A high-speed unmanned aerial vehicle long-winged drone cooperative flight route autonomous generation method

Abstract

The application discloses a kind of high-speed unmanned aerial vehicle long-servant cooperative flight route autonomous generation method, first according to flight task to determine high-speed unmanned aerial vehicle long machine preplanning route and long-servant machine cooperation relationship;Second, the non-must point constraint space geometric broken line planning of long machine preplanning route is carried out, and long machine broken line route is generated;Third, the constraint space self-adapting height heave planning of long machine broken line route is carried out, and long machine planning route is generated;For non-must point, with heave constraint height as waypoint height;For must point, by generating self-adapting height heave three-dimensional zigzag gradient flight section, realize waypoint height accessible;Finally, according to the relative position relationship between long-servant machine and the requirement of cooperative flight section, long machine planning route is segmented and self-adapting cooperative matching planning is carried out, and long-servant machine cooperative flight route is generated.This method can autonomously generate flight route meeting high-speed unmanned aerial vehicle performance constraint and long-servant machine cooperation relationship, and has strong engineering practicability and is easy to use.

Description

Technical Field

[0001] This invention belongs to the field of unmanned aerial vehicle (UAV) route planning technology, and in particular relates to a method for autonomously generating flight routes for high-speed UAVs in a leader-colleague cooperative manner. Background Technology

[0002] After acquiring the flight mission to be performed, the high-speed UAV route planning subsystem optimizes the flight path within the mission area to meet relevant performance constraints, such as turning radius and climb rate. High-speed UAV route planning needs to handle unstructured, large-scale, and complex planning environments. Furthermore, to meet collaborative requirements, it needs to plan multiple UAV routes simultaneously and maintain certain collaborative relationships, ensuring that the unmanned combat aircraft arrive at / depart from their respective mission segments at specified entry / exit angles. This poses a significant challenge to many traditional path planning algorithms (such as the A* algorithm and dynamic programming algorithms), as excessively long runtimes often render them impractical.

[0003] In practical applications of high-speed UAVs, a rather cumbersome method of manually drawing flight paths is often employed. This involves combining extensive flight experience with a deep understanding of the performance of high-speed UAVs to create a flyable route. This planning process relies entirely on human experience and multiple rounds of simulation iterations, placing enormous pressure on human resources and time costs for flight missions.

[0004] Currently, many domestic institutions are conducting research on UAV route planning. The patent "An Intelligent Collaborative Trajectory Planning Method for UAV Formation Flight in Dynamic Environments" (Patent No.: CN201410577358.0) discloses an intelligent collaborative trajectory planning method for UAV formation flight in dynamic environments, including offline pre-flight collaborative trajectory planning based on Voronoi diagrams and ant colony algorithms, online trajectory replanning based on RRT, and formation reconstruction. However, the ant colony algorithm has a slow convergence speed when facing large task spaces in complex environments, and the iteration of pre-planned routes consumes a significant amount of time. The patent "An Automatic Generation Method for Safe Operation Routes of UAV Swarms Based on Spatial Grids" (Patent No.: CN202211704666.6) discloses an automatic generation method for safe operation routes of UAV swarms based on spatial grids. It constructs feasible routes through spatial grid modeling and artificial potential field methods, and avoids local optima through rerouting activities. However, the patent does not consider the performance constraints of the UAVs themselves when constructing the potential field, resulting in routes that are unreachable in actual high-dynamic scenarios. The patent "Route Planning Method for UAV Formation Maintenance" (patent number: CN202210194008.0) discloses a route planning method for UAV formation maintenance. By establishing a formation model for the formation turning process and solving a nonlinear programming problem, a route that satisfies the dynamic constraints of the wingman is obtained. The patent only considers the formation turning constraint solution problem of the wingman route. The lead aircraft route also needs to meet the formation turning requirements in order to achieve the overall formation effect. Summary of the Invention

[0005] The purpose of this invention is to solve the problems mentioned in the background art and provide a method for autonomously generating flight paths for high-speed unmanned aerial vehicles (UAVs) in a leader-colleague cooperative manner.

[0006] To achieve the objective of this invention, a method for autonomously generating flight paths for high-speed unmanned aerial vehicles (UAVs) in a leader-flyer cooperative manner is disclosed, comprising the following steps:

[0007] Step 1: Determine the pre-planned flight path of the high-speed UAV lead aircraft and the lead-wingman cooperative relationship based on the flight mission;

[0008] Step 2: Based on the UAV's turning radius constraint, perform non-essential point constraint spatial geometric polyline planning on the lead aircraft's pre-planned route to generate the lead aircraft's polyline route;

[0009] Step 3: Based on the UAV climb rate constraint, perform constrained space adaptive altitude heave planning on the lead aircraft's zigzag route to generate the lead aircraft's planned route; for non-essential points, the heave constraint altitude is used as the waypoint altitude; for essential points, the waypoint altitude is reached by generating an adaptive altitude heave three-dimensional spiral gradient segment.

[0010] Step 4: Based on the relative positional relationship between the lead aircraft and the wingman and the requirements of the cooperative flight segment, perform segmented adaptive cooperative matching planning for the lead aircraft's planned route to generate the lead aircraft-wingman cooperative flight route.

[0011] Furthermore, step 1 specifically includes:

[0012] Based on the flight mission, set a pre-planned route.

[0013] {P Pre(i) (x Pre(i) ,y Pre(i) ,z Pre(i) ,v Pre(i) ,λ Pre(i) ,β Pre(i) )|i∈(1,n)}

[0014] Where (x) Pre(i) ,y Pre(i) ,z Pre(i) ) is waypoint P Pre(i) The three-dimensional spatial coordinate position; v Pre(i) waypoint P Pre(i) velocity; λ Pre(i) Marking a necessary point; β Pre(i) Identify the lead-wingman cooperative flight route; delineate flight areas, mission segments, and cooperative segments through pre-planned routes.

[0015] Furthermore, step 2 specifically involves:

[0016] For waypoints P that are not required to be visited in the pre-planned route Pre(i) {λ Pre(i) =0}, if the turning radius constraint of the UAV is not satisfied, that is, the UAV at waypoint P Pre(i) When turning, P Pre(i) The angle δ(P) between the preceding and following flight segments Pre(i) It cannot meet the required turning angle.

[0017]

[0018] In formula (1), φ max R is the maximum roll angle of the high-speed drone; g is the acceleration due to gravity; R(P) Pre(i) ) is the waypoint P Pre(i) Turning radius; L tmax The maximum distance for a high-speed drone to turn in advance. (P) Pre(i) Centered on each of the following segments P Pre(i-1) ~P Pre(i) and P Pre(i) ~P Pre(i+1) Forward and backward with R(P) Pre(i) Take the point of the broken line with radius )

[0019] P Turn(i) (x Turn(i) ,y Turn(i) ,z Turn(i) ,v Turn(i) ,λ Turn(i) ,β Turn(i) )

[0020] Then, based on geometric relationships, we can obtain...

[0021]

[0022]

[0023]

[0024]

[0025] In equations (2) to (5) above, ψ(P) Pre(i-1) ,P Pre(i) ) and ψ(P Pre(i) ,P Pre(i+1) ) represent the forward and backward flight directions of the waypoints, respectively; D(·) is the mapping distance of the line connecting the two waypoints in the two-dimensional plane. Based on the above formula, the polyline point information can be obtained. The pre-planned route waypoints are planned sequentially through step 2 to obtain the polyline planned route.

[0026] {P Turn(i) (x Turn(i) ,y Turn(i) ,z Turn(i) ,v Turn(i) ,λ Turn(i) ,β Turn(i) )|i∈(1,m)}.

[0027] Furthermore, step 3 specifically involves:

[0028] Design constrained space adaptive altitude heave planning to generate the lead aircraft's planned flight path:

[0029] {P C(i) (x C(i) ,y C(i) ,z C(i) ,v C(i) ,λ C(i) ,β C(i) )|i∈(1,k)}

[0030] For waypoints P that are not mandatory Turn(i ){λ Turn(i) =0}, using the heave constraint height as the waypoint setting height,

[0031]

[0032] In equation (6), C represents the climb rate constraint for the UAV flight segment; ΔH represents the heave constraint elevation difference for the flight segment. If the heave constraint elevation difference ΔH is lower than the existing elevation difference for the flight segment, then ΔH is used as the set elevation difference to adjust the set elevation of the waypoints, thereby achieving elevation heave planning for non-essential waypoints. Thus, the cumulative non-reachable elevation to the essential point P... C(i) { C(i) =1}, for flight segment P C(i-1) ~P C(i) Design an adaptive three-dimensional spiral gradient programming method to generate adaptive three-dimensional spiral gradient flight segments.

[0033] {P Cs(j) (x Cs(j) ,y Cs(j) ,z Cs(j) ,v Cs(j) ,λ Cs(j) ,β Cs(j) )|j∈(1,g)}.

[0034] Furthermore, the adaptive height-sag 3D spiral gradient planning process is as follows:

[0035] For segment P C(i-1) ~P C(i) Design an adaptive elevation change 3D spiral gradient program to generate adaptive elevation change 3D spiral gradient flight segments.

[0036]

[0037] In equation (7), linespace(v C(i-1) ,v C(i) ,g) is an arithmetic function, thus ensuring that the height of the necessary points can be reached.

[0038] Furthermore, the specific planning steps for the adaptive height-sag 3D spiral gradient are as follows:

[0039] Step 3-1: Perform linear climb with a constrained climb rate (8), where H L The remaining elevation gain is (H). L =0), terminate the adaptive height rise and fall 3D spiral gradient planning;

[0040]

[0041] Step 3-2: Assess the remaining climbing height H L The adaptive height rise and fall three-dimensional spiral gradient is gradually achieved. After a fixed-length turn and climb (9)-(10), the UAV gradually turns and forms a semi-circular spiral gradient route. If the height of the turn and climb process is reached, the UAV will perform a spiral turn in the same height plane.

[0042]

[0043]

[0044] Step 3-3: After steps 3-1 and 3-2, the UAV's heading is now aligned with ψ(P). C(i-1) ,P C(i) Conversely, climb in a straight line in the opposite direction with a constrained climb rate (11)-(12). If the height during the climb is achievable, then the target height is taken as the waypoint height.

[0045]

[0046]

[0047] Steps 3-4: Implement a semi-circular gradient flight path by following the adaptive height rise and fall three-dimensional spiral gradient method (13)-(14). If the height of the turning and climbing process is reached, then perform a spiral turn in the same height plane.

[0048]

[0049]

[0050] Furthermore, step 4 specifically involves:

[0051] Through steps 1 to 3, the lead aircraft's planned flight path is obtained.

[0052] {P C(i) (x C(i) ,y C(i) ,z C(i) ,v C(i) ,λ C(i) ,β C(i) )|i∈(1,k)}

[0053] The wingman's planned route is defined by segmented adaptive collaborative matching planning.

[0054] {P W(i) (x W(i) ,y W(i) ,z W(i) ,v W(i) |i∈(1,k)}

[0055] If the lead aircraft's current segment is a non-cooperative segment, and the preceding segment has no cooperative segment, but the subsequent segment has a cooperative segment, i.e., P C(i) {β C(t) =0|t∈[1,i],β C(s) =1|s∈[i+1,k]}, then the lead aircraft segment is offset according to the normal of the heading of the subsequent mission segment to obtain the wingman segment;

[0056] If the lead aircraft's current segment is a non-cooperative segment, and the preceding segment has a cooperative segment, but the subsequent segment does not have a cooperative segment, i.e., P C(i) {β C(t) =1|t∈[1,i-1],= C(s) =0|s∈[i,k]}, then the lead aircraft segment is offset according to the normal of the aforementioned mission segment heading to obtain the wingman segment;

[0057] If the lead aircraft's current segment is a cooperative segment, i.e., P C(i) {β C(i) If =1}, then the lead aircraft segment will be offset according to the normal direction of the current segment heading to obtain the wingman segment.

[0058] Compared with the prior art, the significant advancements of this invention are: 1) By implementing non-essential point constraint spatial geometric polygonal planning, constraint spatial adaptive altitude rise and fall planning, and segmented adaptive cooperative matching planning for pre-planned routes, flight routes that meet the performance constraints of high-speed UAVs and the cooperative relationship between lead and wingmen are autonomously generated, which is highly practical in engineering and easy to use; 2) No spatial modeling is required during route planning, and the performance constraints of high-speed UAVs are considered, thus achieving the reachability of the planned routes; 3) By first establishing a lead aircraft route that meets the dynamic constraints of UAVs, and then using segmented adaptive cooperative matching planning, the cooperative flight of lead and wingmen during flight is guaranteed.

[0059] To more clearly illustrate the functional characteristics and structural parameters of the present invention, further explanation is provided below in conjunction with the accompanying drawings and specific embodiments. Attached Figure Description

[0060] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0061] Figure 1 A flowchart of a method for autonomously generating a high-speed unmanned aerial vehicle (UAV) leader-wingman cooperative flight path is provided as an embodiment of the present invention;

[0062] Figure 2 A schematic diagram of non-essential point constraint spatial geometric polyline planning provided for embodiments of the present invention;

[0063] Figure 3 A schematic diagram of adaptive height rise and fall planning for non-essential point constraint space provided for embodiments of the present invention;

[0064] Figure 4 A schematic diagram of a three-dimensional spiral gradient flight segment planning method for adaptive elevation gain / loss at essential points, provided for embodiments of the present invention;

[0065] Figure 5 A two-dimensional schematic diagram of wingman segmented adaptive collaborative matching planning provided for an embodiment of the present invention. Detailed Implementation

[0066] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0067] Example

[0068] like Figure 1 As shown in the figure, an embodiment of the present invention provides a method for autonomously generating a high-speed unmanned aerial vehicle (UAV) lead-wingman cooperative flight path, which includes the following steps:

[0069] Step 1: Determine the pre-planned flight path of the high-speed UAV lead aircraft and the lead-wingman cooperative relationship based on the flight mission;

[0070] Step 2: Based on the UAV's turning radius constraint, perform non-essential point constraint spatial geometric polyline planning on the lead aircraft's pre-planned route to generate the lead aircraft's polyline route;

[0071] Step 3: Based on the UAV's climb rate constraint, perform constrained spatial adaptive altitude heave planning on the lead aircraft's polygonal route to generate the planned route for the lead aircraft. For non-essential points, the heave constraint altitude is used as the waypoint altitude; for essential points, the waypoint altitude is reached by generating an adaptive altitude heave 3D spiral gradient segment.

[0072] Step 4: Based on the relative positional relationship between the lead aircraft and the wingman and the requirements of the cooperative flight segment, perform segmented adaptive cooperative matching planning for the lead aircraft's planned route to generate the lead aircraft-wingman cooperative flight route.

[0073] The steps are further described below with reference to the accompanying drawings.

[0074] Step 1: Determine the pre-planned flight path of the high-speed UAV lead aircraft and the lead-wingman coordination relationship based on the flight mission, specifically:

[0075] Based on the flight mission, set a pre-planned route.

[0076] {P Pre(i) (x Pre(i) ,y Pre(i) ,z Pre(i) ,v Pre(i) ,λ Pre(i) ,β Pre(i ))|i∈(1,n)}

[0077] Where (x) Pre(i) ,y Pre(i) ,z Pre(i) ) is waypoint P Pre(i) The three-dimensional spatial coordinate position; v Pre(i) waypoint P Pre(i) velocity; λ Pre(i) Marking a necessary point; β Pre(i) This identifies the routes for lead-wingman cooperation. By pre-planning routes, flight areas, mission segments, and cooperation segments are defined.

[0078] Step 2: Based on the UAV's turning radius constraint, perform non-essential point constraint spatial geometric polyline planning on the lead aircraft's pre-planned route to generate the lead aircraft's polyline route, specifically:

[0079] like Figure 2 As shown, for waypoints P that are not required to be visited in the pre-planned route... Pre(i) {λ Pre(i) =0}, if the turning radius constraint of the UAV is not satisfied, that is, the UAV at waypoint P Pre(i) When turning, P Pre(i) The angle δ(P) between the preceding and following flight segments Pre(i) It cannot meet the required turning angle.

[0080]

[0081] In formula (1), φ max R is the maximum roll angle of the high-speed drone; g is the acceleration due to gravity; R(P) Pre(i) ) is the waypoint P Pre(i) Turning radius; L tmax The maximum distance for a high-speed drone to turn in advance. (P) Pre(i) Centered on each of the following segments P Pre(i-1) ~P Pre(i) and P Pre(i) ~P Pre(i+1) Forward and backward with R(P) Pre(i) Take the point P of the broken line with radius ) Turn(i) (x Turn(i) ,y Turn(i) ,z Turn(i) ,v Turn(i) ,λ Turn(i) ,β Turn(i) Then, based on geometric relationships, we can obtain...

[0082]

[0083]

[0084]

[0085]

[0086] In equations (2) to (5) above, ψ(P) Pre(i-1) ,P Pre(i) ) and ψ(P Pre(i) ,P Pre(i+1) ) represent the forward and backward flight directions of the waypoints, respectively; D(·) is the mapping distance of the line connecting the two waypoints in the two-dimensional plane. Based on the above formula, the polyline point information can be obtained. The pre-planned route waypoints are planned sequentially through step 2 to obtain the polyline planned route {P}. Turn(i) (x Turn(i) ,y Turn(i) ,z Turn(i) ,v Turn(i) ,λ Turn(i) ,β Turn(i) )|i∈(1,m)}.

[0087] Step 3: Based on the UAV's climb rate constraint, perform constrained spatial adaptive altitude heave planning on the lead aircraft's polygonal route to generate the planned route for the lead aircraft. For non-essential points, the heave constraint altitude is used as the waypoint altitude; for essential points, the waypoint altitude is reached by generating an adaptive altitude heave 3D spiral gradient segment, specifically as follows:

[0088] Although the route can satisfy the turning radius constraint of the high-speed UAV through the non-essential point constraint spatial geometric polygonal planning in step 2, it still cannot satisfy the climb rate constraint of the UAV. Therefore, an adaptive altitude heave planning in the constraint space is designed to generate the planned route for the lead aircraft:

[0089] {P C(i) (x C(i) ,y C(i) ,z C(i) ,v C(i) ,λ C(i) ,β C(i) )|i∈(1,k)}

[0090] like Figure 3 As shown, for waypoint P which is not a mandatory route... Turn(i) {λ Turn(i) =0}, using the heave constraint height as the waypoint setting height,

[0091]

[0092] In equation (6), C represents the climb rate constraint for the UAV flight segment; ΔH represents the heave constraint elevation difference for the flight segment. If the heave constraint elevation difference ΔH is lower than the existing elevation difference for the flight segment, then ΔH is used as the set elevation difference to adjust the set elevation of the waypoints, thereby achieving elevation heave planning for non-essential waypoints. Thus, the cumulative non-reachable elevation to the essential point P... C(i) {λC(i) =1}, such as Figure 4 As shown, for flight segment P C(i-1) ~P C(i) Design an adaptive three-dimensional spiral gradient programming method to generate adaptive three-dimensional spiral gradient flight segments.

[0093]

[0094] In equation (7), linespace(v C(i-1) ,v C(i) ,g) is an arithmetic progression function. Thus, the height of the necessary points can be reached. A brief introduction to the specific planning steps for a complete cycle of spiral gradient ascent is provided.

[0095] ① First, perform a linear climb with a constrained climb rate (8), where H L The remaining elevation gain is (H). L =0), terminate the adaptive spiral gradient climbing planning;

[0096]

[0097] ②Secondly, regarding the remaining climbing height H L The UAV gradually turns by climbing in a spiral gradient manner, and after a fixed-length turn climb (9)-(10), a semi-circular spiral gradient flight path is formed. If the height of the turn climb process is reached, a spiral turn is performed in the same height plane.

[0098]

[0099]

[0100] ③Then, after ① and ②, the UAV's heading is now aligned with ψ(P) C(i-1) ,P C(i) Conversely, climb in a straight line in the opposite direction with a constrained climb rate (11)-(12). If the height during the climb is achievable, then the target height is taken as the waypoint height.

[0101]

[0102]

[0103] ④ Finally, following the spiral gradient ascent method (13)-(14), a semi-circular spiral gradient route is achieved. If the height of the turning and ascent process is reached, a spiral turn is performed in the same height plane.

[0104]

[0105]

[0106] Step 4: Based on the relative positional relationship between the lead and wingman aircraft and the requirements of the cooperative flight segment, perform segmented adaptive cooperative matching planning for the lead aircraft's planned route to generate the lead-wingman cooperative flight route:

[0107] Through steps 1-3, the lead aircraft's planned route is obtained.

[0108] {P C(i) (x C(i) ,y C(i) ,z C(i) ,v C(i) ,λ C(i) ,β C(i) )|i∈(1,k)}

[0109] like Figure 5 As shown, the wingman's planned route is defined through adaptive heading offset planning.

[0110] {P W(i) (x W(i) ,y W(i) ,z W(i) ,v W(i) )|i∈(1,k)}

[0111] ① If the lead aircraft's current segment is a non-cooperative segment, and the preceding segment has no cooperative segment, but the subsequent segment has a cooperative segment, i.e., P C(i) {β C(t) =0|t∈[1,i],β C(s) =1|s∈[i+1,k]}, then the lead aircraft segment is offset according to the normal of the heading of the subsequent mission segment to obtain the wingman segment;

[0112] ② If the lead aircraft's current segment is a non-cooperative segment, and the preceding segment has a cooperative segment, but the subsequent segment does not have a cooperative segment, i.e., P C(i) {β C(t) =1|t∈[1,i-1],β C(s) =0|s∈[i,k]}, then the lead aircraft segment is offset according to the normal of the aforementioned mission segment heading to obtain the wingman segment;

[0113] If the lead aircraft's current flight segment is a cooperative flight segment, i.e., P C(i) {β C(i) If =1}, then the lead aircraft segment will be offset according to the normal direction of the current segment heading to obtain the wingman segment.

[0114] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0115] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for autonomously generating flight paths for high-speed unmanned aerial vehicles (UAVs) in a leader-court syndicate cooperative flight pattern, characterized in that: Includes the following steps: Step 1: Determine the pre-planned flight path of the high-speed UAV lead aircraft and the lead-wingman cooperative relationship based on the flight mission; Step 2: Based on the UAV's turning radius constraint, perform non-essential point constraint spatial geometric polyline planning on the lead aircraft's pre-planned route to generate the lead aircraft's polyline route; Step 3: Based on the UAV climb rate constraint, perform constrained space adaptive altitude heave planning on the lead aircraft's zigzag route to generate the lead aircraft's planned route; for non-essential points, the heave constraint altitude is used as the waypoint altitude; for essential points, the waypoint altitude is reached by generating an adaptive altitude heave three-dimensional spiral gradient segment. Step 4: Based on the relative positional relationship between the lead aircraft and the wingman and the requirements of the cooperative flight segment, perform segmented adaptive cooperative matching planning for the lead aircraft's planned route to generate the lead aircraft-wingman cooperative flight route; Step 2 is as follows: For waypoints that are not required to be visited in the pre-planned route If the turning radius constraint of the drone is not met, that is, if the drone is at the waypoint When turning, Angle between the preceding and following flight segments Unable to meet the required turning angle (1) In equation (1), This represents the maximum roll angle for a high-speed drone. It is the acceleration due to gravity; For waypoints Turning radius; The maximum distance for a high-speed drone to turn in advance; Centered on each of the following sections as well as Forward, backward Take the point of the broken line with the radius. The pre-planned route and waypoints are planned sequentially in step 2 to obtain the polyline planned route. 。 2. The method for autonomously generating flight paths for high-speed unmanned aerial vehicles (UAVs) in accordance with claim 1, characterized in that, Step 1 is as follows: Based on the flight mission, set a pre-planned route. in waypoints The three-dimensional spatial coordinate position; waypoints speed; Marking a necessary point; For leader-wingman cooperative flight path identification; By pre-planning flight routes, flight areas, mission segments, and collaborative segments are delineated.

3. The method for autonomously generating flight paths for high-speed unmanned aerial vehicles (UAVs) in accordance with claim 1, characterized in that, Step 3 specifically involves: Design constrained space adaptive altitude heave planning to generate the lead aircraft's planned flight path: For waypoints that are not required The heave constraint height is used as the waypoint setting height. (2) In equation (2), The climb rate constraint for the drone's flight segment altitude; The heave-restrained elevation difference for the flight segment; if the heave-restrained elevation difference If the elevation difference is lower than the existing elevation difference of the flight segment, then... To establish elevation differences, the altitude settings for waypoints are adjusted to allow for elevation and descent planning for non-essential waypoints; thereby accumulating the unreachable altitude to the essential waypoints. For the flight segment Design an adaptive three-dimensional spiral gradient programming method to generate adaptive three-dimensional spiral gradient flight segments. 。 4. The method for autonomously generating flight paths for high-speed unmanned aerial vehicles (UAVs) in accordance with claim 3, characterized in that, The adaptive height-sag 3D spiral gradient planning process is as follows: For the segment Design an adaptive elevation change 3D spiral gradient program to generate adaptive elevation change 3D spiral gradient flight segments. (3) In equation (3), It is an arithmetic function, thus ensuring that the height of the necessary points is reachable.

5. The method for autonomously generating flight paths for high-speed unmanned aerial vehicles (UAVs) in accordance with claim 4, characterized in that, The specific planning steps for an adaptive height-sag 3D spiral gradient are as follows: Step 3-1: Perform a straight climb using a constrained climb rate. The remaining elevation gain is [value]. If the elevation gain is [value], then [the elevation gain is] [value]. ), thus ending the adaptive height rise and fall 3D spiral gradient planning; Step 3-2: Assess the remaining climbing height The process is achieved step by step using an adaptive three-dimensional spiral gradient, with a fixed-length turn and a fixed-angle climb to complete the UAV's gradual turning and form a semi-circular spiral gradient flight path. If the height during the turn and climb process is reached, the UAV will perform a spiral turn within the same altitude plane. Step 3-3: After steps 3-1 and 3-2, the UAV's heading is now opposite to that in step 3-1. Then, it climbs in a straight line in the opposite direction with a constrained climb rate. If the height reached during the climb is achievable, the target height is taken as the waypoint height. Steps 3-4: Using the adaptive height rise and fall three-dimensional spiral gradient method, realize the other half-circular spiral gradient route. If the height during the turning and climbing process is achievable, then perform a spiral turn in the same height plane.

6. The method for autonomously generating flight paths for high-speed unmanned aerial vehicles (UAVs) in accordance with claim 1, characterized in that, Step 4 is as follows: Through steps 1 to 3, the lead aircraft's planned flight path is obtained. The wingman's planned route is defined by segmented adaptive collaborative matching planning. If the lead aircraft's current segment is a non-cooperative segment, and the preceding segment has no cooperative segment, but the subsequent segment has a cooperative segment, i.e. The lead aircraft segment is offset according to the normal of the heading of the subsequent mission segment to obtain the wingman segment; If the lead aircraft's current segment is a non-cooperative segment, and the preceding segment has a cooperative segment, but the subsequent segment does not have a cooperative segment, i.e. Then the lead aircraft segment is offset in position according to the normal direction of the aforementioned mission segment heading to obtain the wingman segment; If the lead aircraft's current flight segment is a cooperative flight segment, that is... Then the lead aircraft segment is offset according to the normal direction of the current segment heading to obtain the wingman segment.

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