Unmanned aerial vehicle cluster cooperative wind field exploration and autonomous energizing flight method

Abstract

The present application relates to the technical field of flight control, in particular to a kind of unmanned aerial vehicle cluster cooperative wind field exploration and autonomous enabling flight method, comprising: the flight task environment of constructing multi-source composite wind field;Determine the authenticity of potential wind source area, and select the target wind source area with the greatest enabling value in the real wind source area, select the corresponding enabling flight strategy based on the wind field type corresponding to the target wind source area to enable flight.The present application realizes intelligent, efficient response to dynamic wind field information, and accurately triggers the enabling flight strategy adapted to different wind fields to improve the energy efficiency and task success rate of long-duration mission.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of flight control technology, in particular to a method for cooperative wind field exploration and autonomous energy acquisition of a UAV cluster. BACKGROUND

[0002] Under the macro background of the rapid development of low-altitude economy, unmanned aerial vehicles (UAVs) as the main carrier of low-altitude flight tasks are increasingly applied in the fields of regional inspection, emergency communication, and environmental monitoring. These tasks have the common feature of placing high demands on the long-endurance operation capability of UAVs.

[0003] Currently, most small and medium-sized UAVs rely on on-board batteries as the main energy source, and their endurance is limited by the energy density of the batteries and the on-board carrying weight, making it difficult to meet the demand for continuous tasks lasting for several hours or even tens of hours. Therefore, how to obtain renewable energy from the flight environment in real time for the replenishment of flight energy has become a core technical approach to breaking through the endurance bottleneck of UAVs and achieving a leap in task capability.

[0004] Wind energy, as the most widely distributed and energy-rich natural resource in the atmospheric environment, provides an ideal energy source for UAVs to realize "flying while charging". The flight height of low-altitude UAVs is just in the area where wind energy resources are relatively abundant. Drawing on the biological principles of albatrosses, vultures, and other birds that use wind field energy for long-distance and long-time soaring, it is possible for UAVs to capture energy from the environmental wind field by performing specific flight maneuvers and strategies. Wind field energy mainly exists in two forms that can be utilized by UAVs: (1) vertical wind field energy (updraft): mainly composed of updrafts formed by uneven heating of the ground or terrain uplift airflows generated when air flows through obstacles. Birds or gliders can efficiently convert the vertical kinetic energy of updrafts into their own gravitational potential energy (height) by continuously circling (static gliding) in the core area of updrafts, thereby obtaining energy net gain. (2) Horizontal wind field energy (gradient wind field): refers to the gradient wind field formed by the change of horizontal wind speed with height. This phenomenon widely exists in the near-surface, sea surface, and high-altitude inversion layer due to factors such as surface friction and atmospheric heat conduction. The flight maneuver of albatrosses and other birds using wind gradient for energy acquisition is called "dynamic gliding". The basic principle is that in a wind field where wind speed increases with height, the wind's kinetic energy is converted into the bird's kinetic and potential energy by climbing against the wind, and then the bird further acquires energy by descending with the wind, forming a closed-loop energy flight cycle, thereby maintaining height and traveling long distances with little or no consumption of its own energy.

[0005] In the field of static gliding, the prior art mainly solves the problem of how to plan the optimal circling climbing trajectory of the unmanned aerial vehicle under the known rising air flow model of the flight environment. The technical core is to establish the gliding performance model of the unmanned aerial vehicle in the circling state, combine it with the rising air flow model, and solve the optimal circling radius, speed and roll angle that can maximize the net power gain at any height, and finally form a complete three-dimensional spiral climbing trajectory that dynamically changes with the height. In the field of dynamic gliding, the prior art focuses on planning a periodic gliding trajectory with maximum energy gain for the unmanned aerial vehicle under the known gradient wind field model of the flight environment. This scheme usually divides a complete gliding period into four stages: upwind climbing, high-altitude turning, downwind descending, and low-altitude turning, and solves the control instruction sequence that can maximize the net energy gain in a single period through optimal control and other theoretical methods.

[0006] Most of the prior art focuses on the optimal flight energy gain strategy planning and design of a single unmanned aerial vehicle in an information complete, single type wind field environment. This framework cannot cope with real scene flight environments with wide area, dynamic and uncertain flight information. The main reasons are as follows: it is difficult for a single unmanned aerial vehicle to efficiently explore, discover and utilize multiple types (such as gradient wind, rising air flow) of randomly distributed wind field energy sources in a wide area; secondly, the unmanned aerial vehicle path planning method is heavily dependent on accurate environmental prior information input. When facing real application scenarios with incomplete flight information and possibly false information, the unmanned aerial vehicle path planning method of the prior art will cause serious flight energy waste and time delay due to the pursuit of false wind source targets; the existing unmanned aerial vehicle cluster coordination strategy often fails to effectively decouple functions and task division for the two completely different task requirements of "wind source exploration" and "flight energy gain", and it is difficult to balance the high energy consumption of "wide area rapid exploration" and the flight goal of "maximum collective energy gain", resulting in low coordination efficiency.

[0007] Therefore, it is necessary to provide a method for unmanned aerial vehicle cluster cooperative wind field exploration and autonomous energy gain flight to solve the above problems. SUMMARY

[0008] In view of the technical problem of how the unmanned aerial vehicle cluster efficiently discovers, identifies and cooperatively utilizes multiple different types of wind field energy under uncertain flight environment wind field conditions to complete the end-to-end long endurance flight, the present application provides a method for unmanned aerial vehicle cluster cooperative wind field exploration and autonomous energy gain flight. Through ingenious task division, asynchronous information sharing and event-driven dynamic decision-making, the present application directs the unmanned aerial vehicle cluster to execute diversified energy gain strategies including multi-segment dynamic gliding and static circling climbing, thereby realizing the global optimization of wind source exploration efficiency and wind field energy gain to solve the existing problems.

[0009] The unmanned aerial vehicle cluster cooperative wind field exploration and autonomous enabling flight method of the present application adopts the following technical scheme, comprising:

[0010] The flight task environment of the multi-source composite wind field is constructed.

[0011] The unmanned aerial vehicle cluster is divided into a reconnaissance unit and a decision execution unit, the reconnaissance unit is used to obtain the wind source parameters of the potential wind source area in each "reconnaissance stage" in the flight task environment, the dynamic wind field information library of the unmanned aerial vehicle cluster is updated according to the wind source parameters of the potential wind source area, and when the potential wind source area determined according to the wind source parameters is a real wind source area, the wind source is actively entered and the corresponding enabling flight strategy is selected from the preset enabling flight strategy library, wherein the wind source parameters include wind source authenticity, wind source type, wind source intensity and wind source position, and the wind source type includes gradient wind field and updraft field; the decision execution unit is used to obtain the wind source enabling value of each real wind source area according to the wind source parameters of each real wind source area in the dynamic wind field information library, the real wind source area corresponding to the highest wind source enabling value is taken as a target wind source area, the corresponding enabling flight strategy is selected from the preset enabling flight strategy library according to the wind field type of the target wind source area, and the unmanned aerial vehicle cluster is controlled to perform enabling flight according to the enabling flight strategy.

[0012] Further schemes of the present application are that the step of constructing the flight task environment of the multi-source composite wind field is:

[0013] The multi-source composite wind field includes a gradient wind field dominated by wind shear and an updraft field driven by thermal convection, wherein two layers of uncertainty parameters are introduced for the multi-source composite wind field, the first layer of uncertainty parameters are that before the start of the flight task, the unmanned aerial vehicle cluster obtains initial prior information about the potential wind source in the flight task environment; the second layer of uncertainty parameters are that the position, type and intensity of the real wind source in the potential wind source have random deviations from the initial prior information.

[0014] Further schemes of the present application are that the reconnaissance unit of the unmanned aerial vehicle cluster includes multiple reconnaissance aircrafts, the multiple reconnaissance aircrafts fly to the potential wind source area in each "reconnaissance stage" allocated in advance, the wind source parameters are obtained when the reconnaissance unit enters the potential wind source area, and the dynamic wind field information library of the unmanned aerial vehicle cluster is updated according to the wind source parameters of the potential wind source area; when the wind source authenticity in the wind source parameters, the wind source is actively entered and the corresponding enabling flight strategy is selected from the preset enabling flight strategy library.

[0015] The further scheme of the present application is that the decision execution unit of the UAV cluster comprises a lead wingman and a following wingman, the lead wingman is used for task allocation, path planning and energy flight strategy of the UAV cluster, the lead wingman is used for obtaining the wind source energy value of each real wind source area when the potential wind source area in the dynamic wind field information base is the real wind source area, taking the real wind source area corresponding to the highest wind source energy value as the target wind source area, selecting the corresponding energy flight strategy from the preset energy flight strategy base according to the wind field type of the target wind source area, and controlling the UAV cluster to perform energy flight according to the energy flight strategy.

[0016] The further scheme of the present application is that the step of taking the real wind source area corresponding to the highest wind source energy value as the target wind source area according to the wind source energy value of each real wind source area comprises:

[0017] The potential energy of the UAV using the updraft is taken as the energy value of the updraft, and the energy consumption difference of the aircraft in the normal flight environment and the energy system of the aircraft in the gradient wind field is taken as the energy value of the gradient wind field under the same horizontal flight distance.

[0018] If the updraft field and the gradient wind field exist simultaneously in the front flight task environment, the wind source area with the maximum energy value is taken as the target wind source area.

[0019] If at least two updraft fields exist simultaneously in the front flight task environment, the updraft field with the greater energy value is taken as the target wind source area.

[0020] If at least two gradient wind fields exist simultaneously in the front flight task environment, the gradient wind field with the greater energy value is taken as the target wind source area.

[0021] The further scheme of the present application is that the energy flight strategy in the preset energy flight strategy base comprises a multi-section dynamic gliding strategy corresponding to the gradient wind field and a static hovering and climbing strategy corresponding to the updraft field.

[0022] The further scheme of the present application is that the static hovering and climbing strategy comprises that the UAV cluster performs approach transition trajectories of hovering cut-in point approximation flight, spiral flight trajectories in the updraft field and take-off transition trajectories of gliding flight after flying away from the updraft field in sequence.

[0023] The further scheme of the present application is that the step of obtaining the approach transition trajectory and the take-off transition trajectory when the wind source type is the updraft field comprises:

[0024] Based on the optimal circling radius, the optimal roll angle and the optimal circling airspeed corresponding to the flight trajectory at different flight altitudes, the best entry point of the flight trajectory and the flight state parameters corresponding to the best entry point are obtained, and the flight state parameters include: the optimal circling radius, the optimal roll angle and the optimal circling airspeed at the best entry point;

[0025] Based on the best entry point and the corresponding flight state parameters, and using a decoupled three-dimensional path planning algorithm, a geometrically optimal approach transition trajectory that meets all terminal state constraints is generated;

[0026] A tangent disengagement strategy is adopted to plan a shortest trajectory planning adjustment trajectory that smoothly transitions the aircraft from the circling flight state to the final target point as the exit transition trajectory.

[0027] Further schemes of the application are as follows:

[0028] A tangent line is drawn from the path planning end point and the last circle circling trajectory of the aircraft in the updraft field, and the tangent point of the tangent line and the circle circling trajectory is taken as the circling exit point;

[0029] A concentric circle is drawn with the updraft field center as the center, and the sum of the radius of the last circle circling trajectory and the preset exit safety adjustment distance is taken as the radius, and the intersection of the connecting line between the path planning end point and the updraft field center and the concentric circle is taken as the target point of the exit stage; the flight state of the target point is set as the best gliding state calculated according to the maximum lift-drag ratio of the aircraft;

[0030] Based on the circling exit point and the target point of the exit stage, an exit transition trajectory of the exit stage is generated by using a three-dimensional path planning algorithm.

[0031] Further schemes of the application are as follows:

[0032] The step of obtaining the dynamic gliding flight trajectory is as follows:

[0033] The starting point pose of the multi-segment dynamic gliding flight trajectory of the aircraft in the gradient wind field is taken as the starting point target pose, and the ending point pose of the flight trajectory is taken as the ending point target pose;

[0034] In a horizontal two-dimensional plane, the start point pose of the aircraft and the start point target pose, the end point pose and the end point target pose are connected, and by comparing a plurality of "arc-line-arc" combinations, the shortest path is selected as the optimal horizontal path;

[0035] Taking the horizontal flight distance as a reference, the horizontal flight distance is the optimal horizontal path, and the optimal vertical change profile corresponding to the start point height and climbing angle to the target height and climbing angle and the end point height and climbing angle to the target height and climbing angle is planned out;

[0036] The optimal horizontal path and the optimal vertical change profile are fused to reconstruct a three-dimensional space kinematic trajectory meeting all start point and end point state constraints, and the three-dimensional space kinematic trajectory is the dynamic gliding flight trajectory.

[0037] A further scheme of the present application is that after the current energy acquisition flight strategy task is completely finished, and the front potential wind source area is a false wind source area, the UAV cluster switches back to the cruising waiting state, and the latest wind source information in the dynamic wind field information library at the next moment is re-evaluated to decide the energy acquisition flight in the next stage.

[0038] The present application has the following advantages:

[0039] The method of the present application can enable the UAV cluster to realize autonomous, dynamic and intelligent task planning and execution in a complex multi-source wind field environment with significant uncertainty. Through the task division of "sacrificing local energy efficiency of part of individuals (scout aircraft) to obtain global information advantage of the cluster, so as to maximize the energy benefit of the main body (wingman formation)", the UAV cluster not only successfully avoids the interference of invalid wind field information, but more importantly, can continuously guide itself to the optimal wind source under the global information, thereby verifying the effectiveness of the method and strategy of the present application in improving the energy efficiency and task success rate of long endurance tasks; secondly, the decision execution unit designs a limited state machine mechanism of "cruising waiting" and "energy acquisition execution". The state conversion is not based on a preset script, but is asynchronously triggered by the wind field parameters updated by the scout unit in the shared dynamic wind field information library, so as to realize intelligent and efficient response to dynamic wind field information, and accurately trigger the energy acquisition flight strategy adapted to different wind fields. BRIEF DESCRIPTION OF DRAWINGS

[0040] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0041] Figure 1A flow chart of a UAV cluster cooperative wind field exploration and autonomous enabling flight method of the present application;

[0042] Figure 2 A flight task environment schematic diagram of a multi-source composite wind field of a UAV cluster in an embodiment of the present application;

[0043] Figure 3 A schematic diagram of the first stage of the lead wingman performing enabling flight in an embodiment of the present application;

[0044] Figure 4 A schematic diagram of the second stage of the lead wingman performing enabling flight in an embodiment of the present application;

[0045] Figure 5 A cooperative task trajectory schematic diagram of the scout and lead wingman in an embodiment of the present application;

[0046] Figure 6 A take-off flight trajectory planning schematic diagram of an aircraft in an updraft field in an embodiment of the present application. DETAILED DESCRIPTION

[0047] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0048] An embodiment of a UAV cluster cooperative wind field exploration and autonomous enabling flight method of the present application, as shown in Figure 1 , includes:

[0049] S1, constructing a flight task environment of a multi-source composite wind field;

[0050] Exemplarily, in a specific embodiment, the present application first constructs a flight task environment of a composite wind field facing a real application scenario, containing heterogeneity, multi-source and uncertainty; wherein, in the wide-area flight space of the UAV, a plurality of different physical cause wind sources are randomly distributed, including a gradient wind field dominated by wind shear and an updraft field driven by thermal convection. More importantly, in order to embody the incompleteness of wind field information prediction in a real flight environment, the wind field environment established in this embodiment introduces two layers of uncertainty parameters: first, before the start of the flight task, the UAV cluster can only obtain initial prior information about the “potential wind source (PWZ)” in the flight area, and the second layer of uncertainty parameter is that the position, type and intensity of the real wind source in the potential wind source have random deviations from the initial prior information.

[0051] Exemplarily, in one specific embodiment, the flight task environment is modeled: the task requires the UAV cluster to fly from a total starting point to a total ending point, and in the process of flight, the entire task airspace is naturally divided into several non-intersecting "reconnaissance stages" by the geographical distribution of all potential wind source areas. Based on this, the flight task environment of the multi-source composite wind field as shown in Figure 2

[0052] S2, determine the authenticity of the potential wind source area, and select the target wind source area with the maximum energy gain value in the real wind source area, select the corresponding energy gain flight strategy based on the wind field type corresponding to the target wind source area to perform energy gain flight;

[0053] Specifically, in order to balance the wind source exploration efficiency and flight energy consumption during flight, the UAV cluster is dynamically divided into a reconnaissance unit and a decision execution unit in this embodiment. The reconnaissance unit is used to obtain the wind source parameters of the potential wind source area in each "reconnaissance stage" in the flight task environment, update the dynamic wind field information library of the UAV cluster according to the wind source parameters of the potential wind source area, and actively enter the wind source when the potential wind source area determined according to the wind source parameters is a real wind source area, and select the corresponding energy gain flight strategy from the preset energy gain flight strategy library. The wind source parameters include: wind source authenticity, wind source type, wind source intensity and wind source position, and the wind source type includes gradient wind field and updraft field; the decision execution unit is used to select the real wind source area corresponding to the highest wind source energy gain value as the target wind source area according to the wind source energy gain value of each real wind source area, select the corresponding energy gain flight strategy from the preset energy gain flight strategy library according to the wind field type of the target wind source area, and control the UAV cluster to perform energy gain flight according to the energy gain flight strategy.

[0054] Exemplarily, in one specific embodiment, the reconnaissance unit of the UAV cluster needs to fly at a high speed, and the core task of the reconnaissance unit is to act as a "sensor" for wind source detection of the UAV cluster. The reconnaissance unit includes multiple reconnaissance machines. According to the known potential wind source area in the flight task environment, the multiple reconnaissance machines fly towards the potential wind source area pre-assigned to each "reconnaissance stage", perform forward flight to quickly verify the authenticity of the potential wind source area, obtain the wind source parameters when the reconnaissance unit enters the potential wind source area, update the dynamic wind field information library of the UAV cluster according to the wind source parameters of the potential wind source area, that is, feed back the wind source authenticity, wind source type (updraft field and gradient wind field), wind source intensity and wind source position information detected to the UAV cluster to update the dynamic wind field information library of the UAV cluster; at the same time, the reconnaissance unit itself is also endowed with the "opportunistic energy gain" ability, and when it is confirmed that the potential wind source area is a real wind source, it can immediately actively enter the wind source area and execute the corresponding energy gain flight strategy to supplement its energy consumption. When the reconnaissance machine has no reconnaissance task to perform, the flight speed is switched to a smaller energy consumption speed for cruising. In this embodiment, the lead wingman cruises at a speed of 12 m / s.​

[0055] In one specific embodiment, the reconnaissance unit collects wind source parameters of the potential wind source area through its own sensors after reaching the corresponding potential wind source area, and updates the collected wind source parameters to a dynamic wind field information database accessible by all drones in real time, serving as the only and reliable basis for subsequent coordinated decision-making. The dynamic wind field information database is characterized by pre-set enabling flight strategies applicable to the entire process of the two types of core wind fields in nature (gradient wind field and updraft). When making a cluster decision, the appropriate enabling flight strategy can be accurately called from the dynamic wind field information database according to the wind source parameters returned by the reconnaissance drone: namely, a multi-section dynamic gliding strategy is executed in the gradient wind field, and a static hovering and climbing strategy is executed in the updraft.

[0056] For example, in one specific embodiment, the decision execution unit of the drone cluster includes a lead drone and a follow drone. The lead drone is the decision core and main body of the cluster, and the follow drone adopts a flight mode following the lead drone to jointly form the "execution main body" of the cluster. The lead drone is used for task allocation, path planning, and enabling flight strategy of the drone cluster. When the potential wind source area in the dynamic wind field information database is a real wind source area, the lead drone obtains the wind source enabling value of each real wind source area, takes the real wind source area corresponding to the highest wind source enabling value as the target wind source area, selects the corresponding enabling flight strategy from the pre-set enabling flight strategy library according to the type of the target wind source area, and controls the drone cluster to perform enabling flight according to the enabling flight strategy.

[0057] For example, in this embodiment, the default state of the lead drone is a cruising waiting state. In the cruising waiting state, the lead drone leads the cluster to perform economic cruising towards the task final destination, and continuously listens to the updates of the dynamic wind field information database. Its decision logic is as follows: when the dynamic wind field information database is updated to show that a potential wind source area is confirmed as a real target wind source area, the lead drone immediately performs enabling value evaluation according to the type and parameters of the target wind source area, and follows the "high value first" principle, i.e., when a target wind source area with the highest enabling value appears, the lead drone immediately triggers decision-making, interrupts the cruising waiting state, and performs enabling flight according to the corresponding wind field type of the target wind source area to select the corresponding enabling flight strategy from the pre-set enabling flight information library.

[0058] In one specific embodiment, the potential energy gained by the UAV from updrafts is considered the energy gain value of the updrafts. For the same horizontal flight distance, the difference between the energy consumption of the aircraft in a normal flight environment and the energy consumption of the energy system when the aircraft flies in a gradient wind field is considered the energy gain value of the gradient wind field. If both updrafts and gradient wind fields exist simultaneously in the forward flight mission environment, the wind source area with the highest energy gain value is designated as the target wind source area. If at least two updrafts exist simultaneously in the forward flight mission environment, the updraft with the highest energy gain value is designated as the target wind source area. If at least two gradient wind fields exist simultaneously in the forward flight mission environment, the gradient wind field with the highest energy gain value is designated as the target wind source area. The energy consumption evaluation model for the energy system when the aircraft flies in a gradient wind field is as follows:

[0059]

[0060]

[0061] In the formula, This indicates the energy consumption of the energy system when an aircraft flies in a gradient wind field; This indicates the output power of the energy system when the aircraft is flying in a gradient wind field; Indicates the output voltage of the energy system; Indicates the output current of the energy system; Indicates the input voltage of the electronic speed controller; This indicates the input current of the electronic speed controller; This indicates the input voltage of the motor; This indicates the input current of the motor; Indicates the internal resistance of the electronic speed controller; Indicates the correction factor; Indicates the initial time of integration; This indicates the time when integration ends, where the flight phase is the unpowered gliding flight phase. When the aircraft is flying in a normal flight environment, the energy consumption of the energy system is automatically acquired by the system.

[0062] In one specific embodiment, the pre-set energy-gaining flight strategy library includes: a multi-stage dynamic gliding strategy corresponding to a gradient wind field and a static hovering and climbing strategy corresponding to an updraft field.

[0063] Specifically, in the embodiment, the static spiral climb strategy includes: the UAV cluster sequentially performs a spiral approach flight trajectory for approaching a cut-in point, a spiral flight trajectory in the updraft field, and a spiral exit flight trajectory after flying away from the updraft field, wherein the spiral flight trajectory is obtained by: constructing an updraft model describing physical characteristics of the thermal updraft, obtaining a vertical updraft speed at a spiral radius at any flight height by using the updraft model; constructing a safety constraint condition based on a roll angle range and a stall speed, and constructing a flight performance model of an aircraft unpowered gliding performance considering a spiral maneuver based on the safety constraint condition; obtaining a potential energy power obtained by the aircraft from the updraft based on a gravity and the vertical updraft speed when the aircraft flies in the updraft; obtaining a resistance dissipation power dissipated by the aircraft itself to overcome resistance based on a spiral sinking rate when the aircraft unpowered spirals; obtaining a net energy gain power of the aircraft based on the potential energy power and the resistance dissipation power; taking the spiral radius corresponding to the maximum net energy gain power of the aircraft at different flight heights as the optimal spiral radius at different flight heights, and obtaining the optimal roll angle and the optimal spiral airspeed corresponding to the optimal spiral radius of the aircraft at different flight heights by using the flight performance model; obtaining the spiral climb trajectory based on the optimal spiral radius, the optimal roll angle and the optimal spiral airspeed corresponding to different flight heights, and taking the spiral climb trajectory as the flight trajectory in the updraft. Wherein, the steps of obtaining the approach transition trajectory and the exit transition trajectory when the wind source type is the updraft field are: obtaining the best cut-in point and the flight state parameters corresponding to the best cut-in point based on the optimal spiral radius, the optimal roll angle and the optimal spiral airspeed corresponding to the flight trajectory at different flight heights, the flight state parameters including: the optimal spiral radius, the optimal roll angle and the optimal spiral airspeed at the best cut-in point; generating an approach transition trajectory which is geometrically optimal and meets all terminal state constraints by using a decoupled three-dimensional path planning path algorithm based on the best cut-in point and the flight state parameters corresponding thereto; planning a shortest path planning adjustment trajectory for smoothly transitioning the aircraft from the spiral flight state to the final target point as the exit transition trajectory by using the tangent departure strategy. In the embodiment, as shown in Figure 6 , the steps of planning a shortest path planning adjustment trajectory for smoothly transitioning the aircraft from the spiral flight state to the final target point as the exit transition trajectory by using the tangent departure strategy in the embodiment are: drawing a tangent line from the path planning target point (i.e. Figure 6 ) at the path planning end point and the last circle spiral trajectory of the aircraft in the updraft, and taking the tangent point of the circle spiral trajectory as a spiral exit point (i.e. Figure 6The disc spiral exit point is taken as the starting point of the flight path planning, a concentric circle is drawn with the updraft center as the center, the radius of the concentric circle is the sum of the radius of the last disc spiral track and the preset take-off safety adjustment distance, a line is drawn connecting the flight path planning end point and the updraft center, and the intersection of the line and the concentric circle is taken as the target point of the take-off stage, the flight state of the target point is set to the optimal gliding state calculated according to the maximum lift-drag ratio of the aircraft, and a take-off transition track is generated based on the disc spiral exit point, the target point of the take-off stage and the three-dimensional flight path planning path algorithm.

[0064] Specifically, in the embodiment, the multi-segment dynamic gliding strategy includes a dynamic gliding flight track composed of an approach track, a multi-segment dynamic gliding flight track in a gradient wind field and a take-off track, the multi-segment dynamic gliding flight track in the gradient wind field adopts the multi-segment dynamic gliding flight track in the gradient wind field disclosed in the patent with the application number 2025116401081, and the step of obtaining the dynamic gliding flight track is as follows: the flight track starting point pose of the multi-segment dynamic gliding flight track of the aircraft in the gradient wind field is taken as the starting point target pose, and the flight track end point pose is taken as the end point target pose; in a horizontal two-dimensional plane, the starting point pose and the starting point target pose of the aircraft are connected, and the end point pose and the end point target pose are connected, a shortest path is selected as an optimal horizontal path by comparing a plurality of "arc-straight line-arc" combinations, the horizontal flight distance is taken as a reference, the horizontal flight distance is the optimal horizontal path, and the optimal vertical variation profile corresponding to the starting point height and climbing angle to the target height and climbing angle and the end point height and climbing angle to the target height and climbing angle is planned, the optimal horizontal path and the optimal vertical variation profile are fused, and a three-dimensional space kinematic track meeting all starting point and end point state constraints is reconstructed, and the three-dimensional space kinematic track is the dynamic gliding flight track.

[0065] For example, after the current energy acquisition flight strategy task is completely finished, and the front potential wind source area is a false wind source area, the UAV cluster switches back to the cruising waiting state, and the latest wind source information in the dynamic wind field information library at the next moment is reevaluated to decide the next stage of energy acquisition flight.

[0066] It should be noted that the task division, asynchronous exploration, information sharing and energy acquisition strategy execution of the UAV cluster are closely combined to form the energy acquisition flight strategy, so that the UAV cluster can avoid the risk brought by wind source uncertainty to the greatest extent, and can flexibly adopt the most efficient wind field energy acquisition mode according to the actual discovered wind field type, thereby realizing efficient and robust cooperative energy acquisition and long flight time in an unknown multi-source wind field environment.

[0067] The application will be further described below in combination with specific embodiments and drawings:

[0068] Step one, construct the flight task environment of multi-source wind field;

[0069] The flight task environment of UAV cluster is set as a space cuboid region, the length (Y direction) of the space cuboid region is 19000m, the width (X direction) is 2000m, and the height (Z direction) is 500m.

[0070] Six potential wind source zones (PWZs) are set in the flight region, and the six potential wind source zones are numbered as [1, 2, 3, 4, 5, 6]. The potential wind source includes gradient wind field and updraft. The types and initial prior information of the six potential wind source zones are shown in Table 1. In order to reflect the uncertainty of wind source information in real scene, it is set that there is one false wind source in the six potential wind source zones, and the real wind source parameters of the six wind sources also have random deviations from the initial prior information.

[0071] Table 1

[0072]

[0073] The UAV cluster is set to be composed of 10 UAVs, the cruise flight speed of the UAV is 12m / s, and the flight task region is given by the task starting point and task ending point parameters. Among the 10 UAVs, two UAVs are set as reconnaissance aircraft, and the task is to explore the wind source. The remaining eight UAVs are formed in the form of one lead wingman and seven following wingmen, and the task is to obtain wind energy flight according to the wind source parameter information fed back by the reconnaissance aircraft. The flight performance of the UAV is given according to the real small UAV as shown in Table 2.

[0074] Table 2

[0075]

[0076] Step two, determine the authenticity of the potential wind source zone, and select the target wind source zone with the maximum energy value in the real wind source zone, and select the corresponding energy flight strategy based on the wind field type corresponding to the target wind source zone to perform energy flight.

[0077] 1. The reconnaissance aircraft performs the wind source search task:

[0078] According to the initial prior information of the potential wind source given in Table 1, two reconnaissance aircrafts are responsible for the wind source parameter exploration and determination of three potential wind sources respectively.

[0079] The reconnaissance aircraft No. 1 is assigned the exploration task of the PWZ No. [1, 3, 6], and at the flight time t = 38.08s, it first enters the No. 1 potential wind source and confirms that the No. 1 gradient wind field actually exists, and the wind source information of the No. 1 potential wind source is transmitted to the lead wingman. The reconnaissance aircraft No. 1 starts to execute the multi-section dynamic gliding strategy to obtain the energy of the gradient wind field corresponding to the No. 1 potential wind source. After completing the flight out of the No. 1 potential wind field area, it continues to fly forward. At t = 492.67s, it enters the No. 3 potential wind field and identifies that the No. 3 potential wind field is a “false wind field”, and after the information is transmitted to the lead wingman, it directly flies to the last target, and finally, at t = 888.48s, it enters the No. 6 potential wind field area and verifies that the No. 6 potential wind field is a real wind field; in the No. 6 potential wind field area, it flies according to the predetermined flight strategy until t = 1345.58s to reach the end point. The total energy consumption of the No. 1 reconnaissance aircraft in the whole flight process is 56144.00J.

[0080] The reconnaissance aircraft No. 2 is responsible for exploring the PWZ No. [2, 4, 5], and at t = 54.94s, it enters the No. 2 potential wind field and confirms that the No. 2 updraft type wind field actually exists, and after transmitting the wind source information, the reconnaissance aircraft No. 2 immediately executes the static hovering climbing flight strategy, and after obtaining energy in the No. 2 potential wind field, it continues to execute the next wind source exploration, and at t = 1638.49s, it confirms that the No. 4 potential wind source is a gradient type wind field and actually exists, at this time, it adopts the multi-section dynamic gliding flight strategy to utilize the energy of the No. 4 wind field, and after obtaining energy, it directly flies to the task end point. Because its task span is large, its total task time is as high as 2643.07s. However, although the flight time is longer, the total energy consumption (27256.36J) is greatly reduced compared with the reconnaissance aircraft No. 1, and the average power is as low as 10.31W. This significant energy efficiency improvement is because it successfully utilizes the energy of the updraft type wind field discovered in the exploration process, obtains a large amount of gravitational potential energy through the static hovering climbing strategy, and after the subsequent exploration task, the cruising speed is switched to the economic cruising speed of the lead wingman, thereby greatly saving the power energy consumption required for subsequent long-distance cruising. This result clearly shows that the exploration and energy acquisition of the method of the present application are not separated, and each unmanned aerial vehicle individual can serve as an energy collection unit of the cluster while completing its main task, thereby maximizing the global energy utilization efficiency of the whole cluster.

[0081] 2. Dynamic decision trajectory of the lead wingman

[0082] The cluster of the lead wingman and the following wingman is designed to delay take-off for 30 seconds, so as to receive the wind source parameters returned by the No. 1 and No. 2 reconnaissance aircrafts, in order to further decide the cluster flight strategy.

[0083] First stage decision: as Figure 3As shown, although the gradient wind field of No. 1 wind source is first discovered, the lead aircraft follows the "high-value first" principle and continues cruising. Until t = 54.94 s, the higher-value No. 2 updraft wind field is confirmed to be real, at which time the lead aircraft interrupts cruising and plans a path to the No. 2 wind field to perform circling climb. Its trajectory also obviously changes at this moment.

[0084] Second-stage decision: As shown, during the flight using the No. 2 wind field, the No. 3 wind field has been confirmed to be "false". Therefore, after completing the energization of the No. 2 wind field, the lead aircraft enters the "cruising waiting" state until t = 1638.49 s, when the No. 4 wind field is confirmed to be real, triggering the decision again, causing the swarm to immediately plan a path to the No. 4 wind field. Figure 4 Third-stage decision: After completing the energization of the No. 4 wind field, the lead aircraft does not experience any "cruising waiting" and directly flies to the No. 6 wind field, because the No. 6 wind field has been discovered by the No. 1 scout aircraft in advance. Therefore, when the lead aircraft completes the No. 4 wind field task, it immediately evaluates the dynamic wind field information library and finds that the next stage of information is complete, thereby achieving seamless connection of cross-stage tasks, and finally forming the flight trajectory as shown in

[0085] Figure 5

[0086] 3. Task comprehensive evaluation:

[0087] As shown in Table 3, Table 3 is the three-dimensional flight trajectory and cooperative exploration and energization task event report of the UAV swarm in this task scenario.

[0088] Table 3

[0089]

[0090] From the results of Table 3 and Figure 5 It is shown that the entire task of the UAV swarm lasts 2722.38 seconds (about 45 minutes), through precise cooperative strategies, the swarm successfully utilizes all available real wind fields and avoids false information, and finally reaches the task endpoint.

[0091] ​​It is necessary to note that the comprehensive evaluation of the whole task not only focuses on the total energy consumption, but also deeply analyzes the energy cost and contribution of different roles in the cluster. First, the scout aircraft exchanges valuable confirmation information and decision-making time for the whole cluster at the cost of its own high energy consumption. The average power of the two scout aircrafts is as high as 20.91 W, far exceeding the flight wingman. Their high-speed forward exploration is a necessary energy investment, and the purpose is to reduce the huge risk faced by the whole cluster due to environmental uncertainty. In particular, the average power of the first scout aircraft is as high as 41.72 W, and it makes a key contribution to quickly exclude interference and lock the optimal wind source target in the early stage of the cluster. Second, the lead wingman is the biggest energy beneficiary of this cooperative strategy as the decision-making core and execution subject. Thanks to the accurate intelligence provided by the scout aircraft, the lead wingman can maximize the avoidance of invalid flight and blind waiting. Its trajectory is almost entirely composed of efficient energy acquisition maneuvers and economic cruise flight. Therefore, although its flight time is as long as 2722.38 s, its average power is only 9.61 W, and the total energy consumption is only 26161.19 J, showing a high wind field energy utilization efficiency. Finally, to evaluate the system total energy consumption of the whole 10-aircraft cluster, the consumption of the 7 follower wingmen needs to be counted. Since the follower wingmen completely reproduce the energy-saving trajectory of the lead wingman, their energy consumption is the same as that of the lead wingman. Therefore, the system total energy consumption of the whole cluster is calculated to be 292689.86 J (about 81.30 Wh).

[0092] The case simulation results show that the method can enable the UAV cluster to realize autonomous, dynamic, and intelligent task planning and execution in a complex multi-source wind field environment with significant uncertainty. Through the task division of "sacrificing local energy efficiency of part of individuals (scout aircrafts) to exchange global information advantage of the cluster, thereby maximizing the energy benefit of the subject (wingman formation)", the UAV cluster not only successfully avoids the interference of invalid wind field information, but more importantly, can continuously guide itself to the optimal wind source under global information, thereby verifying the effectiveness of the method strategy in improving the energy efficiency and task success rate of long-haul tasks.

[0093] The above only describes the preferred embodiments of the present application and should not be used to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims

1. A method for collaborative wind field exploration and autonomous powered flight by a swarm of unmanned aerial vehicles (UAVs), characterized in that, include: Constructing a flight mission environment with multi-source composite wind fields; The UAV swarm is divided into a reconnaissance unit and a decision-making and execution unit. The reconnaissance unit is used to acquire wind source parameters of potential wind source areas in each "reconnaissance phase" of the flight mission environment. It updates the dynamic wind field information database of the UAV swarm based on the wind source parameters of potential wind source areas. When the potential wind source area determined by the wind source parameters is a real wind source area, it actively enters the wind source and selects the corresponding energy-acquiring flight strategy from the pre-set energy-acquiring flight strategy database. The wind source parameters include: wind source authenticity, wind source type, wind source intensity, and wind source location. The wind source type includes gradient wind field and updraft field. The decision-making and execution unit is used to acquire the wind source energy value of each real wind source area based on the wind source parameters of each real wind source area in the dynamic wind field information database. It selects the real wind source area corresponding to the highest wind source energy value as the target wind source area. Based on the wind field type of the target wind source area, it selects the corresponding energy-acquiring flight strategy from the pre-set energy-acquiring flight strategy database and controls the UAV swarm to perform energy-acquiring flight according to the energy-acquiring flight strategy.

2. The method for collaborative wind field exploration and autonomous powered flight of unmanned aerial vehicle (UAV) swarms according to claim 1, characterized in that, The steps for constructing a flight mission environment with a multi-source composite wind field are as follows: The multi-source composite wind field includes a gradient wind field dominated by wind shear and an updraft field driven by thermal convection. Two layers of uncertainty parameters are introduced into the multi-source composite wind field. The first layer of uncertainty parameter is: before the flight mission begins, the UAV swarm obtains initial prior information about potential wind sources in the flight mission environment. The second layer of uncertainty parameter is: the location, type and intensity of the actual wind sources in the potential wind sources have random deviations from the initial prior information.

3. The method for collaborative wind field exploration and autonomous powered flight of unmanned aerial vehicle (UAV) swarms according to claim 1, characterized in that, The reconnaissance unit of the UAV swarm consists of multiple reconnaissance aircraft. These aircraft fly towards potential wind source areas within each pre-assigned "reconnaissance phase". When the reconnaissance unit enters the potential wind source area, it acquires wind source parameters and updates the dynamic wind field information database of the UAV swarm based on the wind source parameters of the potential wind source area. When the wind source in the wind source parameters is real, the UAV swarm actively enters the wind source and selects the corresponding energy-gaining flight strategy from the pre-set energy-gaining flight strategy database.

4. The method for collaborative wind field exploration and autonomous powered flight of unmanned aerial vehicle (UAV) swarms according to claim 1, characterized in that, The decision-making and execution units of the drone swarm include a lead wingman and a follower wingman. The lead wingman is used for task allocation, path planning, and energy acquisition flight strategy of the drone swarm. When the potential wind source area in the dynamic wind field information database is a real wind source area, the lead wingman is used to obtain the wind source energy acquisition value of each real wind source area, take the real wind source area corresponding to the highest wind source energy acquisition value as the target wind source area, select the corresponding energy acquisition flight strategy from the pre-set energy acquisition flight strategy library according to the wind field type of the target wind source area, and control the drone swarm to carry out energy acquisition flight according to the energy acquisition flight strategy.

5. The method for collaborative wind field exploration and autonomous powered flight of unmanned aerial vehicle (UAV) swarms according to claim 4, characterized in that, The steps for selecting the target wind source area based on the wind source energy gain value of each actual wind source area are as follows: The potential energy gained by the UAV from the updraft is taken as the energy gain value of the updraft; the difference between the energy consumption of the aircraft when flying in a normal flight environment and the energy consumption of the energy system when flying in a gradient wind field is taken as the energy gain value of the gradient wind field for the same horizontal flight distance. If both updraft and gradient wind fields exist in the flight mission environment ahead, the wind source area with the greatest energy gain value will be taken as the target wind source area. If there are at least two updraft fields in the flight mission environment ahead, the updraft field with greater energy gain value will be taken as the target wind source area. If at least two gradient wind fields exist simultaneously in the flight mission environment ahead, the gradient wind field with greater energy gain value will be taken as the target wind source area.

6. The method for collaborative wind field exploration and autonomous powered flight of unmanned aerial vehicle (UAV) swarms according to claim 1, characterized in that, The pre-set energy-gaining flight strategy library includes: a multi-stage dynamic gliding strategy corresponding to a gradient wind field and a static hovering and climbing strategy corresponding to an updraft field.

7. The method for collaborative wind field exploration and autonomous powered flight of unmanned aerial vehicle (UAV) swarms according to claim 6, characterized in that, The static hovering and climbing strategy includes: the approach transition trajectory of the drone swarm approaching the entry point in sequence, the spiral flight trajectory in the updraft field, and the exit transition trajectory of gliding flight after leaving the updraft field. The steps for obtaining the entry and exit transition trajectories when the wind source type is an updraft field are as follows: Based on the optimal turning radius, optimal roll angle and optimal turning airspeed corresponding to the flight trajectory at different flight altitudes, the optimal entry point of the flight trajectory and the flight state parameters corresponding to the optimal entry point are obtained. The flight state parameters include: the optimal turning radius, optimal roll angle and optimal turning airspeed at the optimal entry point. Based on the optimal entry point and its corresponding flight state parameters, and using a decoupled 3D trajectory planning path algorithm, a geometrically optimal approach transition trajectory that satisfies all terminal state constraints is generated. A tangential departure strategy is adopted, and a shortest trajectory is planned to smoothly transition the aircraft from hovering to the final target point as the exit transition trajectory.

8. The method for collaborative wind field exploration and autonomous powered flight of unmanned aerial vehicle (UAV) swarms according to claim 7, characterized in that, The steps for planning and adjusting the trajectory to serve as the exit transition trajectory during the exit phase, using a tangential departure strategy, are as follows: Draw a tangent line from the end point of the flight path planning and the last loop trajectory of the aircraft in the updraft field. The point where this tangent line is tangent to the loop trajectory is the loop recovery point. With the center of the updraft field as the center, draw a concentric circle with the radius of the sum of the radius of the last circling trajectory and the preset exit safety adjustment distance as the radius. Connect the end point of the flight path planning and the center of the updraft field, and take the intersection of the line and the concentric circle as the target point of the exit stage. The flight state of the target point is set as the optimal gliding state calculated based on the maximum lift-to-drag ratio of the aircraft. Based on the circling and recovery point and the target point of the exit phase, and using a three-dimensional trajectory planning algorithm, an exit transition trajectory for the exit phase is generated.

9. A method for collaborative wind field exploration and autonomous powered flight of unmanned aerial vehicle (UAV) swarms according to claim 6, characterized in that, The multi-segment dynamic gliding strategy includes: the approach trajectory, the multi-segment dynamic gliding flight trajectory within the gradient wind field, and the exit trajectory, which together form the dynamic gliding flight trajectory. The steps for obtaining a dynamic gliding flight trajectory are as follows: The starting pose of the multi-segment dynamic gliding flight trajectory of the aircraft in the gradient wind field is taken as the starting target pose, and the ending pose of the flight trajectory is taken as the ending target pose. In a horizontal two-dimensional plane, the starting pose of the aircraft is connected to the starting target pose, and the ending pose is connected to the ending target pose. By comparing various combinations of "circular arc-straight line-circular arc", the path with the shortest total length is selected as the optimal horizontal path. Using the horizontal flight distance as a benchmark, which is the optimal horizontal path, we plan the optimal vertical change profiles from the starting point altitude and climb angle to the target altitude and climb angle, and from the ending point altitude and climb angle to the target altitude and climb angle. By fusing the optimal horizontal path and the optimal vertical change profile, a three-dimensional spatial kinematic trajectory that satisfies all starting and ending state constraints is reconstructed. This three-dimensional spatial kinematic trajectory is the dynamic gliding flight trajectory.

10. The method for collaborative wind field exploration and autonomous powered flight of unmanned aerial vehicle (UAV) swarms according to claim 1, characterized in that, Once the current empowered flight strategy mission is fully completed, and the potential wind source area ahead is a false wind source area, the drone swarm will switch back to the cruise waiting state and reassess the latest wind source information in the dynamic wind field information database to decide on the next phase of empowered flight.

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