths;

s15, performing two-dimensional flight path planning of flight path re-planning by an A-star algorithm, and limiting a search space;

let the minimum step length be l_minThe maximum turning angle is theta, the expansion area of the sparse A-star algorithm is a fan-shaped area, the expansion angle is 2 theta, and the expansion radius is l_minIf the expansion area is divided into N equal parts, the expansion point is N + 1;

s16, performing two-dimensional flight path planning of flight path re-planning by an A-star algorithm, and establishing a cost function;

f(n)＝g(n)+h(n)

wherein n is a node to be expanded, g (n) is the real cost from the starting point to the current node, h (n) is a heuristic function representing the cost estimation value from the current node n to the target node, and f (n) is an evaluation function of the node to be expanded, representing the cost estimation required by a certain route passing through the track node n;

s17, performing two-dimensional path planning of path re-planning by an A-algorithm, and expanding the next node with the minimum cost according to the steps S14 and S15 until the target is selected as an expanded node;

s18, endowing each track point in the re-planned two-dimensional track with a proper height value to obtain a feasible three-dimensional track;

s19: and analyzing and contrasting the obtained three-dimensional flight path to obtain three groups of contrast analysis graphs, listing the differences in the three groups of contrast analysis graphs, and analyzing the differences to obtain the optimal three-dimensional flight path.

2. The mission planning method for cooperative combat of manned and unmanned aerial vehicles with respect to the ground according to claim 1, wherein in S1, the digital map information fusion principle is adopted to fuse the original topographic features and threat information of the combat space into comprehensive topographic information, which includes:

selecting a real terrain from a digital terrain elevation database by adopting a digital elevation map with a regular network structure, and obtaining an original digital elevation map through interpolation processing;

the method is equivalent to a three-dimensional threat source map aiming at radar, air defense fire and zones which cannot be crossed;

and carrying out information fusion on the original digital map and the threat equivalent digital map to generate the equivalent digital map.

3. The mission planning method for cooperative combat by human and unmanned aerial vehicles according to claim 1, wherein in S3, the estimation of the flight path by using terrain following and unmanned aerial vehicle flight maintaining relay comprises:

the flight limitation is not considered, and an approximate three-dimensional flight path is obtained by utilizing terrain information following and unmanned aerial vehicle flight maintaining on a vertical section where the unmanned aerial vehicle and the target are positioned, namely the unmanned aerial vehicle v passes through on a three-dimensional comprehensive equivalent map_iLocation and target t_jAnd (3) making a tangent plane perpendicular to a horizontal plane at the position, and planning a flight path meeting the terrain following and flight height limit by taking a line of the tangent plane intersected with the terrain as a reference of an estimated flight path, wherein the length of the flight path is the flight path cost.

4. The method according to claim 1, wherein in S3, the task allocation is performed by using a contract net algorithm, and the method comprises:

the target attack plan of the unmanned aerial vehicle is defined as an ordered set of targets, the targets are attacked in sequence by the unmanned aerial vehicle, the track cost and the threat cost between the unmanned aerial vehicle and different target points are different, the benefits of the unmanned aerial vehicle executing the tasks in different sequences are also different, and in the task allocation process, each computing node makes a decision based on local information, so that each unmanned aerial vehicle sorts the task sequence according to the principle of maximizing self effectiveness, and t { t ═ t is set for the initial targets₁,t₂…,t_nAccording to the limitation of the maximum quota task, all target attack plans can be established;

during the process of constructing the attack plan, the method comprises the steps ofThe method avoids the problems of excessive calculation and screening of some unrealistic plans, and assumes that the task ordering schemes are collected into a set

v_iScheme M selected based on the above principle_i＝{t_i1,t_i2,…,t_ilThe effectiveness of the method is as follows:

in the auction process, after an unmanned aerial vehicle bids for an attack plan of the unmanned aerial vehicle according to the bidding sequence, the value of the allocated target is reduced, other unmanned aerial vehicles need to update the current values of all targets and recalculate the bidding validity function, then the unmanned aerial vehicle bids the attack plan according to the updated income, the situation that the unmanned aerial vehicle attacks the same target excessively can be avoided, higher global target income can be obtained, and the situation that the unmanned aerial vehicle v attacks the same target is assumed_iThe target t is obtained in this round of auction_jThen each drone offering then follows the formula:

Value_new(t_j)＝(1-P_ij)*Value_old(t_j)

updating t_jAnd when it is a turn to bidding to obtain a more reasonable attack plan, bidding using the efficacy function calculated with the new target value.

5. The mission planning method for cooperative combat by unmanned aerial vehicle and manned unmanned aerial vehicle according to claim 1, wherein the step of designing the heuristic function in S16 in g (n) comprises:

g (n) represents the actual cost of the drone at the spatial current node n:

g(n)＝ω_LL_n+ω_TT_n

representing the flight distance, which is the sum of the distances of the adjacent flight path segments, assuming that the starting point S is the 0 th starting point, the current node is the Nth route point, d_i,i+1Is the distance between two adjacent track points;

representing the threat cost, the threat cost of the ith track point position being suffered by the jth threat point, omega₁And ω₁Weight representing flight distance cost and threat cost, and meeting omega₁+ω₂＝1；

Setting a heuristic function h (n) as a current node n (x) of the unmanned aerial vehicle_n,y_n) To the target node G (x)_G,y_G) The Euclidean distance of;

6. the mission planning method for cooperative combat by unmanned aerial vehicle to ground according to claim 1, wherein in said S18, the planning of altitude comprises:

assume that the reference track overlapping the environment change region is S_3D(s₁,s₂,…,s_N) The number of track points is N, and the height corresponding to the ith track point is H_iThe two-dimensional track re-planned by the bypass threat is P_2D＝(p₁,p₂,…,p_n) The number of track points is n, and the height corresponding to the jth track point is h_j，h_jThe values of (A) are as follows:

wherein

Represents rounding，

t ═ i% k represents the surplus, and for making the flight path flyable, consider the maximum angle limit of climbing of unmanned aerial vehicle, want to guarantee

Wherein s is the step length of the A-x algorithm, and alpha is the maximum climbing angle limit of the unmanned aerial vehicle.

7. The mission planning method for cooperative combat by unmanned aerial vehicle and manned unmanned aerial vehicle according to claim 1, wherein in S19, the three-dimensional trajectory is analyzed by software, marked on the software, and the marked three-dimensional trajectory is printed.

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