Engineering method suitable for multi-type target rapid strike task planning

Abstract

The invention relates to an engineering method suitable for rapid attack task planning of multiple types of targets, and belongs to the field of task planning of multiple unmanned aerial vehicles. Comprising the steps of constructing a multi-type target model; calculating the flight path length from each aircraft to each target according to the aircraft position and the target position, and determining the aircraft executing the attack task on the target according to the flight path length and the own capability of the aircraft; the ability of the aircraft comprises a residual voyage and a residual strike ability; establishing a strike task teaching model, screening a target with the minimum evaluation index as a currently selected optimal strike target according to a greedy algorithm, and performing an unmanned aerial vehicle sequence on the target according to the number of unmanned aerial vehicles required for performing strike on the target; and planning an air route for the unmanned aerial vehicle which is allocated to execute the attack task, and searching an optimal attack angle in a circular traversal mode by taking the shortest air range as a target. According to the method, the multi-unmanned aerial vehicle collaborative strike task flight path planning solving time is effectively shortened, and the engineering application capability of the algorithm is improved.

Description

Technical Field

[0001] The invention belongs to the field of multi-unmanned aerial vehicle mission planning, involves task allocation and track planning problems, and is an engineering method suitable for planning rapid strike missions for multiple types of targets. Background Art

[0002] Unmanned aerial vehicles (UAVs) play an increasingly important role in military and civilian fields, such as intelligence gathering, regional surveillance, environmental monitoring, search and rescue, etc. However, due to the limitations of the capabilities of a single UAV, it is difficult to complete complex combat missions. Therefore, the collaborative execution of a group of tasks by multiple UAVs has become one of the current and future research hotspots. In order to enable multiple UAVs to fully utilize their advantages, task allocation is required so that the UAVs can perform tasks in an orderly manner and maximize the performance of the UAV cluster.

[0003] In the field of scientific research, the task planning problem is usually modeled as the Traveling Salesman Problem (TSP), which is a classic combinatorial optimization problem. After mathematical modeling, it can be described as: a salesman has to go to several cities to sell products. He starts from one of the cities, passes through all the cities, and returns to the starting city. How to choose a route to make the total distance shortest? When solving such problems, the path is usually replaced by the Euclidean distance approximation. In the task planning problem, the more accurate and practical Dubins distance and other geometric calculation methods are usually used to determine the path length between the two.

[0004] From the perspective of the solution method of task planning problem: since this type of problem is an NP-complete problem, the exhaustive method is inefficient. The current mainstream method is to use some random and heuristic search algorithms, such as genetic algorithm, ant colony algorithm, simulated annealing algorithm, particle swarm algorithm, etc., but these algorithms have the same shortcomings, which is that they can only get infinitely close to the optimal solution, but cannot get the optimal solution. Moreover, such algorithms are greatly affected by initial conditions, etc., and the results obtained are uncertain. It is impossible to specifically judge the quality of the results, and it is difficult to apply them to actual engineering applications.

[0005] From the perspective of the applicable objects of mission planning: For mission planning problems in combat scenarios, targets are usually simply modeled as particles, which does not conform to the engineering application of mission planning problems. Since targets may have different spatial characteristics, behaviors, etc., it is necessary to subdivide the target types into point targets, line targets, and surface targets based on the relationship between the target size and the size of the fire control aiming field of view of the unmanned aerial vehicle. In the case of heterogeneous target types and different characteristics, the internal resources of multiple unmanned aerial vehicles are coordinated, and key factors such as strike angle, route length, number of route turning points, aircraft turning radius, and aircraft terminal guidance reserved lateral distance are considered. By optimizing the strike task allocation plan and shortening the total length of the flight path, the strike mission can be successfully completed.

[0006] Therefore, studying a method suitable for rapid strike mission planning for multiple types of targets and shortening the solution time of the planning problem while ensuring the smooth completion of the mission has very important theoretical and practical significance for the engineering application of the method. Summary of the invention

[0007] The technical problems to be solved by the present invention are:

[0008] For the multi-UAV strike mission scenario where there are heterogeneous targets such as points, lines, and surfaces in the environment, in order to achieve a comprehensive solution to the strike mission allocation and trajectory planning problems under the condition of free combination of heterogeneous targets, the present invention proposes an engineering method suitable for rapid strike mission planning of multi-type targets, which effectively shortens the trajectory planning solution time of multi-UAV coordinated strike missions and improves the engineering application capability of the algorithm.

[0009] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0010] An engineering method suitable for planning a rapid strike mission against multiple types of targets, characterized by comprising:

[0011] Build multi-type target models;

[0012] Calculating the track length of each aircraft to each target based on the aircraft position and the target position, and determining the aircraft to perform the strike mission on the target based on the track length and the aircraft's own capabilities; the aircraft's own capabilities include remaining range and remaining strike capability;

[0013] Establish a strike mission teaching model, select the target with the smallest evaluation index as the optimal strike target according to the greedy algorithm, and select the sequence of unmanned aerial vehicles to strike the target according to the number of drones required to strike the target;

[0014] The routes of the unmanned aerial vehicles that have been assigned to carry out strike missions are planned, and the optimal strike angle is found through a cyclic traversal method with the goal of shortest range.

[0015] A further technical solution of the present invention: the multi-type target model is specifically:

[0016] Τ j ={type j ,P jT ,r j ,value j ,V jT}

[0017] Among them, type j Target j The types of targets are divided into point targets, line targets and surface targets; jT Indicates j The position coordinates of the point target P jT The number of points is 1, and the line target is P jT The number of points is 2. If the surface target is a circular surface target, then P jT The number of points is 1. If it is a polygonal target, then P jT The number of points is the number of polygon vertices; r j Threat range for target, point target and line target j =0, circular target r j is the circle radius; V jT For j The moving speed, assuming that only point targets among all targets will move, and the speeds of surface targets and line targets are both 0; value j For j value.

[0018] A further technical solution of the present invention is as follows: the establishment of the strike task teaching model, selecting the target with the smallest evaluation index as the currently selected optimal strike target according to the greedy algorithm, is specifically as follows:

[0019] Assume that there are n aircraft U{U1,U2,…,U n}Execute n T Heterogeneous Targets Coordinated strike mission, each target T j Execute strike by constraint jT Each aircraft moves from its current position P i0 (x i0 ,y i0 ,z i0 ) to carry out multiple strike missions in sequence, assuming U i The strike mission sequence executed is: where Λ im ={ k ,P jk ,β k} means U iThe mth strike mission is to target the kth target T k At the strike angle β k From P jk Enter execution, at this time U i Complete the task sequence Λ i The required range is L i ;

[0020] Taking cluster minimization of strike mission completion time, minimization of strike resource consumption and maximization of mission completion value as the optimization objectives in the multi-aircraft strike trajectory planning process, a global optimization model minJ of multi-dynamic traveling salesman problem is established, and its variables are the mission sequence Λ of each aircraft:

[0021]

[0022] Among them, ω1, ω2 and ω3 correspond to the weight coefficients of the three task evaluation indicators, v i is the speed of the aircraft.

[0023] A further technical solution of the present invention is to use the Dubins algorithm to plan the route of the unmanned aerial vehicle that has been assigned to perform the strike mission.

[0024] A computer system, characterized in that it comprises: one or more processors, and a computer-readable storage medium for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the above-mentioned method.

[0025] A computer-readable storage medium, characterized in that it stores computer-executable instructions, which are used to implement the above method when executed.

[0026] A computer program product, characterized by comprising computer executable instructions, wherein the instructions are used to implement the above method when executed.

[0027] The beneficial effects of the present invention are:

[0028] The present invention provides an engineering method for planning rapid strike missions for multiple types of targets, which realizes the engineering solution of rapid strike mission planning problems in combat scenarios where there are free combinations of points, lines, and surfaces, etc., while satisfying multiple constraints such as unmanned aerial vehicle mission capabilities, self-constraints, and target characteristic constraints. The algorithm is verified under two situations: changes in the number of targets and adjustments to the mission capabilities of the unmanned aerial vehicle, and the flight trajectory of the unmanned aerial vehicle flight performance constraints and the efficient mission execution sequence of multiple aircraft are obtained. The method proposed in the present invention is close to the actual situation, has a fast solution speed, a unique solution result, is simple and practical, and is conducive to engineering implementation. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The drawings are only for the purpose of illustrating particular embodiments and are not to be considered limiting of the present invention. Like reference symbols denote like components throughout the drawings.

[0030] Figure 1 Define the fire control aiming distance and terminal guidance distance for striking the target.

[0031] Figure 2 To optimize the point target strike trajectory after striking the azimuth.

[0032] Figure 3 This is an illustration of the trajectory planning for striking a linear target.

[0033] Figure 4 Simplified schematic diagram of a surface target.

[0034] Figure 5 This is a schematic diagram of the trajectory for striking surface targets.

[0035] Figure 6 This is a schematic diagram of the strike mission scenario.

[0036] Figure 7 Schematic diagram of Dubins basic principle: (a) Dubins tangency circle; (b) Schematic diagram of Dubins track under initial velocity and terminal velocity constraints.

[0037] Figure 8 is the free Dubins track in the direction of terminal velocity.

[0038] Fig. 9 Constrain the Dubins track in the direction of the terminal velocity.

[0039] Fig.10 This is a diagram showing the planning results of the rapid strike mission of the unmanned aerial vehicle in the present invention in a scenario where points, lines and surfaces are freely combined in the area.

[0040] Fig.11 This is a diagram showing the planning results of a rapid strike mission after adjusting the position and strike capability of the unmanned aerial vehicle and adding surface targets in the present invention.

[0041] Fig.12 It is a flow chart of a specific implementation method of the present invention. DETAILED DESCRIPTION

[0042] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0043] In order to solve the problem of rapid strike mission planning of multiple unmanned aerial vehicles in an environment with free combinations of heterogeneous targets such as points, lines and surfaces, the problem description and related definitions of coordinated strike mission trajectory planning are given first, and a mathematical model of coordinated strike mission planning of multiple unmanned aerial vehicles is established; then, a strike trajectory solution method based on Dubins is given, which can realize dynamic adjustment of the trajectory when it is necessary to coordinate the internal resources of multiple unmanned aerial vehicles; secondly, in order to maximize the effectiveness of cluster strike, a method for optimizing the strike mission azimuth and the matching sequence of aircraft and targets is performed based on the track length obtained by the trajectory planning algorithm; finally, an engineering algorithm flow is given and the correctness of the algorithm is verified by simulation.

[0044] (1) Related concepts and definitions

[0045] In the actual strike mission engineering application for aircraft clusters, the targets often have obvious spatial characteristics. According to the relationship between the target size and the size of the aircraft fire control aiming field of view, the targets are divided into point targets and line targets. According to the actual strike load constraints of the aircraft, the fire control aiming distance and terminal guidance distance constraints should be met during the strike process. Figure 1 The relevant definitions are as follows:

[0046] 1) Point target: refers to a target that can be instantly and completely covered by the field of view sensor of a single aircraft, such as ground buildings, tanks, etc.

[0047] 2) Line targets: refers to targets that cannot be completely covered instantly by a single aircraft's field of view sensor, but whose threat width is smaller than the field of view width. A single aircraft can completely cover and strike them by flying along the centerline of their long sides, such as railways, airport runways, etc.

[0048] 3) Surface target: refers to a target that cannot be completely covered instantly by a single aircraft's field of view sensor and whose threat width is greater than the field of view width. Multiple aircraft are required to coordinate positioning and strike at a certain angle, which is generally called regional strike, such as factory areas, lakes, etc.

[0049] 4) Fire control aiming distance: In order to ensure accurate locking of the target during the strike process, the fire control aiming field of view should be opened at a certain distance from the target. The corresponding strike track planning point is the second track point reversely deduced from the target point.

[0050] 5) Terminal guidance distance: In order to ensure reliable strikes on the target, the ammunition should be switched to a reliable terminal guidance process when it is at a certain distance from the target. The corresponding lateral distance within this process is the terminal guidance distance, and the corresponding strike track planning point is the first track point reversely deduced from the target point.

[0051] According to the above-mentioned definitions and descriptions of related concepts, limited by the performance of a single aircraft and its load, aircraft cluster coordinated combat is the main application mode of strike missions. Combined with the characteristics of cluster strike mission trajectory planning, the relative coordinate system is selected as the reference coordinate system. Multiple aircraft are independently distributed in the mission airspace at the initial moment. In order to ensure the attack effect on the cluster, the fastest strike azimuth and strike sequence are optimized for multiple targets. The elements related to strike trajectory planning (aircraft, target, mission constraints, etc.) are expressed as symbolic information. Therefore, the aircraft strike load capability model, target model and strike mission model are established to lay the foundation for the proposal of trajectory planning optimization solution method. The model is described as the following quaternion:

[0052] {U,T,M,Λ}(1)

[0053] Among them, U={U i |i=1,2,…,n U} is the aircraft cluster set, n U is the number of aircraft; T = {T j |j=1,2,…,n T} is the target set, n T is the target number; M = {M j |j=1,2,…,n jT} is the target T j The task space-time coordination requirements, Λ = {Λ i |i=1,2,…,n U} is the geometric set of strike mission sequences corresponding to U, Λ i For U i The specific description of the four-tuple model is as follows.

[0054] (2) Aircraft Model

[0055] Aircraft are equipped with autopilots. i0 (x i0 ,y i0 ,z i0 ) is the aircraft U i Initial position, χ i0 ,γ i0 is the initial heading of the aircraft, C i ={C i,1 ,C i,2 ,…,C i,n} means U iThe n strike resources carried by the aircraft will be reduced as the number of strike targets is consumed during the aircraft's mission. In addition to meeting the aircraft's own performance constraints (including the aircraft's kinematic model, minimum turning radius, maximum turning angle, minimum track length, and maximum flight range constraints), the following model constraints must be established based on the aircraft's strike load capability constraints:

[0056] 1) Coordinates of the first track point

[0057] According to the constraints of the aircraft's flight capabilities, the aircraft U i Initial position P i0 (x i0 ,y i0 ,z i0 ) and the first track point position P i1 (x i1 ,y i1 ,z i1 ) can ensure accurate turning to the next track point for cruising flight, namely:

[0058] |P i0 P i1 |≥d0=r min tan(Δχ max / 2)+l0 (2)

[0059] Where: r min For U i Minimum turning radius; Δχ max For U i Maximum turning angle; l0 is U i The shortest distance required to stabilize the heading after each turn.

[0060] 2) Weapon launch point coordinates

[0061] Assume aircraft U i The coordinates of the weapon launch point are P is (x is ,y is ,z is ), at this time U i The strike angle β has been set. i Lock on target, U i The terminal guidance distance is R s , then P is The coordinates are calculated as:

[0062]

[0063] Where: (x t ,y t ,z t ) is the position coordinate of the target.

[0064] 3) Fire control start point coordinates

[0065] Assume aircraft U i The fire control start point coordinates are P if (x if ,y if ,z if ), the track direction of the fire control start point is also the strike azimuth angle β i , the corresponding fire control aiming distance is R f , then P if The coordinates are calculated as:

[0066]

[0067] (3) Target Model

[0068] According to the cluster task description, there are n T targets, for the jth target Τ j The model can be described by the following five-tuple:

[0069] Τ j ={type j ,P jT ,r j ,value j ,V jT} (5)

[0070] In the formula, type j For j The types of targets are divided into point targets, line targets and surface targets; jT Indicates j The position coordinates of the point target P jT The number of points is 1, and the line target is P jT The number of points is 2. If the surface target is a circular surface target, then P jT The number of points is 1. If it is a polygonal target, then P jT The number of points is the number of polygon vertices; r j Threat range for target, point target and line target j =0, circular target r j is the circle radius; V jT For j The moving speed, assuming that only point targets among all targets will move, and the speeds of surface targets and line targets are both 0; value j For j The value of is generally given by expert experience or target evaluation.

[0071] 1) Point Target

[0072] The fire control aiming section of the aircraft can instantly lock the target. The terminal point of the attack trajectory planning for point targets is the point target coordinates, which are calculated by the point coordinates P jT =P jt (x jt ,y jt ,z jt The direction angle of the planned terminal track is obtained by optimizing the strike angle, so that the strike track is obtained as follows: Figure 2 shown.

[0073] 2) Line target

[0074] The line target position can be determined by P jT = {P j1 (x j1 ,y j1 ,z j1 ),P j2 (x j2 ,y j2 ,z j2 )} means that P j1 and P j2 are the coordinates of the two vertices of the center line of the long side of the line target. The aircraft flies along the center line of the long side of the line target to complete the strike mission on the line target, that is, according to or The attack starts from any end in the direction of Figure 3 shown.

[0075] 3) Surface target

[0076] According to the area type, the surface target can be divided into polygonal surface targets, namely P jT = {P j1 (x j1 ,y j1 ,z j1 ),…,P jm (x jm ,y jm ,z jm )}, m is the number of vertices in the region, and the circular target, P jT =P jc (x jc ,y jc ,z jc ), P jc is the coordinate of the center of the circle, r j is the threat range of circular surface targets. According to the size of the surface target area and the fire control and aiming capability of the aircraft strike load, the number of aircraft n required to strike a single surface target can be obtained. jT :

[0077]

[0078] in, Indicates rounding up operation, W A The width of the surface target perpendicular to the direction of impact.

[0079] When performing a strike mission against an area target, the strike angle is obtained through algorithm optimization. To simplify the problem, the polygonal target is converted into a rectangular area, and the circular area of ​​the target is circumscribed into a square area. At the same time, the number of circumscribed squares of the circular area is infinite. The square area circumscribed at the optimal angle is optimized according to the range cost of entering the area for strike. The simplified schematic diagram of the strike trajectory of the area target is shown in the figure below. Figure 4 shown.

[0080] Due to the different initial positions of the aircraft, the path costs are also different. Therefore, during the optimization process, the best entry angle among all aircraft that are attacking the surface target will be found and used as the attack entry angle. The schematic diagram of the surface target attack trajectory is as follows: Figure 5 shown.

[0081] (4) Mathematical model of strike mission

[0082] When multiple aircraft are dispatched to coordinate strikes on multiple targets, due to the uncertainty of the aircraft's initial position and the target's position, value and other parameters, it is necessary to optimize the aircraft sequence for each target's strike mission and the sequence of strike missions performed by each aircraft to maximize mission effectiveness. Therefore, the trajectory planning optimization process of multiple aircraft's strike missions against multiple targets is described as a Multiple Dynamic Travelling Salesman Problem, and an MDTSP model is established.

[0083] Consider a combat scenario with n aircraft U{U1,U2,…,U n}Execute n T Heterogeneous Targets Coordinated strike mission, each target T j Execute strike by constraint jT Each aircraft moves from its current position P i0 (x i0 ,y i0 ,z i0 ) to carry out multiple strike missions in sequence, such as Figure 7 As shown. Assume U i The strike mission sequence to be executed is: where Λ im ={ k ,P jk ,β k} means U i The mth strike mission is to target the kth target T k At the strike angle βk From P jk Enter execution, n i For U i The number of strike missions executed is the strike load consumption. At this time, U i Complete the task sequence Λ i The required range is L i .

[0084] Taking cluster minimization of strike mission completion time, minimization of strike resource consumption and maximization of mission completion value as the optimization objectives in the multi-aircraft strike trajectory planning process, the MDTSP global optimization model minJ is established, and its variable is the mission sequence Λ of each aircraft:

[0085]

[0086] Among them, ω1, ω2 and ω3 correspond to the weight coefficients of the three task evaluation indicators respectively.

[0087] (5) Strike mission optimization method

[0088] When multiple aircraft perform strike mission trajectory planning on multiple targets, due to the different target costs and path transfer costs, the order of striking different targets is optimized according to the MDTSP model to obtain U{U1,U2,…,U n}Strike Mission Sequence In order to improve the solution capability and engineering application capability of the trajectory planning algorithm for the rapid strike mission of aircraft clusters, the targets with high evaluation indicators are collaboratively allocated in turn according to the greedy algorithm until the allocation and strike of all targets in the scene are completed.

[0089] The greedy algorithm makes a series of choices to find the optimal solution to the problem, and makes the currently best choice at each decision point, that is, makes the local optimal (greedy) choice to construct the global optimal solution. The greedy algorithm depends on the choices made before, but not on the future choices. Therefore, the cluster trajectory planning of the greedy algorithm has the characteristics of simplicity and practicality, low computational complexity, definite optimization calculation results, and little influence by initial conditions. It has the characteristics of easy implementation and high efficiency of the algorithm, and can be applied to cluster trajectory planning problems under various complex dynamic conditions. The algorithm principle will not be introduced in detail in this invention.

[0090] (6) Implementation principle of Dubins trajectory planning algorithm

[0091] The Dubins algorithm was proved in 1957 to be the shortest path obtained by connecting two attitude points in a given direction, that is, the shortest flight path between two points with directional angles on a plane is the "arc C-straight line L-arc C" or "arc C-arc C-arc C" curve calculated by Dubins, where the initial segment and the terminal segment are arcs of maximum curvature that satisfy the kinematic constraints of the aircraft, the initial segment arc is tangent to the initial azimuth of the aircraft at the same time, and the terminal segment arc is tangent to the target azimuth of the aircraft at the same time. Between the initial segment arc and the terminal segment arc, there is a straight line or arc tangent to the two arcs. At present, many methods have been used to study the Dubins algorithm and its related strategies. This section studies the Dubins algorithm based on analytic geometry in view of the trajectory planning constraints in the engineering application of aircraft clusters.

[0092] 1) Basic Dubins algorithm

[0093] Considering the turning radius constraint r ≥ r min , starting point P S =(x S ,y S ,x S ,r S ,V S ) and the target pose point P E =(x E ,y E ,x E ,r E ,V E ) constraint, r S and r E Dubins starting circle C S and the end circle C E The turning radius is generated based on the given starting point and target point. Figure 7 (a) The circle shown has a curvature center O at its center. S or E Therefore, the Dubins calculation of the shortest path between the starting point and the target point is transformed into the problem of finding the common tangent line or common tangent circle of two arcs.

[0094] P S =(x S ,y S ,x S ,r S ,V S ) Explain the meaning of each variable: (x S ,y S ) is the coordinate of the starting point; S is the angle between the initial velocity direction and the horizontal direction; r S V is the turning radius of the starting circle; Sis the velocity vector at the start.

[0095] from Figure 7 (b) It can be seen that there are two ways of turning at a tangent point, one is to turn left, the generated circular curve is called L, and the other is to turn right, the generated circular curve is called R. The common tangent of the two arcs is recorded as S. Therefore, the track between the given starting and target posture points is composed of a straight line track and a circular track.

[0096] Aiming at the engineering application of multi-UAV trajectory planning, the calculation of Dubins trajectory with freedom in terminal velocity direction and constraint in terminal velocity direction is given.

[0097] 2) Free Dubins track in terminal velocity direction

[0098] The terminal direction free Dubins track refers to the track point obtained by constraint planning based on the aircraft motion equation when the initial position point and the initial target point are given, without specifying the speed direction when reaching the target point. According to the relationship between the initial point and the target point, the terminal free Dubins track is divided into seven modes, namely S, L, R, RS, LS, RL, LR, such as Figure 8 shown.

[0099] According to the above description, let the starting arc turn left or right, and the starting point P S =(x S ,y S ,x S ,r S ,V S ) Passing the left turning arc Right turn arc or straight line segment Movement Angle The obtained P A The coordinates are:

[0100]

[0101] in: is the center angle of the starting arc.

[0102] 3) Dubins track with terminal velocity direction constraint

[0103] The optimal trajectory under the terminal velocity direction constraint must be one of the six cases: RLR, LRL, RSL, LSR, RSR, LSL, such as Fig. 9 shown.

[0104] According to the basic principle of Dubins path, the calculation process of the common tangent line and tangent point coordinates of the CLC path is given:

[0105] (a) Calculate C S Center O S (x CS ,y CS ) and C E Center O E (x CE ,y CE )as follows:

[0106]

[0107] (b) Calculate the coordinates of the tangent point of the common tangent line

[0108] exist Fig. 9 In the RSL, LSR, RSR and LSL paths shown in the figure, point P A =(x A ,y A ,x A ,r S ) and P B =(x B ,y B ,x B ,r E ) are circle C S and C E The cut-out and cut-in points on the circle are shown in Figure 1. α is the angle between the line connecting the centers of the two circles and the common tangent line, β is the angle between the line connecting the centers of the two circles and the positive direction of the OX axis, t is the length of the common tangent line, and c is the length of the line connecting the centers. S and C E When the common tangent of is an internal common tangent or an external common tangent, α corresponds to different calculation formulas, and α and β are calculated as follows:

[0109]

[0110] The coordinates of the common tangent point are calculated as follows:

[0111]

[0112] Where: (x G ,y G ) is the coordinate of the point of tangency with the starting circle; (x F ,y F ) is the coordinate of the point of tangency with the termination circle; A and χ B The calculations based on the sizes of the four Dubins configurations, α and β are shown in the following table.

[0113] Table 1 Calculation of azimuth angles of cut-out and cut-in points under four path types

[0114]

[0115] (c) Calculate the center angle of the arc track

[0116] Calculate the center angle from the starting point to the cut-out point according to the cut-out point and the center angle corresponding to the rotation from the entry point to the target pose point

[0117]

[0118] (d) Calculation of CLC path length

[0119] The CLC Dubins path length L can be calculated using the Pythagorean theory:

[0120]

[0121] Where t is the length of the common tangent.

[0122] For the Dubins path of the CCC curve, the process of calculating the coordinates of the common tangent point of its path is as follows:

[0123] Calculate the starting circle C S Center O S (x CS ,y CS ), middle circle C M Center(x CM ,y CM ) and C E Center O E (x CE ,y CE )as follows:

[0124]

[0125] Among them, θ is a vector The angle with the positive direction of OX, φ = ∠O L O S O E , when the starting pose point and the target pose point turn left, the middle circle corresponds to a right turn C R , center C S and C E The angle with the positive direction of OX is χ S +π / 2 and χ E +π / 2; when the starting and target pose points turn right, the middle circle corresponds to a left turn C L , the angle between the center of the circle and the positive direction of OX is χ S -π / 2 and χ E -π / 2.

[0126] (e) Calculate the coordinates of the tangent point of the common tangent line

[0127] exist Fig. 9 In the RLR and LRL paths shown in the figure, point P A =(x A ,y A ,x A ,r S ) and P B =(x B ,y B ,x B ,r E ) are circle C S and C E The coordinates of the common tangent point are calculated as follows:

[0128]

[0129] In case of RLR:

[0130]

[0131] In case of LRL:

[0132]

[0133] Where: A , χ B The angles of the entry and exit points.

[0134] (f) Calculate the center angle of the arc track

[0135] Calculate the center angle from the starting position point to the first cut-out point according to different configurations The center angle from the first cut-out point to the second cut-out point and the center angle from the second cut-out point to the target pose point In case of RLR:

[0136]

[0137] In case of LRL:

[0138]

[0139] (g) Calculate the CCC path length

[0140] The Dubins path length of the CCC type can be calculated using the Pythagorean theory:

[0141]

[0142] In order to enable those skilled in the art to better understand the present invention, the present invention is described in detail below in conjunction with specific embodiments.

[0143] The embodiment of the present invention is an engineering method applicable to the problem of multi-type target rapid strike mission planning, comprising the following steps:

[0144] Step 1: Initialize the aircraft, target and mission information. For aircraft: location (latitude, longitude and altitude), strike load, turning radius, strike field coverage width; for targets: location (latitude, longitude and altitude), number of aircraft required to strike the target, type (point, line, surface); for mission information: determine the set of mission targets that the aircraft needs to perform, that is, the set of targets that need to participate in the subsequent task allocation.

[0145] Step 2: Traverse all unstruck targets. Update the current position status of the aircraft: position (latitude and longitude, heading, remaining strike capability), then calculate the track length of each aircraft to the target, and determine the aircraft that will perform the strike mission on the target based on the distance from the aircraft to the target and the aircraft's own capabilities (remaining range, remaining strike capability). After traversing all unstruck targets, proceed to step 3.

[0146] Step 3: The traversal of unhit targets is completed. When all unhit targets are traversed, the target T with the smallest evaluation index min J is selected according to the greedy algorithm. k The optimal target currently selected is the sequence of unmanned aerial vehicles that will attack the target based on the number of drones required to attack the target.

[0147] Step 4: Update status information. After the current optimal strike target is selected, update the relevant parameters of the aircraft, target, and mission information, and delete the target that has been assigned to the aircraft to perform the strike mission from the list of unstrike targets.

[0148] Step 5: Update the track information. Plan the route for the unmanned aerial vehicle that has been assigned to perform the strike mission, and find the optimal strike angle through a loop traversal method.

[0149] Step 6: Determine whether the target has completed all the strike mission assignments. If completed, proceed to step 7, otherwise, return to step 2.

[0150] Step 7: All targets are assigned, the mission planning results are parsed and output, and the entire strike mission planning is now complete.

[0151] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should be included in the protection scope of the present invention.

Claims

1. An engineering method suitable for planning rapid strike missions against multiple types of targets, characterized in that: include: Build multi-type target models; Calculating the track length of each aircraft to each target based on the aircraft position and the target position, and determining the aircraft to perform the strike mission on the target based on the track length and the aircraft's own capabilities; the aircraft's own capabilities include remaining range and remaining strike capability; Establish a strike mission teaching model, select the target with the smallest evaluation index as the optimal strike target according to the greedy algorithm, and select the sequence of unmanned aerial vehicles to strike the target according to the number of drones required to strike the target; The routes of the unmanned aerial vehicles that have been assigned to carry out strike missions are planned, and the optimal strike angle is found through a cyclic traversal method with the goal of shortest range.

2. An engineering method suitable for multi-type target rapid strike mission planning according to claim 1, characterized in that: The multi-type target model is specifically: Τ j ={type j ,P jT ,r j ,value j ,V jT } Among them, type j Target j The types of targets are divided into point targets, line targets and surface targets; jT Indicates j The position coordinates of the point target P jT The number of points is 1, and the line target is P jT The number of points is 2. If the surface target is a circular surface target, then P jT The number of points is 1. If it is a polygonal target, then P jT The number of points is the number of polygon vertices; r j Threat range for target, point target and line target j =0, circular target r j is the circle radius; V jT For j The moving speed, assuming that only point targets among all targets will move, and the speeds of surface targets and line targets are both 0; value j For j value.

3. An engineering method suitable for planning rapid strike missions against multiple types of targets according to claim 2, characterized in that: The strike task teaching model is established, and the target with the smallest evaluation index is selected as the optimal strike target currently selected according to the greedy algorithm, specifically: Assume that there are n aircraft U{U1,U2,,U n }Execute n T Heterogeneous Targets Coordinated strike mission, each target T j Execute strike by constraint jT Each aircraft moves from its current position P i0 (x i0 ,y i0 ,z i0 ) to carry out multiple strike missions in sequence, assuming U i The strike mission sequence to be executed is: where Λ im ={ k ,P jk ,β k } means U i The mth strike mission is to target the kth target T k At the strike angle β k From P jk Enter execution, at this time U i Complete the task sequence Λ i The required range is L i ; Taking cluster minimization of strike mission completion time, minimization of strike resource consumption and maximization of mission completion value as the optimization objectives in the multi-aircraft strike trajectory planning process, a global optimization model minJ of multi-dynamic traveling salesman problem is established, and its variables are the mission sequence Λ of each aircraft: Among them, ω1, ω2 and ω3 correspond to the weight coefficients of the three task evaluation indicators, v i is the speed of the aircraft.

4. According to claim 1, an engineering method suitable for planning a rapid strike mission for multiple types of targets is characterized in that: The Dubins algorithm is used to plan the route of the unmanned aerial vehicle that has been assigned to perform the strike mission.

5. A computer system, characterized in that include: One or more processors, and a computer-readable storage medium for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors are enabled to implement the method of claim 1.

6. A computer-readable storage medium, characterized in that Computer executable instructions are stored, and when the instructions are executed, they are used to implement the method of claim 1.

7. A computer program product, characterized in that The invention comprises computer executable instructions, which are used to implement the method of claim 1 when being executed.

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