Aircraft trajectory planning method, device, computer equipment and storage medium

Abstract

The present invention provides a method, device, computer equipment and storage medium for aircraft trajectory planning. The aircraft trajectory planning disclosed in the present invention mainly includes: constructing an improved A* algorithm based on a fading factor ζ; determining a combat scenario description of multiple aircraft; and performing trajectory planning for multiple aircraft based on the improved A* algorithm and the combat scenario description. By adding a fading factor ζ to the original heuristic function, the algorithm gradually eliminates the influence of the known cost G as the nodes expand, thereby solving the shortcoming of the existing algorithm that the planning efficiency decreases as the number of aircraft nodes increases; by determining the combat scenario description of multiple aircraft, the aircraft formation is divided into a leading aircraft and a following aircraft, thereby solving the trajectory planning problem in a multi-formation aircraft cooperative combat scenario. The algorithm is simple and easy to implement, and the complexity of the trajectory planning does not increase with the increase in the number of aircraft, which can meet the ever-changing battlefield environment.

Description

Technical Field

[0001] The present invention relates to the technical field of aircraft trajectory planning, and in particular to an aircraft trajectory planning method, device, computer equipment and storage medium. Background Art

[0002] At present, the problem of aircraft trajectory planning has been widely studied at home and abroad. So far, the problem of trajectory planning has been widely studied in the field of UAVs. The problem of UAV route planning refers to how to successfully avoid obstacles in the environment and reach the target area when the UAV performs the corresponding flight mission from the starting point to the target point. In the research on the theoretical algorithm of route planning for formation UAVs, domestic and foreign scholars have proposed many algorithms on how to plan the optimal path for UAV formation flight, and have also achieved many related research results. Among these research results, the more prominent algorithms are: Ant Colony Algorithm (ACA), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Artificial Potential Field (APF), Rapidly-exploring Random Trees (RRT), etc.

[0003] With the deepening of research on multi-individual cooperative control, research on aircraft cooperative combat has also made corresponding progress. Therefore, it is necessary to consider task cooperative planning. On the basis of comprehensively considering the flight cost and combat effect of the aircraft formation, a new idea of ​​aircraft trajectory planning is proposed, that is, the cooperative trajectory planning of the aircraft formation system is combined with task allocation, and the aircraft trajectory planning method based on task allocation is studied to meet the purpose and requirements of multi-aircraft cooperative combat.

[0004] Among the current research results, most of the existing algorithms have the disadvantage that the planning efficiency decreases as the number of aircraft nodes increases. At the same time, the collaborative planning algorithms are generally more complex, and most trajectory planning methods are time-consuming and cannot meet the ever-changing battlefield environment. Therefore, it is of great engineering significance to study a trajectory planning method that converges faster, is simple in form, easy to implement, and meets the requirements of collaborative conditions. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides an aircraft trajectory planning method, device, computer equipment and storage medium.

[0006] In a first aspect, in one embodiment, the present invention provides an aircraft trajectory planning method, comprising:

[0007] S1: Construct an improved A* algorithm based on the fading factor ζ;

[0008] Step S1 includes:

[0009] S11: Obtain the fading factor ζ;

[0010] S12: Obtain the original heuristic function in the original A* algorithm;

[0011] The original heuristic function is shown in formula (1):

[0012] F=G+H (1)

[0013] S13: obtaining an improved heuristic function according to the fading factor ζ and the original heuristic function;

[0014] The improved heuristic function is shown in formula (2):

[0015] F=ζG+H (2)

[0016] Among them, in formula (1) and formula (2), the G value is the cost of moving from the starting point to the current node, the H value is the estimated cost of moving from the current node to the end point, and the F value is the sum of the G value and the H value;

[0017] S14: obtaining an improved A* algorithm according to the improved heuristic function;

[0018] S2: Determine the combat scenario description for multiple aircraft;

[0019] S3: Perform trajectory planning for multiple aircraft based on the improved A* algorithm and combat scenario description.

[0020] In one embodiment, step S2 includes:

[0021] S21: dividing the multiple aircraft into a plurality of formations, each formation including a leading aircraft and a plurality of following aircraft;

[0022] Among them, the leading aircraft tracks the reference track points through inertial navigation or combined navigation; the following aircraft forms and maintains the desired formation according to the formation control algorithm, and makes corresponding formation changes according to mission requirements.

[0023] In one embodiment, step S3 includes:

[0024] S31: Abstract multiple hazards into no-fly zones; no-fly zones represent areas of bad weather, enemy threats, and / or altitude exceeding thresholds;

[0025] S32: Determine the restriction relationship between the no-fly zone and the extended node;

[0026] S33: construct an alternative set including multiple expansion nodes;

[0027] S34: determining an extension node from multiple extension nodes in the candidate set based on the improved A* algorithm and the restriction relationship between the no-fly zone and the extension node;

[0028] S35: Based on the determined expansion nodes, reference track points for track planning are formed.

[0029] In one embodiment, the i-th no-fly zone is represented by X fbdi =(x fbdi ,y fbdi ), the corresponding no-fly zone radius is r fbdi ; Extension nodes are represented by X f =(x f ,y f ); the step S32 mainly comprises:

[0030] S321: Determine the safety radius r sa f e ;

[0031] S322: According to the safety radius r safe Get the restriction relationship between the no-fly zone and the extended node;

[0032] The restriction relationship between the no-fly zone and the extended nodes is shown in formula (3):

[0033] ||X fbdi -X f ||2>r fbdi +r safe (3)

[0034] In formula (3), ||X fbdi -X f ||2 means X fbdi -X f The second norm of .

[0035] In one embodiment, in step S33, the candidate set is as shown in formula (4):

[0036]

[0037] In formula (4), X n =(x n ,y n ) indicates the current node position, Indicates the position of the i-th node expanded under the current node; l min =[l min l min ] T ; T i It is expressed as an extended matrix related to the current heading state of the node, as shown in formula (5):

[0038]

[0039] In formula (5), M represents the number of candidate expansion nodes, M = 2n + 1, n∈Ν, φ max represents the maximum turning angle,

[0040] In one embodiment, step S34 mainly includes:

[0041] S341: Create OpenList set and CloseList set;

[0042] Among them, the OpenList set is used to store the expansion nodes to be detected, and the CloseList set is used to store the expansion nodes that have been detected; each expansion node in the OpenList set and the CloseList set has a parent node, an F value, a G value, and an H value, and the parent node is the previous node in the path that passes through the expansion node;

[0043] S342: Push the current node into the CloseList set;

[0044] S343: Determine whether the path planning is completed. If the path planning is completed, then end, otherwise go to step S344;

[0045] S344: Expand M expansion nodes according to the constructed node expansion algorithm;

[0046] S345: traverse and judge all expansion nodes. If the expansion node is outside the map boundary, or has been pressed into the CloseList set, or belongs to a no-fly zone, return to step S344. Otherwise, update the values ​​of G and H, and calculate F = γG + H.

[0047] S346: Determine whether the newly obtained extension node has been pushed into the OpenList set. If it has been, store the extension node with a smaller G value. If not, push it into the OpenList set.

[0048] S347: Generate a random number P with uniform distribution of [0 1], and compare it with the set probability P*. If P>P*, select the expansion node with the smallest F in the OpenList set, otherwise randomly select an expansion node in the OpenList set; as shown in formula (6):

[0049]

[0050] S348: Update the selected expansion node as the current node, and return to step S342.

[0051] In a second aspect, in one embodiment, the present invention provides an aircraft trajectory planning device, comprising:

[0052] Improved A* algorithm building module, used to build an improved A* algorithm based on the fading factor ζ;

[0053] The improved A* algorithm building blocks include:

[0054] A fading factor ζ acquisition module, used to acquire the fading factor ζ;

[0055] The original heuristic function acquisition module is used to obtain the original heuristic function in the original A* algorithm;

[0056] An improved heuristic function acquisition module is used to obtain an improved heuristic function according to a fading factor ζ and an original heuristic function;

[0057] An improved A* algorithm acquisition module is used to obtain an improved A* algorithm according to an improved heuristic function;

[0058] An aircraft combat scenario description module, used to determine combat scenario descriptions for multiple aircraft;

[0059] The aircraft trajectory planning module is used to plan the trajectories of multiple aircraft based on the improved A* algorithm and combat scenario description.

[0060] In one embodiment, the aircraft trajectory planning module includes:

[0061] The no-fly zone representation module is used to abstract various hazards into no-fly zones;

[0062] A module for obtaining the restriction relationship between the no-fly zone and the extended node, used to determine the restriction relationship between the no-fly zone and the extended node;

[0063] An alternative set construction module for extended nodes, used to construct an alternative set containing multiple extended nodes;

[0064] An expansion node determination module, used to determine an expansion node from multiple expansion nodes in the candidate set based on the improved A* algorithm and the restriction relationship between the no-fly zone and the expansion node;

[0065] The reference track point acquisition module is used to form reference track points for track planning based on the determined expansion nodes.

[0066] In a third aspect, in one embodiment, the present invention proposes a computer device, including a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the processor executes the steps of the aircraft trajectory planning method in any of the above-mentioned embodiments.

[0067] In a fourth aspect, in one embodiment, the present invention proposes a storage medium storing a computer program, which, when executed by a processor, enables the processor to execute the steps of the aircraft trajectory planning method in any of the above-mentioned embodiments.

[0068] Compared with the prior art, the present invention has the following beneficial effects:

[0069] 1. Construct an improved A* algorithm based on the fading factor ζ. Add a fading factor ζ on top of the original heuristic function so that the algorithm can gradually eliminate the influence of the known cost G as the nodes expand, solving the problem that the planning efficiency in the existing algorithm decreases with the increase in the number of aircraft nodes.

[0070] 2. Determine the combat scenario description of multiple aircraft and divide the aircraft formation into leading aircraft and following aircraft. The algorithm is simple and easy to implement, and the complexity of trajectory planning does not increase with the increase in the number of aircraft, which can meet the ever-changing battlefield environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 A schematic diagram of a flow chart of a method for trajectory planning of an aircraft in one embodiment of the present invention;

[0072] Figure 2 A schematic diagram of a specific process of constructing an improved A* algorithm based on a fading factor ζ in one embodiment of the present invention;

[0073] Figure 3 A schematic diagram of a specific process for determining a combat scenario for multiple aircraft in one embodiment of the present invention;

[0074] Figure 4 A schematic diagram of a specific process of performing trajectory planning for multiple aircraft based on an improved A* algorithm and a combat scenario description in one embodiment of the present invention;

[0075] Figure 5 A schematic diagram of a specific process for determining a restriction relationship between a no-fly zone and an extended node in one embodiment of the present invention;

[0076] Figure 6 A schematic diagram of a specific process of determining an extension node from multiple extension nodes in a candidate set based on an improved A* algorithm and a restriction relationship between extension nodes in a no-fly zone in an embodiment of the present invention;

[0077] Figure 7 A schematic diagram of aircraft cooperative trajectory planning operations in one embodiment of the present invention;

[0078] Figure 8 The track planning result based on the improved A* algorithm in one embodiment of the present invention;

[0079] Fig. 9 The simulation result of the aircraft track tracking algorithm in one embodiment of the present invention;

[0080] Fig.10 The A* algorithm and its improved effect obtained when the fading factor is 1 under the Euclidean distance cost in one embodiment of the present invention;

[0081] Fig.11 The A* algorithm and its improved effect obtained when the fading factor is 0.9 under the Euclidean distance cost in one embodiment of the present invention;

[0082] Fig.12 The A* algorithm and its improved effect obtained when the fading factor is 0.09 under the Euclidean distance cost in one embodiment of the present invention;

[0083] Fig.13 The A* algorithm and its improved effect obtained when the fading factor is 1 under the Manhattan distance cost in one embodiment of the present invention;

[0084] Fig.14 The A* algorithm and its improved effect obtained when the fading factor is 0.9 under the Manhattan distance cost in one embodiment of the present invention;

[0085] Fig.15 The A* algorithm and its improved effect obtained when the fading factor is 0.09 at the Manhattan distance cost in one embodiment of the present invention. DETAILED DESCRIPTION

[0086] The technical solution of the present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0087] In view of the deficiencies in the prior art, the present invention provides an aircraft trajectory planning method, device, computer equipment and storage medium.

[0088] In one embodiment, the first aspect is as follows: Figure 1-2 As shown, the present invention provides an aircraft trajectory planning method, comprising:

[0089] S1: Construct an improved A* algorithm based on the fading factor ζ;

[0090] Wherein, step S1 comprises:

[0091] S11: Obtain the fading factor ζ;

[0092] S12: Obtain the original heuristic function in the original A* algorithm;

[0093] The original heuristic function is shown in formula (1):

[0094] F=G+H (1)

[0095] Where G is the cost of moving from the starting point to the current node, H is the estimated cost of moving from the current node to the end point, and F is the sum of G and H. Since the algorithm is iterative, the specific expression of G is shown in formula (2):

[0096] G=G n +G father (2)

[0097] In the formula, G father Represents the cost of its parent node, G n The cost from the parent node to the current node, G n The value of the parent node N f and the current node N n The state calculation of H is through the target node N T and the current node N n The state calculation of H and G n The expression of is shown in formula (3):

[0098] H=ρ(N T ,N n )

[0099] G n =ρ(N f ,N n ) (3) where ρ(N i ,N j ) represents node N i With node N j The cost calculation function.

[0100] S13: obtaining an improved heuristic function according to the fading factor ζ and the original heuristic function;

[0101] The improved heuristic function is shown in formula (4):

[0102] F=ζG+H (4)

[0103] In step S13, a gradual elimination factor ζ is added on the basis of the original A* algorithm, so as to enable the algorithm to continuously reduce the influence of the known cost G in the process of continuously expanding the node to the target node, wherein 0<ζ≤1; when ζ=1, the algorithm is the original A* algorithm. In addition, the smaller ζ is, the fewer nodes need to be expanded, the smaller the algorithm search space is, and the search rate will be greatly improved.

[0104] S14: obtaining an improved A* algorithm according to the improved heuristic function;

[0105] S2: Determine the combat scenario description for multiple aircraft;

[0106] S3: Perform trajectory planning for multiple aircraft based on the improved A* algorithm and combat scenario description.

[0107] In the above embodiment, the improved A* algorithm based on the fading factor ζ is derived from the original A* algorithm. In the heuristic function of the original A* algorithm, as the node continues to expand toward the target node, its cost G will continue to accumulate. If an obstacle node is encountered, when the node is expanded near the obstacle node, the cost G will be superimposed, which will cause the value of the entire F to increase. Since the algorithm will select the node to be expanded with the smallest F, it will select a position closer to the initial node, that is, the "backtracking effect". Since these node positions are far from the target node position, the number of search nodes is increased, and the planning time is extended. The present invention adds a fading factor ζ in step S1. As the node expands, the influence of the known cost G can be gradually eliminated, thereby improving the search rate of the algorithm.

[0108] In one embodiment, Figure 3 As shown, step S2 includes:

[0109] S21: dividing the multiple aircraft into a plurality of formations, each formation including a leading aircraft and a plurality of following aircraft;

[0110] Among them, the leading aircraft realizes the tracking of the reference track point through inertial navigation or combined navigation;

[0111] The following aircraft forms and maintains the desired formation according to the formation control algorithm, and performs corresponding formation changes according to the mission requirements. In addition, during the formation flight, the following aircraft does not perform trajectory planning;

[0112] When the leading aircraft fails, an aircraft is selected from the following aircraft as the leading aircraft, and the aircraft formation continues to fight in coordination.

[0113] In the above embodiment, the aircraft combat scenario description provides a stable and reliable flight plan for the aircraft combat system, solves the trajectory planning problem in the scenario of coordinated combat of multiple formations of aircraft, the algorithm is simple and easy to implement, and the complexity of trajectory planning does not increase with the increase in the number of aircraft, and can meet the ever-changing battlefield environment.

[0114] In a preferred embodiment, Figure 7 The multi-aircraft coordinated combat process shown in the figure consists of aircraft F ii 、Aircraft F ij 、Aircraft F ik Form an aircraft formation i, aircraft F ji 、Aircraft F jj 、Aircraft F jk The aircraft formation j is composed of aircraft Fij 、Aircraft F jj They are the leading aircraft in aircraft formation i and aircraft formation j respectively, and the remaining aircraft are the following aircraft in the formation. The following aircraft in each formation takes the leading aircraft as a reference.

[0115] Leader aircraft F ij 、F jj Tracking of reference track points is achieved through inertial navigation or integrated navigation, providing reference for following aircraft during formation flying;

[0116] The following aircraft forms and maintains the desired formation according to the formation control algorithm, and performs corresponding formation changes according to the mission requirements. In addition, during the formation flight, the following aircraft does not perform trajectory planning;

[0117] When the leader aircraft F ij or F jj After the failure, an aircraft is selected from the following aircraft as the leader aircraft, and the aircraft formation continues to fight in coordination.

[0118] In the above embodiment, if the following aircraft and the leading aircraft maintain formation flight, when the leading aircraft F ij and F jj When they are able to reach the combat area at the same time, all aircraft in the two formations can reach the combat area at the same time and launch attacks on the target.

[0119] In one embodiment, Figure 4 As shown, step S3 includes:

[0120] S31: Abstracting multiple dangers into no-fly zones;

[0121] The no-fly zone is set as a circular no-fly zone, the center of the circle is represented as the center of the no-fly zone, and the radius of the no-fly zone needs to ensure that the no-fly zone is covered. When the planning result does not meet the expected trajectory, the safety radius of the no-fly zone can be further reduced, or other forms of no-fly zones can be considered; the no-fly zone represents an area with bad weather, enemy threats and / or altitude exceeding a threshold;

[0122] S32: Determine the restriction relationship between the no-fly zone and the extended node;

[0123] S33: construct an alternative set including multiple expansion nodes;

[0124] S34: determining an extension node from multiple extension nodes in the candidate set based on the improved A* algorithm and the restriction relationship between the no-fly zone and the extension node;

[0125] S35: Based on the determined expansion nodes, reference track points for track planning are formed.

[0126] In one embodiment, Figure 5 As shown, the i-th no-fly zone is represented by X fbdi =(x fbdi ,y fbdi ), the corresponding no-fly zone radius is r fbdi ; The expansion node is represented by X f =(x f ,y f ); the step S32 mainly comprises:

[0127] S321: Determine the safety radius r safe ;

[0128] S322: According to the safety radius r safe Get the restriction relationship between the no-fly zone and the extended node;

[0129] The restriction relationship between the no-fly zone and the extended nodes is shown in formula (5):

[0130] ||X fbdi -X f ||2>r fbdi +r safe (5)

[0131] In formula (5), ||X fbdi -X f ||2 means X fbdi -X f The second norm of .

[0132] In one embodiment, since the aircraft track points are not grids, the track extension direction is affected by the current heading angle θ, and the extension range is affected by the maximum heading angle φ max And the shortest planning distance limit l min Restriction, in step S33, in step S33, the candidate set is as shown in formula (6):

[0133]

[0134] In formula (6), X n =(x n ,y n ) indicates the current node position, Indicates the position of the i-th node expanded under the current node; l min =[l min l min ] T ; T i It is expressed as an extended matrix related to the current heading state of the node, as shown in formula (7):

[0135]

[0136] In formula (7), M represents the number of candidate expansion nodes, M = 2n + 1, n∈Ν, φ max represents the maximum turning angle, The larger M is, the more nodes need to be expanded, and the slower the planning efficiency; φ max It is related to the normal overload capacity of the aircraft itself and should not be too large; min It determines the node expansion step size. If the value is too small, the planning solution rate will be slowed down. If it is too large, it may not be possible to plan the track points. Therefore, it is necessary to select M and φ appropriately. max and l min The numerical value of .

[0137] In one embodiment, Figure 6 As shown, step S34 mainly includes:

[0138] S341: Create OpenList set and CloseList set;

[0139] Among them, the OpenList set is used to store the expansion nodes to be detected, and the CloseList set is used to store the expansion nodes that have been detected; each expansion node in the OpenList set and the CloseList set has a parent node, an F value, a G value, and an H value, and the parent node is the previous node in the path that passes through the expansion node;

[0140] S342: Push the current node into the CloseList set;

[0141] S343: Determine whether the path planning is completed. If the path planning is completed, then end, otherwise go to step S344;

[0142] S344: Expand M expansion nodes according to the constructed node expansion algorithm;

[0143] S345: traverse and judge all expansion nodes. If the expansion node is outside the map boundary, or has been pressed into the CloseList set, or belongs to a no-fly zone, return to step S344. Otherwise, update the values ​​of G and H, and calculate F = γG + H.

[0144] S346: Determine whether the newly obtained extension node has been pushed into the OpenList set. If it has been, store the extension node with a smaller G value. If not, push it into the OpenList set.

[0145] S347: Generate a random number P with uniform distribution of [0 1], and compare it with the set probability P*. If P>P*, select the expansion node with the smallest F in the OpenList set, otherwise randomly select an expansion node in the OpenList set; as shown in formula (8):

[0146]

[0147] S348: Update the selected expansion node as the current node, and return to step S342.

[0148] In the above embodiment, the node selection strategy is no longer simply selected by the minimum F, but introduces random distribution, selects the node with the minimum F with a certain probability P*, and randomly selects feasible nodes in other cases. This solves the problem that when there are obstacles at a position close to the target node, the algorithm will continuously expand nodes near the minimum F, and the other feasible nodes will not be selected, thereby improving the search rate of the algorithm.

[0149] In a second aspect, in one embodiment, the present invention provides an aircraft trajectory planning device, comprising:

[0150] Improved A* algorithm building module, used to build an improved A* algorithm based on the fading factor ζ;

[0151] The improved A* algorithm building blocks include:

[0152] A fading factor ζ acquisition module, used to acquire the fading factor ζ;

[0153] The original heuristic function acquisition module is used to obtain the original heuristic function in the original A* algorithm;

[0154] An improved heuristic function acquisition module is used to obtain an improved heuristic function according to a fading factor ζ and an original heuristic function;

[0155] An improved A* algorithm acquisition module is used to obtain an improved A* algorithm according to an improved heuristic function;

[0156] An aircraft combat scenario description module, used to determine combat scenario descriptions for multiple aircraft;

[0157] The aircraft trajectory planning module is used to plan the trajectories of multiple aircraft based on the improved A* algorithm and combat scenario description.

[0158] In the above embodiment, the improved A* algorithm construction module constructs an improved A* algorithm based on the fading factor ζ, and adds a fading factor ζ on top of the original heuristic function, so that the algorithm gradually eliminates the influence of the known cost G as the nodes expand, solving the problem that the planning efficiency in the existing algorithm decreases as the number of aircraft nodes increases;

[0159] The aircraft combat scenario description module determines the combat scenario description of multiple aircraft and divides the aircraft formation into leading aircraft and following aircraft. The algorithm is simple and easy to implement, and the complexity of trajectory planning does not increase with the increase in the number of aircraft, which can meet the ever-changing battlefield environment.

[0160] In one embodiment, the aircraft trajectory planning module includes:

[0161] The no-fly zone representation module is used to abstract various hazards into no-fly zones;

[0162] A module for obtaining the restriction relationship between the no-fly zone and the extended node, used to determine the restriction relationship between the no-fly zone and the extended node;

[0163] An alternative set construction module for extended nodes, used to construct an alternative set containing multiple extended nodes;

[0164] An expansion node determination module, used to determine an expansion node from multiple expansion nodes in the candidate set based on the improved A* algorithm and the restriction relationship between the no-fly zone and the expansion node;

[0165] The reference track point acquisition module is used to form reference track points for track planning based on the determined expansion nodes.

[0166] In the above embodiment, the node selection strategy of the expansion node determination module is no longer simply selected by the minimum F, but introduces random distribution, selects the node with the minimum F with a certain probability P*, and randomly selects feasible nodes in other cases. This solves the problem that when there are obstacles at a position close to the target node, the algorithm will continuously expand nodes near the minimum F, and the other feasible nodes will not be selected, thereby improving the search rate of the algorithm.

[0167] In a third aspect, in one embodiment, the present invention proposes a computer device, including a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the processor executes the steps of the aircraft trajectory planning method in any of the above-mentioned embodiments.

[0168] In the above embodiment, an improved A* algorithm based on a fading factor ζ is constructed, and a fading factor ζ is added to the original heuristic function, so that the algorithm gradually eliminates the influence of the known cost G as the nodes expand, thereby solving the problem that the planning efficiency in the existing algorithm decreases as the number of aircraft nodes increases.

[0169] A combat scenario description for multiple aircraft was determined, and the aircraft formation was divided into a leading aircraft and a following aircraft. The algorithm is simple and easy to implement, and the complexity of trajectory planning does not increase with the increase in the number of aircraft, which can meet the ever-changing battlefield environment.

[0170] In a fourth aspect, in one embodiment, the present invention proposes a storage medium storing a computer program, which, when executed by a processor, enables the processor to execute the steps of the aircraft trajectory planning method in any of the above-mentioned embodiments.

[0171] In the above embodiment, an improved A* algorithm based on a fading factor ζ is constructed, and a fading factor ζ is added to the original heuristic function, so that the algorithm gradually eliminates the influence of the known cost G as the nodes expand, thereby solving the problem that the planning efficiency in the existing algorithm decreases as the number of aircraft nodes increases.

[0172] A combat scenario description for multiple aircraft was determined, and the aircraft formation was divided into a leading aircraft and a following aircraft. The algorithm is simple and easy to implement, and the complexity of trajectory planning does not increase with the increase in the number of aircraft, which can meet the ever-changing battlefield environment.

[0173] To verify the effectiveness of the improved A* algorithm, in a 50×50 grid scene with obstacles (the obstacles are not shown), the improved A* algorithm and its aircraft trajectory planning method are used to find a path from the initial node N0=(0,0) to the target node N T =(50,50) feasible trajectory points, respectively select the Euclidean distance cost calculation function And Manhattan distance cost calculation function ρ(N i ,N j )=|x i -x j |+|y i -y j | and other two cost calculation methods for comparative analysis, Figure 10-Figure 15 The results of the algorithm search are shown separately. All the black squares in the figure represent nodes that have been expanded and searched, and the white squares represent nodes to be searched. It can be seen that as the fading factor ζ decreases, the number of nodes searched by the algorithm becomes smaller and smaller, indicating that the improved method improves the efficiency of the search and reduces the search space, proving that the improvement is effective. At the same time, the comparison Figure 10-12 and Figure 13-15 It can be seen that different cost functions will also affect the search rate and search space of the algorithm, so it is necessary to select an appropriate cost calculation method.

[0174] In order to verify the proposed ability to plan the trajectory of multiple aircraft based on the improved A* algorithm and combat scenario description, four target locations, (100km, 100km), (80km, 100km), (60km, 100km), and (40km, 100km), were selected to test the trajectory planning method. The threats and no-fly zones in the simulation are uniformly modeled as circular no-fly zones, the maximum deflection angle of the aircraft is 20°, and the trajectory planning step length is 3km. The simulation results are shown in Figure 2. Figure 8 As shown in Figure 1, the trajectory planning method can avoid no-fly zones and has the ability to plan the trajectory. The target point (100km, 100km) is selected for trajectory tracking verification. The trajectory tracking algorithm uses the proportional guidance law. The trajectory tracking effect is shown in Figure 1. Fig. 9 As shown in the figure, it can be seen that the planned track points can be tracked well. Although the track has a large curvature near the no-fly zone, it is still feasible.

[0175] In trajectory tracking, the aircraft motion model used is shown in formula (9):

[0176]

[0177] In the formula, (x, y), V, θ, and a represent the current position, velocity, heading angle, and normal overload of the aircraft respectively. c is the normal overload instruction, and its expression is λ=arctan((y next -y) / (x next -x)), (x next ,y next ) indicates the location of the next planned track point.

[0178] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solution of the present invention, which should be included in the scope of the claims of the present invention.

Claims

1. A method for aircraft trajectory planning, characterized in that: Mainly include: S1: Construct an improved A* algorithm based on the fading factor ζ; Step S1 includes: S11: Obtain the fading factor ζ; S12: Obtain the original heuristic function in the original A* algorithm; The original heuristic function is shown in formula (1): F=G+H (1) S13: obtaining an improved heuristic function according to the fading factor ζ and the original heuristic function; The improved heuristic function is shown in formula (2): F=ζG+H (2) Among them, in formula (1) and formula (2), the G value is the cost of moving from the starting point to the current node, the H value is the estimated cost of moving from the current node to the end point, and the F value is the sum of the G value and the H value; S14: obtaining an improved A* algorithm according to the improved heuristic function; S2: Determine the combat scenario description for multiple aircraft; S3: Trajectory planning for multiple aircraft based on the improved A* algorithm and combat scenario description; The step S3 comprises: S31: Abstract multiple hazards into no-fly zones; no-fly zones represent areas of bad weather, enemy threats, and / or altitude exceeding thresholds; S32: Determine the restriction relationship between the no-fly zone and the extended node; S33: construct an alternative set including multiple expansion nodes; S34: determining an extension node from multiple extension nodes in the candidate set based on the improved A* algorithm and the restriction relationship between the no-fly zone and the extension node; S35: forming reference track points for track planning according to the determined expansion nodes; The i-th no-fly zone is denoted as X fbdi =(x fbdi ,y fbdi ), the corresponding no-fly zone radius is r fbdi ; Extension nodes are represented by X f =(x f ,y f ); the step S32 mainly comprises: S321: Determine the safety radius r safe ; S322: According to the safety radius r safe Get the restriction relationship between the no-fly zone and the extended node; The restriction relationship between the no-fly zone and the extended nodes is shown in formula (3): ||X fbdi -X f ||2>r fbdi +r safe (3) In formula (3), ||X fbdi -X f ||2 means X fbdi -X f The second norm of .

2. The method for aircraft trajectory planning according to claim 1, characterized in that: The step S2 comprises: S21: dividing the multiple aircraft into a plurality of formations, each formation including a leading aircraft and a plurality of following aircraft; Among them, the leading aircraft tracks the reference track points through inertial navigation or combined navigation; the following aircraft forms and maintains the desired formation according to the formation control algorithm, and makes corresponding formation changes according to mission requirements.

3. The method for aircraft trajectory planning according to claim 1, characterized in that: In step S33, the candidate set is as shown in formula (4): In formula (4), X n =(x n ,y n ) indicates the current node position, Indicates the position of the i-th expanded node under the current node; l min =[l min l min ] T ; T i It is expressed as an extended matrix related to the current heading state of the node, as shown in formula (5): In formula (5), M represents the number of candidate expansion nodes, M = 2n + 1, n∈Ν, φ max represents the maximum turning angle, 4. The method for aircraft trajectory planning according to claim 3, characterized in that: The step S34 mainly includes: S341: Create OpenList set and CloseList set; Among them, the OpenList set is used to store the expansion nodes to be detected, and the CloseList set is used to store the expansion nodes that have been detected; each expansion node in the OpenList set and the CloseList set has a parent node, an F value, a G value, and an H value, and the parent node is the previous node in the path that passes through the expansion node; S342: Push the current node into the CloseList set; S343: Determine whether the path planning is completed. If the path planning is completed, then end, otherwise go to step S344; S344: Expand M expansion nodes according to the constructed node expansion algorithm; S345: traverse and judge all expansion nodes. If the expansion node is outside the map boundary, or has been pressed into the CloseList set, or belongs to a no-fly zone, return to step S344. Otherwise, update the values ​​of G and H, and calculate F = γG + H. S346: Determine whether the newly obtained extension node has been pushed into the OpenList set. If it has been, store the extension node with a smaller G value. If not, push it into the OpenList set. S347: Generate a random number P with uniform distribution of [0 1], and compare it with the set probability P*. If P>P*, select the expansion node with the smallest F in the OpenList set, otherwise randomly select an expansion node in the OpenList set; as shown in formula (6): S348: Update the selected expansion node as the current node, and return to step S342.

5. An aircraft trajectory planning device, characterized in that: include: Improved A* algorithm building module, used to build an improved A* algorithm based on the fading factor ζ; The improved A* algorithm building blocks include: A fading factor ζ acquisition module, used to acquire the fading factor ζ; The original heuristic function acquisition module is used to obtain the original heuristic function in the original A* algorithm; An improved heuristic function acquisition module is used to obtain an improved heuristic function according to a fading factor ζ and an original heuristic function; An improved A* algorithm acquisition module is used to obtain an improved A* algorithm according to an improved heuristic function; An aircraft combat scenario description module, used to determine combat scenario descriptions for multiple aircraft; Aircraft trajectory planning module, used to plan the trajectory of multiple aircraft based on the improved A* algorithm and combat scenario description; The trajectory planning of multiple aircraft based on the improved A* algorithm and combat scenario description includes: Abstract multiple hazards into no-fly zones; no-fly zones represent areas of severe weather, enemy threats, and / or altitudes exceeding thresholds; Determine the restriction relationship between the no-fly zone and the extended nodes; Construct an alternative set containing multiple extension nodes; Based on the improved A* algorithm and the restriction relationship between the no-fly zone and the expansion node, an expansion node is determined from multiple expansion nodes in the candidate set; Based on the determined expansion nodes, reference track points for track planning are formed; The i-th no-fly zone is denoted as X fbdi =(x fbdi ,y fbdi ), the corresponding no-fly zone radius is r fbdi ; Extension nodes are represented by X f =(x f ,y f ); Determine the restriction relationship between the no-fly zone and the extended node mainly includes: Determine the safety radius r safe ; According to the safety radius r safe Get the restriction relationship between the no-fly zone and the extended node; The restriction relationship between the no-fly zone and the extended nodes is shown in formula (3): ||X fbdi -X f ||2>r fbdi +r safe (3) In formula (3), ||X fbdi -X f ||2 means X fbdi -X f The second norm of .

6. The aircraft trajectory planning device according to claim 5, characterized in that: The aircraft trajectory planning module includes: The no-fly zone representation module is used to abstract various hazards into no-fly zones; A module for obtaining the restriction relationship between the no-fly zone and the extended node, used to determine the restriction relationship between the no-fly zone and the extended node; An alternative set construction module for extended nodes, used to construct an alternative set containing multiple extended nodes; An expansion node determination module, used to determine an expansion node from multiple expansion nodes in the candidate set based on the improved A* algorithm and the restriction relationship between the no-fly zone and the expansion node; The reference track point acquisition module is used to form reference track points for track planning based on the determined expansion nodes.

7. A computer device comprising a memory and a processor, wherein a computer program is stored in the memory, wherein: When the computer program is executed by the processor, the processor executes the steps of the aircraft trajectory planning method according to any one of claims 1 to 4.

8. A storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the processor executes the steps of the aircraft trajectory planning method as described in any one of claims 1 to 4.

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