Flight path planning method

Abstract

The invention discloses a flight path planning method, and belongs to the field of flight path planning of aircrafts. The method comprises the following steps: establishing a flight path model before and after aircraft bomb dropping, taking a cost function of the flight path model as a fitness function of a symbiotic organism search model, and obtaining an optimal flight path through the symbiotic organism search model; the cost function comprises a first stage before bomb dropping and a second stage after bomb dropping. The whole track is divided into two parts before and after bomb dropping, track models before and after bomb dropping are comprehensively considered, and track planning obtained through symbiotic organism searching has good performance in an assault task to the ground.

Description

Technical Field

[0001] The invention relates to the field of aircraft trajectory planning, and in particular to a trajectory planning method. Background Art

[0002] Trajectory planning is a type of path planning and an important technical component of aircraft. Its significance lies in helping aircraft avoid various threats and meet various constraints in harsh flight environments, providing UAVs with executable flight paths, and successfully completing missions. In the prior art, algorithms such as ant colony algorithms, genetic algorithms, and dynamic programming algorithms have been applied to the field of trajectory planning. The trajectory model established in the trajectory planning process does not take into account the situation in which the trajectory of the carrier aircraft deviates after performing bombing operations in actual combat and the different trajectory model indicators before and after bombing. This makes the aircraft trajectory planning have certain limitations in ground assault missions. Summary of the invention

[0003] In view of the above-mentioned defects of the prior art, the present invention provides a trajectory planning method, which divides the entire trajectory into two parts before and after bombing, comprehensively considers the trajectory models before and after bombing, and the trajectory planning obtained by symbiotic organism search has good performance in ground assault missions.

[0004] A track planning method of the present invention comprises the following steps:

[0005] A trajectory model of the aircraft before and after bombing is established, and the cost function of the trajectory model is used as the fitness function of the symbiotic search model. The optimal trajectory is obtained through the symbiotic search model; the cost function J is:

[0006] J=J (1) (m)+J (2) (n);

[0007] Among them, J (1) (m) is the cost function of the trajectory model before bombing, which is:

[0008]

[0009] J (2) (n) is the cost function of the trajectory model after bombing, which is:

[0010]

[0011] in, represents the sth flight attribute of the ith segment before bombing. represents the sth flight attribute of the jth segment after bombing. represents the flight phase of the aircraft returning after dropping the bomb, the sth flight attribute of the jth flight segment, represents the sth flight attribute of the jth segment during the missile attack target flight phase after bombing, λ s Represents the weight of s flight attributes.

[0012] Furthermore, the flight segment division method adopts a step length division method.

[0013] Furthermore, the flight attribute is one or more combinations of average flight altitude, threat intensity and flight segment length.

[0014] Furthermore, the threat intensity is represented by a threat cost, and the threat cost is:

[0015] f j (i) = x j β j U j / (d j ) 4 ;

[0016] U j is the intensity of the threat potential field, which is determined by the characteristics of the threat itself; when the aircraft is detected, β j =1, otherwise β j >1; when the flight segment passes through the target threat area, χ j =1, otherwise χ j =0.

[0017] Furthermore, the constraint conditions of the trajectory model of the aircraft before and after bombing are one or more combinations of the total trajectory length constraint, the maximum and minimum flight altitude constraints, the maximum climb / descent angle constraints, the minimum turning radius constraints, the minimum trajectory segment length constraints and the fuel consumption constraints.

[0018] Furthermore, the total track length constraint is:

[0019] Among them, ||l i || is the track segment l i Length;

[0020] The maximum and minimum flight altitude constraints are:

[0021] H min ≤H≤H max ;

[0022] The maximum climb / descent angle constraint is:

[0023] The minimum turning radius constraint is: r k ≥r min ;

[0024] The minimum track segment length constraint is: l i ≥lmin (i=1,2,…,n)

[0025] The fuel consumption constraint is: (F P1 t1+F P2 t2)≤F max <F0.

[0026] Furthermore, the method for obtaining the optimal track by the symbiotic organism search model includes:

[0027] Step 1: Initialize the size of the biological population N, the upper and lower bounds of the search space ub and lb, and the maximum number of iterations iterMax;

[0028] Step 2: Identify the organism X with the best fitness best ;

[0029] Step 3: Randomly select two different individuals X from the biological population i and X j , generate the relationship feature vector Mutual_Vector between them, and generate a new individual X i_new and X j_new ; For biological individual X i and X j , if f(X i ) <f(X i_new ) or f(X j ) <f(X j_new ), then choose to accept the new individual X i_new , X j_new ;

[0030] Step 4: Randomly select two different individuals X from the biological population i and X j , generate a new individual X i_new ; For biological individual X i , if f(X i ) <f(X i_new ), then choose to accept the new individual X i_new ;

[0031] Step 5: Generate mutation vector Parasite_Vector; for biological individual X i , if f(Parasite_Vector) <f(X j ), then choose to accept the new individual X i_new ;

[0032] Step 6: If the number of individuals in the population is reached, go to step 7, otherwise go to step 2 and continue the next search;

[0033] Step 7: If the termination criteria are met, output the global optimal solution Xbest ; Otherwise, enter the iteration counter, record and continue the next search.

[0034] Compared with the prior art, the present invention has the following technical effects:

[0035] 1. The track model of the present invention is established based on the actual situation of the aircraft before and after bombing. The cost function established according to the actual situation before and after bombing can fully reflect the actual flight situation of the aircraft in the ground attack mission and can provide a better track planning result.

[0036] 2. The flight segments in the present invention are different from the segmentation method of the rated number of segments in the prior art. It can flexibly reflect the threat costs of different flight segments and is more in line with the flight conditions before and after the aircraft drops bombs, so that the optimal flight track obtained has the highest safety.

[0037] The concept, specific structure and technical effects of the present invention will be further described below in conjunction with the accompanying drawings to fully understand the purpose, characteristics and effects of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 is a schematic diagram of a track of a specific embodiment of the present invention;

[0039] Figure 2 This is a schematic diagram of an 8-domain node search according to a specific embodiment of the present invention;

[0040] Figure 3 is a schematic diagram of the total trace length of a specific embodiment of the present invention;

[0041] Figure 4 is a schematic diagram of terrain flight altitude requirements of a specific embodiment of the present invention;

[0042] Figure 5 is a schematic diagram of a flight pitch angle of a specific embodiment of the present invention;

[0043] Figure 6 is a schematic diagram of a turning radius of a specific embodiment of the present invention;

[0044] Figure 7 is a minimum step size selection diagram of a specific embodiment of the present invention;

[0045] Figure 8 is a schematic diagram of threat cost according to a specific embodiment of the present invention;

[0046] Fig. 9 This is a schematic diagram of the trajectory before and after bombing of a specific embodiment of the present invention;

[0047] Fig.10 This is a schematic diagram of dividing flight segments by step length according to a specific embodiment of the present invention;

[0048] Fig.11 It is a flow chart of the SOS algorithm of a specific embodiment of the present invention. DETAILED DESCRIPTION

[0049] The following describes the embodiments of the present invention by specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0050] It should be noted that the illustrations provided in the following embodiments are only used to illustrate the basic concept of the present invention in a schematic manner, and thus the illustrations only show components related to the present invention rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0051] While some exemplary embodiments of the present invention have been described for the purpose of illustration, it should be understood that the present invention may be implemented in other ways not specifically shown in the drawings.

[0052] In a specific embodiment, a trajectory planning method is provided, comprising the following steps:

[0053] S1. Establish the flight path model of the aircraft before and after bombing.

[0054] Mathematical modeling of trajectory planning is the process of finding each track point and their sequential relationship based on the threat space and in accordance with the constraints of the aircraft and missiles themselves and the environment during flight, including the representation of track and constraints, the design of cost function and other aspects.

[0055] The main purpose of trajectory planning is to establish one or more routes through which the aircraft or missile can fly from the starting spatial location to other pre-planned locations. Generally speaking, the aircraft must pass through multiple intermediate locations before reaching the final location, and there may be multiple track points in multiple airspaces, such as Figure 1 shown.

[0056] Assume that in a certain planning space, the state variable of the aircraft at each spatial position is P(x, y, z), and the coordinates (x, y, z) are the spatial coordinates of the aircraft, which can generally be expressed by longitude, latitude and altitude, and can also be expressed by spatial rectangular coordinate system and spherical coordinate system. They represent the track deflection and track inclination of the aircraft respectively. The track deflection is the angle between the projection of the tangent of the navigation track on the horizontal plane and the reference zero heading, and the track inclination is the angle between the navigation route and the horizontal plane. If the aircraft needs to enter the designated area, the heading or pitch angle of this matter should be considered. At this time, θ and In order to further simplify the trajectory planning problem of the aircraft, sometimes we can only consider the position change of the aircraft and ignore θ and At this time, what needs to be dealt with is still the three-dimensional planning problem with the track point state variables P(x, y, z).

[0057] Now consider a vehicle starting from the initial state P s Towards the final state P f During the navigation process, track planning is to generate a path that connects multiple P s and P f The path r(q) is expressed as follows:

[0058]

[0059] Where U represents the constraint condition, r(q) is the track that satisfies the constraint condition, and q is the path constraint parameter.

[0060] Due to the complex terrain of the combat area, the track can be divided into segments, and then a model is established for each segment. The constraints are adjusted in combination with different tasks and environmental characteristics to improve the overall rationality of track planning. In more actual combat tasks, it is necessary to plan the track for the coordinated combat tasks of multiple aircraft at the same time, which can be described by the following formula:

[0061]

[0062] In the formula, r i (q) is i The initial state P of the aircraft si and the final state P fi The track between.

[0063] Theoretically, the search direction in three-dimensional space can be expanded in multiple directions arbitrarily, but this will result in a large amount of data that cannot be calculated. Therefore, considering the needs of complex practical environments, the 8-domain grid is mainly used as the search space here. Figure 2 Schematic diagrams of two-dimensional and three-dimensional search results from point A to point B in 8 domain spaces.

[0064] Each type of aircraft has different basic performance, various parameters are quite different, and different conditions need to be met in different combat environments, mainly including basic requirements such as flyability and safety. First of all, the flyability of trajectory planning is to meet the performance condition restrictions of the aircraft, such as within the curvature range when turning, and non-parametric adjustment must not approach or even exceed the performance limit; secondly, the requirement of safety is that the planned route must enable the aircraft to successfully avoid various dangers and threats, prevent collisions with obstacles or other physical objects, and try to avoid enemy fire attacks and detection ranges; finally, if you want to complete the assault mission to a greater extent and continuously improve the planning efficiency, you often need to add other constraints. Based on the actual assault mission, the constraints of the trajectory model of the aircraft before and after bombing are one or more combinations of the total length of the trajectory, the maximum and minimum flight altitude constraints, the maximum climb / descent angle constraints, the minimum turning radius constraints, the minimum track segment length constraints, and the fuel consumption constraints.

[0065] ① Total track length constraint:

[0066] The aircraft's fuel load, flight time, and speed determine the total length of the trajectory planning problem. Figure 3 As shown in the schematic diagram of the total length of the track, the track of the aircraft is assumed to be {l i |i=1,2,…,n}, the maximum allowed total track length is L max , then the constraint can be expressed as:

[0067]

[0068] In the formula, ||l i || is the track segment l i Length.

[0069] ② Maximum and minimum flight altitude constraints:

[0070] The requirements of the aircraft's mission and its own performance determine the range of altitudes it can fly in combat. The maximum altitude is usually expressed by the aircraft's altitude, and the minimum altitude is expressed by the vertical distance from the ground, such as Figure 4 As shown, the maximum and minimum flight altitude constraints are:

[0071] H min ≤H≤H max .

[0072] ③ Maximum climb / descent angle constraints:

[0073] The aircraft's own structure and maneuverability determine its maximum angle of climb and descent. Figure 5 As shown, let the position of the i-th track point be (x i ,y i ,zi ), the end point is (x i+1 ,y i+1 ,z i+1 ), the z axis is the height direction, and its horizontal projection is a i =(x i+1 -x i ,y i+1 -y i ) T , the maximum allowed climb / descent angle is θ, then the maximum climb / descent angle constraint is:

[0074]

[0075] ④Minimum turning radius constraint:

[0076] The performance of the aircraft itself determines its maneuverability, and the turning radius is also affected by factors such as altitude and maximum tolerable overload. Therefore, in actual combat, the turning radius allowed by various influencing factors is comprehensively considered, such as Figure 6 As shown, the maximum value is the minimum threshold of the constraint, so the minimum rotation radius constraint is:

[0077] r k ≥r min .

[0078] ⑤Minimum track segment length constraint:

[0079] The maneuverability of the aircraft limits the shortest distance between each track point during track planning, reflecting the shortest distance that the aircraft must have before adjusting its flight attitude to correct navigation errors. i |i=1,2,…,n}, the minimum route segment length is l i This constraint can be expressed as:

[0080] l i ≥l min (i=1,2,…,n);

[0081] like Figure 7 As shown, if m is the current track node, n is the previous track node, m1, m2, m3, m4, m5 are the next candidate track nodes, corresponding to the track lengths L1, L2, L3, L4, L5 respectively. In order to meet the constraint of the minimum track segment length, only the nodes m2, m3, m4 corresponding to L2, L3, L4 can be selected as the next track nodes.

[0082] ⑥ Constraints on fuel consumption:

[0083] The amount of fuel loaded on the aircraft before takeoff is certain. To ensure that its fuel is sufficient to meet combat needs, the total amount of fuel required in the actual combat mission should be considered as a factor in trajectory planning. Suppose the total amount of fuel on the aircraft at the beginning is F0, and the total amount of fuel required to successfully complete the mission is F max , the trajectory of the aircraft is divided into two stages: flight and mission. Then: the average amount of fuel consumed per unit time in the flight stage is F P1 The flight phase time is t1; the average amount of fuel consumed per unit time during the mission phase is F P2 , the mission phase flight time is t2, then the bundle with the maximum fuel consumption is:

[0084] (F P1 t1+F P2 t2)≤F max <F0.

[0085] The above are common constraints that need to be considered in aircraft trajectory planning. In specific applications, they should be targeted at different mission requirements. In the process of trajectory planning, relevant constraints can be selected based on actual conditions. If necessary, new constraints should be set. On this basis, a model can be established to solve a relatively safe and efficient trajectory.

[0086] Under the above constraints, in order to further quantify the cost of the flight path during the mission, a flight segment is usually divided into multiple segments, and each segment is assigned a value after fully considering various factors that affect the quality of the flight path. The traditional evaluation function is defined as:

[0087]

[0088] Where P Di P represents the probability that the aircraft is detected by the enemy detection radar in the i-th segment of the track; Ki represents the probability of the aircraft being discovered and destroyed; P Ci It indicates the probability of the aircraft touching the ground or hitting a mountain in this flight segment. It can be seen that the safety of the track is related to factors such as the distance between the aircraft and the threat, the flight altitude, the flight distance, and the number of threats. The calculation is quite complicated. Taking each influencing factor as a flight attribute factor,

[0089]

[0090] Among them, x si It is the flying attribute factor.

[0091] like Figure 8 As shown. i can be Calculate. In the formula, f i(i) represents the threat cost of target j to the i-th segment, then the threat cost of the track is:

[0092] f j (i) = x j β j U j / (d j ) 4 ;

[0093] Among them, U j is the intensity of the threat potential field, which is determined by the characteristics of the threat itself; when the aircraft is detected, β j =1, otherwise β j >1; when the flight segment passes through the target threat area, χ j =1, otherwise χ j =0;d j is the distance between the aircraft and the threat center, Figure 8 d in k1 ,d k2 ,…,d k5 .

[0094] The cost function represented by the cost function J only represents the cost calculation value of the aircraft during the entire trajectory process, which has certain limitations compared to the ground assault mission: on the one hand, in actual combat, the trajectory of the carrier aircraft deviates after performing the bombing mission, and the respective cost model indicators are different; on the other hand, the calculation of the threat cost of the threat area in the literature mainly divides the flight segment into designated 3, 5 or 10 segments, which cannot flexibly reflect its threat cost value.

[0095] In view of the first limitation mentioned above, in a specific embodiment, the aircraft track and the missile track are considered separately, such as Fig. 9 As shown in Figure 1, the flight path is divided into two stages: the first stage before the bomb is dropped and the second stage after the bomb is dropped. The flight paths before and after the bomb are represented by L (1) L (2) The total track J is:

[0096] J=J (1) (m)+J (2) (n);

[0097] Among them, J (1) (m) is the cost function of the trajectory model before bombing, which only includes the navigation situation of the carrier aircraft carrying bombs, which includes m segments and is:

[0098]

[0099] J (2) (n) is the cost function of the trajectory model after bombing, which includes the situation of the aircraft returning after recovery and the missile flying through the enemy's air defense position, which is:

[0100]

[0101] in, represents the sth flight attribute of the ith segment before bombing. represents the sth flight attribute of the jth segment after bombing. represents the flight phase of the aircraft returning after dropping the bomb, the sth flight attribute of the jth flight segment, represents the sth flight attribute of the jth segment during the missile attack target flight phase after bombing, λ s Represents the weight of s flight attributes.

[0102] In this embodiment, one or more combinations of average flight altitude, threat intensity, and flight segment length are used as flight attributes. i The average flight altitude of the i-th segment is represented by f i It represents the threat index of the i-th segment, and l i represents the length of the i-th segment. Let the average flight altitude, threat intensity and segment length of the flight phase before the carrier aircraft drops the bomb be h i (1) 、f i (1) , l i (1) ,but:

[0103]

[0104] Let the average flight altitude, threat intensity and flight length of the return flight phase after the carrier aircraft drops the bomb be h i (2) 、f i (2) , l i (2) The average flight altitude, threat intensity and flight length of the missile attack target during the flight phase are h mi (2) 、f mi (2) , l mi (2) ,but:

[0105]

[0106] In view of the second limitation mentioned above, in a specific embodiment, a step length is used to divide each flight segment into nodes. Since the combat area is large, simply dividing the flight segment passing through a certain threat area by a specified number of nodes is not accurate enough and cannot accurately reflect the threat cost of the flight segment. Here, a step length κ is used to divide each flight segment, such as Fig.10As shown: the segment length is 150km, the step length is 10km, and it is divided into 15 small segments.

[0107] Then we have:

[0108]

[0109] In the formula, N' is the number of segments divided by step length κ, and the improved threat cost can be expressed as follows:

[0110]

[0111] The track cost is affected by the total length of the track, the average height and the distance from the threat source. These indicators are unidirectional variables, and the threat cost of the track f i (i) = x j β j U j / (d j ) 4 The penalty variables in are not of the same nature and are at the same level. They should be normalized by using the following transformation method.

[0112]

[0113] Where, is the normalized length, height and threat index. Taking the first stage track cost function as an example, it is converted into:

[0114]

[0115] The trajectory planning problem includes the threat costs of terrain, radar and anti-aircraft firepower, as well as other costs such as altitude and distance. It is a multi-criteria decision-making problem, also known as a limited-scheme multi-criteria decision, which is to select and sort the schemes according to a certain decision criterion to select the best or relatively better scheme. Because in multi-criteria decision-making problems, the weight attributes are often not mastered, and the method of determining weights by empirical valuation is highly subjective, in order to reduce the influence of subjective factors, the objective attributes of weights should be analyzed more. This topic introduces the method of information entropy to objectively weight each attribute, which effectively solves the problem of serious subjective components of weights.

[0116] Constructing a scheme matrix: Assume that the result of the aircraft trajectory planning is a scheme set consisting of N sub-schemes, each of which includes s attribute values ​​(in this embodiment, there are 3 attribute values: flight altitude, fuel consumption and threat cost), represented by the vector X s =(x1,x2,…,x s ) indicates that:

[0117]

[0118] Solve the normalized matrix: The properties of each attribute vector in the above formula are different, and should be dimensionless to obtain the normalized matrix:

[0119]

[0120] Solve the normalized matrix: After normalizing the normalized matrix, we get:

[0121]

[0122] Calculate information entropy:

[0123]

[0124] when hour,

[0125] Obtain the weight vector: Let λ = (λ1,λ2,…,λ m ), then:

[0126]

[0127] S2. Using the cost function of the track model as the fitness function of the symbiotic organism search model, and obtaining the optimal track through the symbiotic organism search model.

[0128] The trajectory planning algorithm is the core of the aircraft trajectory planning system, involving multiple fields such as operations research, artificial intelligence, machine learning and optimal control. The purpose of using computer software to solve is to scientifically and comprehensively consider the constraints, efficiently find the route that meets the given conditions, and give the optimal solution or formulate alternative plans according to the priority order. This topic compares the principles of the Voronoi diagram method, ant colony algorithm, dynamic programming algorithm, genetic algorithm, simulated annealing algorithm, A* algorithm, particle swarm optimization algorithm and SOS search algorithm, as well as their advantages and disadvantages in trajectory planning, and makes a detailed analysis of the new meta-heuristic optimization algorithm Symbiotic Organisms Seasrch (SOS), and applies it to solve the problem.

[0129] The comparison between the SOS search algorithm and other commonly used search algorithms is shown in Table 1:

[0130]

[0131]

[0132] In summary, each algorithm has its corresponding advantages and disadvantages. It is impossible for an algorithm to solve all optimization problems. Therefore, it is necessary to select or improve the algorithm based on actual problems. The research task of the subject is the ground assault mission against the surface-to-air missile position. The combat environment is complex and changeable, and the combat information is rich and diverse. Therefore, the selection of the symbiotic organism search (SOS) algorithm can not only solve the pre-planning problem well, but also continuously add new combat elements and combat tasks, which is more conducive to the solution of dynamic and real-time task planning problems in actual combat.

[0133] In the symbiotic search algorithm, new individuals are continuously generated by imitating the interaction between two individuals in the ecosystem, including three stages: mutualism, partial benefit and parasitism. In each stage, the probability of each individual interacting with other individuals is equal, and the global optimal solution is obtained through a continuous selection process.

[0134] ① Mutually beneficial stage

[0135] Suppose there are two organisms X in the population i and X j (i≠j), by establishing a mutually beneficial symbiotic relationship, the survival ability of the two individuals is continuously improved. The new organism generation function is shown in the following formula, and organisms with stronger adaptability are selected to be passed on to the next generation of the population.

[0136]

[0137] Where, X best is the current optimal individual in the population, α and β are random values ​​in the interval [0,1], Mutual_Vector represents the characteristic vector of the relationship between two individuals, BF1 and BF2 take values ​​of 1 or 2, indicating different degrees of profitability.

[0138] ②Biased stage

[0139] For two organisms X in a population i and X j (i≠j), where one individual X i Profit from the symbiotic relationship, while another individual X j There is no profit or loss, the new organism production function is shown in the following equation, and organisms with stronger adaptability are selected to be passed on to the next generation of the population.

[0140] X i_new =X i +δ(X best -X j )

[0141] Where δ is a random number in the interval [-1, 1].

[0142] ③ Parasitic stage

[0143] First assign an organism X i , Parasite_Vector is used to represent the mutation vector, which plays a role similar to that of the malaria mosquito and is generated by the following function.

[0144]

[0145] In the formula, pick is the variable bit, lb and ub represent the lower bound and upper bound respectively.

[0146] When host X i When attacked by a parasite, if the parasite's fitness value is higher, the host will be killed, and if the host produces a higher immunity value, the parasite will be killed.

[0147] According to the principle of SOS algorithm, the specific process of its implementation steps is as follows Fig.11 As shown, including:

[0148] Step 1: Initialize the size of the biological population N, the upper and lower bounds of the search space ub and lb, and the maximum number of iterations iterMax;

[0149] Step 2: Identify the organism X with the best fitness best , the organism with the strongest fitness refers to the spatial position node with the best fitness;

[0150] Step 3: Randomly select two different individuals X from the biological population i and X j , generate the relationship feature vector Mutual_Vector between them, and generate a new individual X i_new and X j_new ; For biological individual X i and X j , if f(X i ) <f(X i_new ) or f(X j ) <f(X j_new ), then choose to accept the new individual X i_new , X j_new ;

[0151] Step 4: Randomly select two different individuals X from the biological population i and X j , generate a new individual X i_new ; For biological individual X i , if f(X i ) <f(X i_new ), then choose to accept the new individual X i_new ;

[0152] Step 5: Generate mutation vector Parasite_Vector; for biological individual X i , if f(Parasite_Vector) <f(X j ), then choose to accept the new individual X i_new ;

[0153] Step 6: If the number of individuals in the population is reached, go to step 7, otherwise go to step 2 and continue the next search;

[0154] Step 7: If the termination criteria are met, output the global optimal solution X best ; Otherwise, enter the iteration counter, record and continue the next search.

[0155] The above embodiments are merely illustrative of the principles and effects of the present invention, and are not intended to limit the present invention. Anyone familiar with the art may modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by a person of ordinary skill in the art without departing from the spirit and technical concept disclosed by the present invention shall still be covered by the claims of the present invention.

Claims

1. A trajectory planning method, characterized in that: The steps include: A trajectory model of the aircraft before and after bombing is established, and the cost function of the trajectory model is used as the fitness function of the symbiotic search model. The optimal trajectory is obtained through the symbiotic search model; the cost function J is: J=J (1) (m)+J (2) (n); Among them, J (1) (m) is the cost function of the trajectory model before bombing, which is: J (2) (n) is the cost function of the trajectory model after bombing, which is: in, represents the sth flight attribute of the ith segment before bombing. represents the sth flight attribute of the jth segment after bombing. represents the flight phase of the aircraft returning after dropping the bomb, the sth flight attribute of the jth flight segment, represents the sth flight attribute of the jth segment during the missile attack target flight phase after bombing, λ s Represents the weight of s flight attributes.

2. A trajectory planning method according to claim 1, characterized in that: The flight segment division method adopts a step length division method.

3. A trajectory planning method according to claim 1, characterized in that: The flight attribute is one or more combinations of average flight altitude, threat intensity and flight segment length.

4. A trajectory planning method according to claim 3, characterized in that: The threat intensity is expressed by threat cost, which is: f j (i)=x j b j U j / (d j ) 4 ; U j is the intensity of the threat potential field, which is determined by the characteristics of the threat itself; when the aircraft is detected, β j =1, otherwise β j >1; when the flight segment passes through the target threat area, χ j =1, otherwise χ j =0.

5. A trajectory planning method according to claim 1, characterized in that: The constraint conditions of the aircraft's track model before and after bombing are one or more combinations of track total length constraint, maximum and minimum flight altitude constraint, maximum climb / descent angle constraint, minimum turning radius constraint, minimum track segment length constraint and fuel consumption constraint.

6. A trajectory planning method according to claim 5, characterized in that: The total track length constraint is: Among them, ||l i || is the track segment l i Length; The maximum and minimum flight altitude constraints are: H min ≤H≤H max ; The maximum climb / descent angle constraint is: The minimum turning radius constraint is: r k ≥r min ; The minimum track segment length constraint is: l i ≥l min (i=1,2,...,n) The fuel consumption constraint is: (F P1 t1+F P2 t2)≤F max <F0.

7. A trajectory planning method according to claim 1, characterized in that: The method for obtaining the optimal track by the symbiotic organism search model includes: Step 1: Initialize the size of the biological population N, the upper and lower bounds of the search space ub and lb, and the maximum number of iterations iterMax; Step 2: Identify the organism X with the best fitness best ; Step 3: Randomly select two different individuals X from the biological population i and X j , generate the relationship feature vector Mutual_Vector between them, and generate a new individual X i_new and X j_new ; For biological individual X i and X j , if f(X i ) <f(X i_new ) or f(X j ) <f(X j_new ), then choose to accept the new individual X i_new , X j_new ; Step 4: Randomly select two different individuals X from the biological population i and X j , generate a new individual X i_new ; For biological individual X i , if f(X i ) <f(X i_new ), then choose to accept the new individual X i_new ; Step 5: Generate mutation vector Parasite_Vector; for biological individual X i , if f(Parasite_Vector) <f(X j ), then choose to accept the new individual X i_new ; Step 6: If the number of individuals in the population is reached, go to step 7, otherwise go to step 2 and continue the next search; Step 7: If the termination criteria are met, output the global optimal solution X best ; Otherwise, enter the iteration counter, record and continue the next search.

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