Unmanned aerial vehicle formation control method, electronic equipment and storage medium

Abstract

The embodiment of the invention provides an unmanned aerial vehicle formation control method, electronic equipment and a storage medium, and belongs to the technical field of unmanned aerial vehicle formation. The method is applied to a target unmanned aerial vehicle in at least two unmanned aerial vehicles in an unmanned aerial vehicle formation, the unmanned aerial vehicle formation flies along a fixed direction, and the method comprises the steps of obtaining the position of each unmanned aerial vehicle, and determining the nearest unmanned aerial vehicle as a reference unmanned aerial vehicle; calculating a position difference between the target and the reference unmanned aerial vehicle to obtain a first uprush flow speed; calculating a partial derivative of the relative position based on the first uprush flow velocity to obtain a regression vector; constructing target upwash data by combining the historical upwash velocity, the historical relative position and the regression vector, and estimating the relative position; and performing correction prediction by using the position of the neighbor unmanned aerial vehicle, determining the expected position of the next time slot, and controlling the target unmanned aerial vehicle to fly. According to the embodiment of the invention, the total energy consumption during cooperative flight of multiple unmanned aerial vehicles can be reduced.

Description

Technical Field

[0001] The present application relates to the technical field of drone formations, and in particular to a control method, electronic equipment, and storage medium for a drone formation. Background Art

[0002] A low-altitude wireless network (LAWN) refers to a dedicated wireless communication network built on ground base stations, drones, and other low-altitude equipment to support communication and navigation for low-altitude aircraft. In a low-altitude wireless network scenario, the total energy consumption required for drones to perform their missions primarily consists of communication energy and mobility energy. Related technologies typically reduce the total energy consumption of drones by reducing communication energy, but this approach results in suboptimal energy efficiency.

[0003] Therefore, how to reduce the total energy consumption during the coordinated flight of multiple UAVs has become a technical problem that needs to be solved urgently. Summary of the Invention

[0004] The main purpose of the embodiments of the present application is to propose a control method, electronic device and storage medium for a drone formation, aiming to reduce the total energy consumption during the coordinated flight of multiple drones.

[0005] To achieve the above objectives, a first aspect of an embodiment of the present application provides a method for controlling a drone formation, which is applied to a target drone among at least two drones in a drone formation, wherein the drone formation flies in a fixed direction. The method comprises: Obtaining the actual position of each drone in the first time slot; According to the actual position of the UAV, determining the UAV closest to the target UAV in the fixed direction to obtain a reference UAV; Calculating an upwash velocity based on a position difference between the actual position of the target UAV and the actual position of each of the UAVs to obtain a first upwash velocity in the first time slot; performing partial derivative calculation on the relative position between the target UAV and the reference UAV according to the first upwash velocity to obtain a regression vector; Target upwash data is constructed based on the historical upwash velocity, the historical relative position in the historical time slot, the regression vector, and the first upwash velocity; wherein the historical time slot is the time slot before the first time slot, the historical relative position is the relative position between the target UAV and the reference UAV in the historical time slot, and the historical upwash velocity is the upwash velocity in all time slots before the first time slot; Calculating an estimated relative position based on the regression vector, the target upwash data, and the historical relative position; According to the actual positions of all the drones, two drones closest to the target drone are determined as neighboring drones, and the estimated relative positions of the neighboring drones are obtained as intermediate relative positions; Position prediction is performed based on the actual position of the reference UAV, the actual position of the target UAV, the estimated relative position and the intermediate relative position to obtain the expected position of the target UAV in the second time slot, and the target UAV is controlled to fly according to the expected position of the UAV, wherein the second time slot is the next time slot of the first time slot.

[0006] In some embodiments, constructing target upwash data based on the historical upwash velocity, the historical relative position in the historical time slot, the regression vector, and the first upwash velocity includes: The maximum value of the historical rushing velocity is taken as the basic rushing velocity; Multiplying the transposed matrix of the regression vector by the historical relative position to obtain a change value of the upwash velocity; The target upwash data is obtained by performing an integrated calculation based on the basic upwash velocity, the upwash velocity change value and the first upwash velocity.

[0007] In some embodiments, calculating the estimated relative position based on the regression vector, the target upwash data, and the historical relative position includes: performing a difference calculation between the target upwash data and the upwash velocity change value to obtain upwash velocity error data; Multiplying the regression vector, the upwash velocity error data, and a preset step value to obtain position correction data; Position superposition is performed based on the historical relative position and the position correction data to obtain the estimated relative position.

[0008] In some embodiments, performing position prediction based on the actual position of the reference drone, the actual position of the target drone, the estimated relative position, and the intermediate relative position to obtain the expected drone position of the target drone in the second time slot includes: Performing a weighted summation on the estimated relative position and the intermediate relative position according to preset predicted quantity weight data to obtain a target position change value; The actual position of the target UAV is updated according to the target position change value and the actual position of the reference UAV to obtain the expected position of the target UAV in the second time slot.

[0009] In some embodiments, updating the actual position of the target UAV according to the target position change value and the actual position of the reference UAV to obtain the expected UAV position of the target UAV in the second time slot includes: Obtaining the flight speed of the target UAV in the fixed direction; Determining a first position change amount in the fixed direction and a second position change amount in a horizontal direction based on the target position change value; wherein the horizontal direction is perpendicular to the fixed direction; Performing position calculation based on the first position change, the actual position of the target UAV, and the actual position of the reference UAV to obtain a first target coordinate in the fixed direction; Performing position calculation based on the second position change, the flight speed, the actual position of the target UAV, and the actual position of the reference UAV to obtain a second target coordinate in the horizontal direction; The desired position of the drone is determined according to the first target coordinates and the second target coordinates.

[0010] In some embodiments, determining the drone closest to the target drone in the fixed direction based on the actual position of the drone to obtain a reference drone includes: Calculating the flight distance between the target drone and any other drone based on the actual drone position of the target drone and the actual drone position of any other drone; Taking the minimum value of the flight distances as the target distance; The reference drone is determined from the drone formation based on the target distance.

[0011] In some embodiments, calculating the flight distance between the target drone and any other drone based on the actual drone position of the target drone and the actual drone position of any other drone includes: Determining a first position coordinate of the target drone in the fixed direction and a second position coordinate of the target drone in the horizontal direction based on the actual position of the target drone; wherein the horizontal direction is perpendicular to the fixed direction; Determining a third position coordinate of the drone in the fixed direction and a fourth position coordinate of the drone in the horizontal direction based on the actual position of any other drone; Calculating a first distance component for the first position coordinate and the third position coordinate, and calculating a second distance component for the preset distance weight, the second position coordinate, and the fourth position coordinate; The flight distance is obtained by summing the first distance component and the second distance component.

[0012] In some embodiments, calculating the upwash velocity based on the position difference between the actual position of the target UAV and the actual position of each of the UAVs to obtain a first upwash velocity in the first time slot includes: Calculating a difference between the actual position of the target UAV and the actual position of the reference UAV to obtain a first relative position difference; Calculating a difference between the actual position of each drone and the actual position of the reference drone to obtain a second relative position difference; Calculating an induced velocity based on the first relative position difference and each of the second relative position differences to obtain an intermediate upwash velocity; All the intermediate upwash flow velocities are summed up to obtain the first upwash flow velocity.

[0013] To achieve the above-mentioned purpose, the second aspect of an embodiment of the present application proposes an electronic device, which includes a memory and a processor, wherein the memory stores a computer program, and the processor implements the method described in the first aspect when executing the computer program.

[0014] To achieve the above-mentioned purpose, the third aspect of the embodiments of the present application proposes a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the method described in the first aspect.

[0015] The control method, electronic device, and storage medium for a drone formation proposed in this application obtain the actual position of each drone in the first time slot and, based on this position, determine the drone closest to the target drone in a fixed direction as a reference drone. The upwash velocity is then calculated using the position difference between the target drone and each drone. Based on this upwash velocity, the partial derivative of the relative position between the target drone and the reference drone is calculated to obtain a regression vector for estimation. Target upwash data is then constructed based on historical upwash velocities, historical relative positions, and the first upwash velocity, thereby dynamically updating the estimated relative position. On this basis, an intermediate relative position is constructed based on the positional relationship between the target drone and the two nearest drones. The actual position, estimated relative position, and intermediate relative position are then combined to perform position prediction. Ultimately, the target drone's expected position in the next time slot is obtained, and the target drone is controlled to fly. By introducing target upwash data construction based on upwash velocity, the target UAV can perceive and respond to the spatial distribution and aerodynamic interference characteristics of other UAVs in the formation. During the entire position prediction process, the target UAV not only makes flow field response judgments based on the upwash velocity generated by the reference UAV, but also introduces multi-point information fusion by introducing the relative estimated position with neighboring UAVs, thereby achieving more robust and directional coordinated adjustment. Through the method of the embodiment of the present application, the position changes, upwash induced velocity and historical trajectory of other UAVs have a real-time, dynamic and physically constrained impact on the target UAV, prompting the target UAV to gradually approach the optimal energy-saving area, thereby driving the formation as a whole to gradually converge to a flight form with a better aerodynamic structure, and ultimately effectively improving the overall energy-saving performance, attitude stability and coordinated flight efficiency of the formation. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a flow chart of a method for controlling a UAV formation provided by an embodiment of the present application; Figure 2 This is a schematic diagram of an application scenario of the control method for a UAV formation provided in an embodiment of the present application; Figure 3 yes Figure 1 Flowchart of step S102 in FIG. Figure 4 yes Figure 1 Flowchart of step S103 in FIG. Figure 5 yes Figure 1 Flowchart of step S105 in FIG. Figure 6 yes Figure 1 Flowchart of step S106 in FIG. Figure 7 yes Figure 1 Flowchart of step S108 in FIG. Figure 8 yes Figure 7 Flowchart of step S702 in FIG. Figure 9 Schematic diagram of the hardware structure of the electronic device provided in the embodiment of the present application; Figure 10 This is a schematic diagram of a simulation result provided by an embodiment of the present application; Figure 11 This is another simulation result diagram provided in an embodiment of the present application. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0018] The terms "first", "second" and the like in the specification, claims and drawings are used to distinguish similar objects and are not necessarily used to describe a particular order or sequence.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application pertains. The terms used herein are for the purpose of describing the embodiments of this application only and are not intended to limit this application.

[0020] First, let’s analyze some of the terms used in this application: Upwash, also known as upwash, is the flow of air above the tail of an aircraft (such as a fixed-wing drone) caused by wingtip vortices or overall aerodynamic disturbances. This flow typically occurs during tailing flight, particularly in formation flying or group flight control, and significantly affects the attitude stability, lift distribution, and energy management of the following aircraft.

[0021] Low-altitude wireless network (LAWN): A low-altitude wireless network (LAWN) is a wireless communication network structure deployed between the ground and traditional aviation altitudes. It aims to provide continuous, high-speed, and secure communication connections for low-altitude aircraft (such as drones), aerial platforms, or urban air mobility (UAM) systems. LAWN networks typically feature wide coverage, low latency, and strong interference resistance. They are suitable for a variety of scenarios such as low-altitude traffic management, environmental monitoring, emergency rescue, and logistics distribution, contributing to the construction of an integrated air-ground-space intelligent communication system.

[0022] Adapt-then-combine (ATC): Adapt-then-combine is a typical diffusion-based collaborative optimization strategy widely used in distributed learning and signal processing scenarios. In ATC, each node first updates its parameters based on local data (adaptation), then shares this information with neighboring nodes and performs a weighted average (combination). Compared to simultaneous combining and updating, this approach responds more quickly to local changes, enhancing algorithm stability and convergence speed in non-stationary environments. It is commonly used in sensor networks, adaptive filtering, and distributed estimation tasks.

[0023] Diffusion Least Mean Square (LMS): The Diffusion Least Mean Square (DLS) algorithm is an extension of the traditional LMS algorithm for multi-node distributed systems. It aims to achieve an optimal solution through information diffusion and collaboration among nodes. While performing local LMS updates at each node, the algorithm incorporates state information from neighboring nodes for joint adjustments, thereby improving the estimation accuracy and robustness of the entire network. The DLS algorithm is widely used in distributed signal processing, environmental perception, adaptive control, and other fields, and is a foundational algorithm in distributed adaptive systems.

[0024] Energy efficiency (EE) is a key performance indicator that measures a system's ability to effectively complete missions per unit of energy consumed. In wireless communications and unmanned systems, energy efficiency is typically expressed as the amount of data transmitted per unit of energy, or the effective flight distance or time per unit of energy consumed. High energy efficiency means that the system can minimize energy consumption while completing its intended functions, thereby extending device uptime and improving system stability.

[0025] A low-altitude wireless network (LAWN) refers to a dedicated wireless communication network built on ground base stations, drones, and other low-altitude equipment to support communication and navigation for low-altitude aircraft. In a low-altitude wireless network scenario, the total energy consumption required for drones to perform their missions primarily consists of communication energy and mobility energy. Related technologies typically reduce the total energy consumption of drones by reducing communication energy, but this approach results in suboptimal energy efficiency.

[0026] Therefore, how to reduce the total energy consumption during the coordinated flight of multiple UAVs has become a technical problem that needs to be solved urgently.

[0027] Based on this, the embodiments of the present application provide a control method, electronic device and storage medium for a drone formation, aiming to reduce the total energy consumption during the coordinated flight of multiple drones.

[0028] The control method, electronic device and storage medium of the drone formation provided in the embodiments of the present application are specifically illustrated through the following embodiments. First, the control method of the drone formation in the embodiments of the present application is described.

[0029] The control method for a drone formation provided in the embodiment of the present application relates to the technical field of drone formations. The control method for a drone formation provided in the embodiment of the present application can be applied to a terminal, can be applied to a server side, or can be software running in a terminal or a server side. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc.; the server side can be configured as an independent physical server, or as a server cluster or distributed system composed of multiple physical servers, or as a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application that implements the control method for a drone formation, etc., but is not limited to the above forms.

[0030] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and the like. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, and the like that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments in which tasks are performed by remote processing devices connected via a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media, including storage devices.

[0031] Figure 1 This is an optional flowchart of the control method for the drone formation provided in an embodiment of the present application. The control method for the drone formation provided in an embodiment of the present application is applied to a target drone among at least two drones in the drone formation, and the drone formation flies in a fixed direction. Figure 1 The method may include but is not limited to steps S101 to S108.

[0032] Step S101: Obtain the actual position of each UAV in the first time slot.

[0033] Step S102: According to the actual position of the UAV, determine the UAV closest to the target UAV in a fixed direction to obtain a reference UAV.

[0034] Step S103 , calculating the upwash velocity based on the position difference between the actual position of the target UAV and the actual position of each UAV, to obtain a first upwash velocity in the first time slot.

[0035] Step S104 , performing partial derivative calculation on the relative position between the target UAV and the reference UAV according to the first upwash velocity to obtain a regression vector.

[0036] Step S105 , constructing target upwash data according to the historical upwash velocity, the historical relative position in the historical time slot, the regression vector, and the first upwash velocity.

[0037] Step S106: Calculate the estimated relative position based on the regression vector, the target upwash data and the historical relative position.

[0038] In step S107 , based on the actual positions of all drones, the two drones closest to the target drone are determined as neighboring drones, and the estimated relative positions of the neighboring drones are obtained as the intermediate relative positions.

[0039] Step S108, perform position prediction based on the actual position of the reference drone, the actual position of the target drone, the estimated relative position and the intermediate relative position, obtain the expected position of the target drone in the second time slot, and control the target drone to fly according to the expected position of the drone, wherein the second time slot is the next time slot of the first time slot.

[0040] In steps S101 to S108, as shown in the embodiment of the present application, the actual position of each drone in the first time slot is obtained, and based on this position, the drone closest to the target drone in a fixed direction is determined as a reference drone. The upwash velocity is then calculated using the position difference between the target drone and each drone. Based on this upwash velocity, the partial derivative of the relative position between the target drone and the reference drone is calculated to obtain a regression vector for estimation. Target upwash data is then constructed based on the historical upwash velocity, historical relative position, and the first upwash velocity, thereby dynamically updating the estimated relative position. On this basis, an intermediate relative position is constructed based on the positional relationship between the target drone and the two nearest drones. The actual position, estimated relative position, and intermediate relative position are then combined to perform position prediction. Ultimately, the target drone's expected position in the next time slot is obtained, and the target drone is controlled to fly. By introducing target upwash data construction based on upwash velocity, the target UAV can perceive and respond to the spatial distribution and aerodynamic interference characteristics of other UAVs in the formation. During the entire position prediction process, the target UAV not only makes flow field response judgments based on the upwash velocity generated by the reference UAV, but also introduces multi-point information fusion by introducing the relative estimated position with neighboring UAVs, thereby achieving more robust and directional coordinated adjustment. Through the method of the embodiment of the present application, the position changes, upwash induced velocity and historical trajectory of other UAVs have a real-time, dynamic and physically constrained impact on the target UAV, prompting the target UAV to gradually approach the optimal energy-saving area, thereby driving the formation as a whole to gradually converge to a flight form with a better aerodynamic structure, and ultimately effectively improving the overall energy-saving performance, attitude stability and coordinated flight efficiency of the formation.

[0041] Before introducing the control method of the drone formation provided by the embodiment of the present application, it is necessary to first introduce the scenario diagram of the method. Figure 2 , Figure 2 This is a schematic diagram of an application scenario of the control method for drone formations provided in an embodiment of the present application. Specifically, in the drone flight area, there are K drone formations, each of which consists of The UAV formation moves in a fixed direction at a speed of Flight. A ground base station (GBS) with integrated sensing and communication functions is provided on the ground. The embodiment of the present application establishes a three-dimensional Cartesian coordinate system based on the ground base station, and the origin of the coordinate system is the position of the ground base station, which is denoted as In this embodiment, the positive direction of the z-axis of the coordinate system is perpendicular to the ground and upward, the positive direction of the y-axis is the opposite direction of the aforementioned fixed direction, and the positive direction of the x-axis is determined by rotating the positive direction of the y-axis 90 degrees clockwise. It can be understood that the positive direction of each axis of the coordinate system can be modified according to actual needs. The above-mentioned description of the positive direction of the coordinate axis is only for explaining this embodiment and is not a strict limitation on the establishment of the coordinate system. For a period of time T, the time period is evenly divided into discrete time slots, and the interval between two consecutive time slots is expressed as ( ), and the position of the UAV in the current time slot remains unchanged. Furthermore, the flight altitude of each UAV in the UAV formation is fixed during the flight. drones ( and , ) in the time slots ( ) is represented by . Among them, the vector represents the coordinates of the drone's position projected onto the plane xoy, and H represents the fixed altitude at which the drone flies.

[0042] In this embodiment, each drone in the drone formation can serve as a target drone.

[0043] In step S101 of some embodiments, the first time slot refers to the current time slot, and the actual position of the drone refers to the physical position coordinates of the drone in three-dimensional space during the current time slot. The drone's location is sensed and measured by a ground base station in the area where the drone is located, and the drone's own positioning system (e.g., GNSS) is used to obtain the drone's location information during the first time slot, which is expressed as a coordinate vector in a coordinate system.

[0044] In step S102 of some embodiments, another drone in the same formation that is the closest to the target drone in a given flight direction is selected as a reference drone for the target drone. This step can be implemented by traversing the position coordinates of all other drones in the formation, calculating their relative position differences in a fixed direction, and selecting the drone with the smallest relative position difference as the reference drone.

[0045] The reason for selecting a reference drone for the target drone is that the upwash generated by each drone in the drone formation during flight affects the target drone. However, the upwash far from the target drone is almost zero (see the formula for average induced velocity, which is explained in detail in the following examples). Therefore, the upwash experienced by the target drone primarily comes from the closest preceding drone. When constructing the upwash observation model for the target drone, selecting the reference drone with the greatest aerodynamic influence as the observation reference helps improve the accuracy of subsequent data.

[0046] In other embodiments, the reference drone may be determined by calculating the distance between other drones and the target drone. Figure 3 Step S102 may include but is not limited to steps S301 to S303: Step S301 : Calculate the flight distance between the target UAV and any other UAV based on the actual UAV position of the target UAV and the actual UAV position of any other UAV.

[0047] Step S302: taking the minimum value of the flight distances as the target distance.

[0048] Step S303: Determine a reference drone from the drone formation based on the target distance.

[0049] In step S301 of some embodiments, the flight distance refers to the relative spatial distance between the target UAV and any other UAV in the UAV formation. This distance comprehensively considers the position difference between the target UAV and the other UAVs in a fixed direction and the position difference in a horizontal direction orthogonal to the fixed direction. In addition to directly calculating the Euclidean distance between the target UAV and the other UAVs, the flight distance can also be calculated using the following steps: First, based on the actual position of the target drone, the first position coordinate of the target drone in the fixed direction and the second position coordinate of the target drone in the horizontal direction are determined. The horizontal direction is perpendicular to the fixed direction. In this embodiment, the fixed direction is the negative direction of the -y axis, and the horizontal direction is the positive direction of the x axis. The first position coordinate is the y coordinate of the target drone (the mth drone in the kth drone formation), which is recorded as The second position coordinate is the x coordinate of the target drone, recorded as .

[0050] Then, the third position coordinate of the drone in the fixed direction and the fourth position coordinate of the drone in the horizontal direction are determined based on the actual position of any other drone. , the third position coordinate is the y coordinate of the drone, recorded as The fourth position coordinate is the x coordinate of the drone, recorded as .

[0051] Subsequently, a first distance component is calculated for the first and third position coordinates, and a second distance component is calculated for the second and fourth position coordinates, with a preset distance weight. Specifically, the difference between the first and third position coordinates, and the difference between the second and fourth position coordinates, is squared. The square of the difference between the second and fourth position coordinates is multiplied by the preset distance weight to obtain the second distance component. Finally, the first and second distance components are summed to obtain the flight distance. Specifically, the calculation can be performed using the following analytical formula: (1), (2), in, is the identifier of any drone in the k-th drone formation that is not the m-th drone, is the actual position of the UAV with reference to the UAV, and . is the actual position of the mth UAV (i.e., the target UAV) in the kth UAV formation. is the actual position of any UAV in the k-th UAV formation that is not the m-th UAV. is the flight distance. is the first position coordinate, is the second position coordinate. is the third position coordinate, is the fourth position coordinate. is the distance weight. In this embodiment, the distance weight can be .

[0052] In step S302 of some embodiments, the target distance refers to the minimum value of the flight distances between the target UAV and all other UAVs in the current UAV formation.

[0053] In step S303 of some embodiments, a drone corresponding to the target distance is selected from the current drone formation and used as a reference drone.

[0054] Steps S301 to S303, as illustrated in the embodiments of this application, utilize a mechanism for calculating the flight distance between a target UAV and any other UAVs, combined with a method for selecting the minimum target distance. This allows for rapid and accurate determination of the reference object for a target UAV within a UAV formation, ensuring that the target UAV always uses the UAV with the shortest flight distance as a reference. This approach not only improves the rationality and timeliness of reference selection but also enhances the target UAV's ability to adapt to the current local formation structure, helping to maintain the formation stability and flight path coherence of the target UAV in a dynamic flight environment. This lays the foundation for subsequent aerodynamic coordination, energy optimization, and position prediction based on reference relationships.

[0055] It should be noted that for each drone, the total upwash airflow will change in each time slot, and the total upwash airflow between different drones will also vary. In order to build a formation structure, a leader drone must be determined first. In the kth UAV formation at time slot n, the frontmost UAV in a fixed direction will not have a reference UAV. In this embodiment, the UAV with the smallest y value is used as the leader UAV. , the selection process can refer to the following analytical formula: (3), in, This is the logo of the leader drone. It refers to the position coordinates of any UAV m in a fixed direction in the k-th UAV formation at time slot n.

[0056] When the target UAV is the leader UAV, the method steps performed are different from those of the non-leader UAVs. The relative position is directly calculated and estimated. For the leader UAV, The method for determining the expected position will be further explained in the following embodiment related to step S106.

[0057] In step S103 of some embodiments, it is assumed that The first in the formation The horizontal position of the drone , the total upwash airflow it observes is generated by the rest of the UAVs. The first upwash velocity is the total upwash velocity generated by the other UAVs observed by the target UAV in time slot n, denoted as , please refer to the following analytical formula for details: (4), in, It refers to the total upwash observed by the target UAV at its current position. is the total number of drones in the k-th drone formation, It refers to the average induced velocity.

[0058] It should be noted that it is necessary to establish an aerodynamic model for the drone in order to obtain the average induced speed. The flight power consumed by the drone during flight depends on the drag that needs to be overcome (including parasitic drag and induced drag). The induced drag is generated by the wingtip vortex and downwash effect of the drone. The vortex line of the drone will be near the position [x, y] T Generate an induced velocity. This induced velocity can be calculated according to the Biot-Savart law, specifically referring to the following analytical formula: (5), in, It refers to the velocity field (i.e. induced velocity) induced by the vorticity distribution at the spatial position with coordinates (x, y). It refers to the position integral variable with the direction of the vortex line. arrive vector. represents the vortex circulation (unit is ), the value can be 2 . is pi.

[0059] Furthermore, to simplify the mathematical derivation, the NASA-Burnham-Hallock model can be used to approximate . It is also assumed that the induced velocity along The axis shows Gaussian attenuation, then the analytical formula (5) is converted into: (6), in, It is the preset system parameter. Refers to the wingspan of the drone, which is 1m. It is the mean value corresponding to Gaussian attenuation, and its value can be 0.7. It is the standard deviation of the corresponding Gaussian attenuation and can be 4. represents the vortex circulation (unit is ), the value can be 2 . is the circumference of a circle. e is a natural constant.

[0060] Furthermore, by integrating the induced velocity over the wingspan, the average induced velocity can be obtained: (7), in, It refers to the average induced velocity. Refers to the wingspan of the drone, which is 1m. is the integration variable.

[0061] Furthermore, the average induced velocity can be calculated by referring to the following analytical formula: (8), in, It refers to the wingspan of the UAV. In this embodiment, it is 1m. , represents the distance between the two vortices. represents the vortex circulation (unit is ), the value can be 2 . Indicates the eddy current radius, which can be 0.1m. Since the induced velocity decays Gaussianally along the y-axis, is the mean value corresponding to Gaussian attenuation, which can be 0.7. is the standard deviation of the corresponding Gaussian decay, which can be 4. e is a natural constant.

[0062] See also Figure 4 In some embodiments, step S103 may include but is not limited to steps S401 to S404: In step S401 , a difference is calculated between the actual position of the target UAV and the actual position of the reference UAV to obtain a first relative position difference.

[0063] In step S402 , a difference is calculated between the actual position of each UAV and the actual position of the reference UAV to obtain a second relative position difference.

[0064] Step S403 : Calculating the induced velocity based on the first relative position difference and each second relative position difference to obtain the intermediate upwash velocity.

[0065] Step S404: summing up all the intermediate upwash flow velocities to obtain a first upwash flow velocity.

[0066] In step S401 of some embodiments, the first relative position difference is the two-dimensional position difference between the target UAV and the reference UAV in the coordinate system, specifically including the difference in the x-axis and y-axis directions, respectively denoted as and .in, , .

[0067] In step S402 of some embodiments, the second relative position difference is the two-dimensional position difference between each UAV other than the target UAV and the reference UAV, specifically including the difference in the x-axis and y-axis directions, respectively denoted as .in, , .

[0068] In step S403 of some embodiments, the first relative position difference and the second relative position difference [ , ] is subtracted and inserted into the analytical formula (4) to calculate the average induced velocity, and the intermediate upwash velocity is obtained, that is, .

[0069] In step S404 of some embodiments, the first upwash velocity refers to the total upwash observed by the target UAV in the k-th UAV formation relative to the reference UAV at the current first time slot n. (9), in, Indicates the first upwash velocity.

[0070] Steps S401 to S404 shown in the embodiment of the present application introduce an upwash velocity calculation method based on actual position difference, which can realize the local perception of the current formation aerodynamic environment by the target UAV. A quantitative expression of the spatial upwash effect is constructed through the first relative position difference and the second relative position difference, and then the intermediate upwash velocity is calculated, and all the intermediate upwash velocities are summed up, so that the target UAV can accurately obtain the degree of total upwash influence at the current position. The method of this embodiment improves the spatial resolution capability of induced velocity modeling, which is conducive to realizing optimized response based on the actual aerodynamic environment in subsequent flight control, and effectively enhances the energy-saving control effect and attitude stability of the UAV formation under complex spatial distribution. In step S104 of some embodiments, the relative position between the target UAV and the reference UAV, i.e., the coordinate difference between the target UAV and the reference UAV, corresponds to the equation (9) and The calculation process of the regression vector can refer to the following analytical formula: (10), in, represents the regression vector. yes Abbreviation for first upwash velocity. Represents the component of the first relative position difference on the x-axis. Represents the component of the first relative position difference on the y-axis. is a binary variable ( ),when = -1, it means the target UAV tends to follow behind the reference UAV on the right side. =1, it means that the target UAV tends to follow behind the left side of the reference UAV.

[0071] In step S105 of some embodiments, the historical time slot is the time slot before the first time slot, the historical relative position is the relative position between the target UAV and the reference UAV in the historical time slot, and the historical upwash velocity is the upwash velocity in all time slots before the first time slot.

[0072] See also Figure 5 In some embodiments, step S105 may also include but is not limited to steps S501 to S503: Step S501: taking the maximum value of the historical rushing velocity as the basic rushing velocity.

[0073] Step S502 : multiplying the transposed matrix of the regression vector by the historical relative position to obtain the upwash velocity change value.

[0074] Step S503 , performing integrated calculation based on the basic upwash velocity, the upwash velocity change value and the first upwash velocity to obtain target upwash data.

[0075] In step S501 of some embodiments, the base upwash velocity refers to the maximum total upwash observed by the target UAV in all time slots before the first time slot, denoted as .

[0076] In step S502 of some embodiments, it is assumed that the position where the optimal energy saving effect can be achieved is marked relative to the position coordinates of the reference drone. For the target UAV m, the optimal relative position of the first time slot n (that is, the position relative to the reference UAV that can achieve the best energy saving effect) is recorded as ) is roughly estimated, and the optimal relative position of the previous time slot n-1 (denoted as ) as the initial value of the optimal relative position of the first time slot n, that is, The change in upwash velocity is .

[0077] In step S503 of some embodiments, the calculation process of the target up-cleaning data can refer to the following analytical formula: (11), in, Indicates that the target is uploading data. Indicates the basic flow velocity. Indicates the first upwash velocity. Indicates the change in uprush flow velocity. It means the mean is zero and the variance is Uncorrelated additive white Gaussian noise (AWGN).

[0078] Steps S501 to S503, as shown in the embodiment of this application, utilize the historical maximum upwash velocity as the base upwash velocity and quantify the relationship between the regression vector's guidance information and the historical relative position as the upwash velocity change value. Ultimately, this constructs target upwash data, achieving a dynamic, time-sensitive quantitative representation of the upwash impact on the target drone. This processing approach not only fully utilizes the favorable aerodynamic information in the historical time slots but also corrects the airflow trend based on the current observation direction, significantly improving the robustness and reference value of the upwash observation data in subsequent position estimation.

[0079] In step S106 of some embodiments, in order to determine the optimal relative position of the target UAV to achieve energy-saving flight in the current time slot , can be combined with the analytical formula (11) to construct the optimal estimation problem, the goal of which is to find a , so that the combined mean square error of all drones is minimized. To this end, the optimal estimation problem can be specifically referred to the following analytical formula: (12), This problem can be solved using the ATC diffusion LMS algorithm. Figure 6 In some embodiments, step S106 includes but is not limited to steps S601 to S603: Step S601 , performing a difference calculation between the target upwash data and the upwash velocity change value to obtain upwash velocity error data.

[0080] Step S602 : multiplying the regression vector, the upwash velocity error data, and the preset step value to obtain position correction data.

[0081] Step S603: Position superposition is performed based on the historical relative position and the position correction data to obtain an estimated relative position.

[0082] In step S601 of some embodiments, the uprush velocity error data is .

[0083] In step S602 of some embodiments, the preset step value is the corresponding step size of the LMS algorithm, which can be 2×10 -3 , but not limited to this. Position correction data refers to the adjustment amount for iteratively updating the relative position of the target UAV based on the current estimation error, recorded as .

[0084] In step S603 of some embodiments, the calculation process of estimating the relative position may refer to the following analytical formula: (13), in, Represents an estimated relative position. Represents position correction data, where Indicates the preset step value. represents the regression vector. Indicates that the target is uploading data. Indicates the change in uprush flow velocity.

[0085] In steps S601 to S603, as shown in the embodiment of this application, upwash velocity error data is constructed by subtracting the target upwash data from the upwash velocity change value, and position correction data is calculated by combining the regression vector and step size value. This position correction data is then superimposed with the historical relative position to form an estimated relative position, thus implementing a dynamic position estimation mechanism based on error feedback. This mechanism can sense the relative offset between the aerodynamic state and the target drone in real time, and continuously guide the target drone to a more energy-efficient position area, effectively improving the stability, adaptability, and energy efficiency of the drone formation in a changing aerodynamic environment.

[0086] It should be noted that if the target drone is the leader drone , steps S102 to S106 do not need to be executed, and the estimated relative position can be directly obtained. For details, please refer to the following analytical formula: (14), in, Represents the leader drone in the k-th drone formation The estimated relative position at the next time slot, Indicates that the leader drone The optimal relative position in the first time slot.

[0087] In step S107 of some embodiments, based on the current position coordinates, the Euclidean distances between all drones and the target drone are calculated, and the distances are sorted from smallest to largest, and the first two are selected as neighboring drones. within, no. The first in the formation The neighbor set of the drone is denoted as .

[0088] Then, the estimated relative positions of these two neighboring UAVs are obtained and used as the intermediate relative position of the target UAV.

[0089] In step S108 of some embodiments, the coordinates of the target drone's desired drone position in the second time slot can be recorded as The second time slot is the next time slot of the first time slot. Controlling the target UAV to fly according to the desired UAV position means adjusting the heading, speed, and attitude of the target UAV through the flight controller so that the UAV approaches the desired position.

[0090] See also Figure 7 In some embodiments, step S108 may include but is not limited to steps S701 to S702: Step S701, performing weighted summation on the estimated relative position and the intermediate relative position according to preset prediction weight data to obtain a target position change value.

[0091] Step S702 : updating the actual position of the target UAV according to the target position change value and the actual position of the reference UAV to obtain the expected position of the target UAV in the second time slot.

[0092] In step S701 of some embodiments, the value range of the prediction weight data is [0, 1], which is recorded as ,satisfy And when hour, =0.

[0093] The target position change value refers to the coordinate change of the target drone in the second time slot compared to the actual position of the drone in the first time slot. The specific calculation process can be referred to the following analytical formula: (15), in, Indicates the target position change value. Represents the set of neighbor drones. Represents the predicted weight data, which can be . Indicates the middle relative position.

[0094] It should be noted that in the first time slot of the current time period T, the actual position of each drone in the formation is , parameters Λm,k, total number of time slots N and initial estimate They are all initialized at the beginning of the algorithm execution and are known quantities.

[0095] In step S702 of some embodiments, see Figure 8 In some embodiments, step S702 may include but is not limited to steps S801 to S805: Step S801: Obtain the flight speed of the target UAV in a fixed direction.

[0096] Step S802 : determining a first position change amount in a fixed direction and a second position change amount in a horizontal direction based on the target position change value; wherein the horizontal direction is perpendicular to the fixed direction.

[0097] Step S803 , performing position calculation based on the first position change, the actual position of the target UAV, and the actual position of the reference UAV to obtain a first target coordinate in a fixed direction.

[0098] Step S804 , performing position calculation based on the second position change, the flight speed, the actual position of the target UAV, and the actual position of the reference UAV to obtain a second target coordinate in the horizontal direction.

[0099] Step S805: determining the desired position of the UAV according to the first target coordinates and the second target coordinates.

[0100] In step S801 of some embodiments, the flight speed of the target UAV in a fixed direction is recorded as .

[0101] In step S802 of some embodiments, the horizontal direction and the fixed direction have been described in detail in the embodiment of step S301 and will not be repeated here.

[0102] Change the target position by Perform vector decomposition in fixed and horizontal directions, i.e. Among them, the first position change is , the second position change is .

[0103] In step S803 of some embodiments, due to the limited speed of drones in practice, they may not be able to reach the desired location in every time slot. To solve this problem, a variable Furthermore, the calculation process of the first target coordinates can refer to the following analytical formula: (16), in, Indicates the first target position. represents the actual y coordinate of the target UAV at the first time slot n. Indicates the y coordinate of the reference drone. Indicates the first position change. Represents the state transition noise corresponding to a fixed direction, which obeys the Gaussian distribution , in this embodiment, =2×10 -4 . Indicates the flight speed, which can be 5 . Indicates the duration of each time slot, which can be 0.05s.

[0104] In step S804 of some embodiments, the calculation process of the second target coordinates may refer to the following analytical formula: (17), in, Indicates the second target position. represents the actual x-coordinate of the target UAV at the first time slot n. Indicates the x-coordinate of the reference drone. is the aforementioned binary variable, which indicates the following position of the target UAV relative to the reference UAV. Indicates the second position change. Represents the state transition noise corresponding to the horizontal direction, which obeys Gaussian distribution , in this embodiment, = .

[0105] It should be noted that for the leader drone For , the calculation of the first target coordinate and the second target coordinate refers to the following analytical formula: (18), in, Indicates leader drone The second target coordinates. Indicates leader drone The actual x-coordinate at the first time slot n. Represents the state transition noise corresponding to the horizontal direction, which obeys Gaussian distribution . Indicates leader drone The first target coordinates. Indicates leader drone The actual y coordinate at the first time slot n. Indicates flight speed. Indicates the duration of each time slot. Represents the state transition noise corresponding to a fixed direction, which obeys the Gaussian distribution .

[0106] In step S805 of some embodiments, the target coordinates in the two directions are vector-merged to generate the expected position coordinates of the target drone in the second time slot. The control method for the drone formation provided by the embodiment of the present application is traversed until the time slot number n exceeds the total number of time slots N.

[0107] Steps S801 to S805, as illustrated in the present embodiment, utilize a joint modeling mechanism combining flight speed, position change, and directional decomposition to independently calculate the target position in both the fixed and horizontal directions. This calculation generates the target drone's desired position, enabling the target drone to accurately respond to directional velocity characteristics and relative position differences during the prediction process. This improves the physical consistency and path smoothness of the position deduction. This method enhances the target drone's adaptability to the formation's aerodynamic environment.

[0108] In steps S701 and S702, as shown in the embodiment of this application, the estimated relative position and the intermediate relative position are weighted and fused based on the predicted weight data to form a target position change value. This target position change value is then superimposed with the actual position of the reference drone to obtain the target drone's expected position in the next time slot, thus achieving flexible control of the target drone's flight path. This mechanism balances estimation accuracy with neighboring collaborative information, improving the positioning stability and path continuity of the target drone in complex formation structures, and further enhancing the ability of drone formations to maintain structural order and energy-saving coordination in dynamic environments.

[0109] The present application also provides an electronic device comprising a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the aforementioned drone formation control method. The electronic device can be any smart terminal, such as a tablet computer or an in-vehicle computer.

[0110] See also Figure 9 , Figure 9 The hardware structure of an electronic device according to another embodiment is shown. The electronic device includes: The processor 901 can be implemented as a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application. The memory 902 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 902 can store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 902 and is called by the processor 901 to execute the control method for the drone formation of the embodiments of this application. Input / output interface 903, used to implement information input and output; Communication interface 904, used to implement communication interaction between this device and other devices, which can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WiFi, Bluetooth, etc.); Bus 905 , which transmits information between various components of the device (e.g., processor 901 , memory 902 , input / output interface 903 , and communication interface 904 ); The processor 901 , the memory 902 , the input / output interface 903 and the communication interface 904 are connected to each other in communication within the device via a bus 905 .

[0111] An embodiment of the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the above-mentioned control method for the drone formation.

[0112] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely arranged relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0113] The control method, electronic device and storage medium of the drone formation provided in the embodiment of the present application obtain the actual position of each drone in the first time slot, and determine the drone closest to the target drone in a fixed direction as a reference drone based on the position. Then, the upwash velocity is calculated based on the position difference between the target drone and each drone, and the partial derivative of the relative position between the target drone and the reference drone is calculated based on the upwash velocity to obtain a regression vector for estimation. The target upwash data is constructed based on the historical upwash velocity, historical relative position and the first upwash velocity, thereby realizing the dynamic update of the estimated relative position. On this basis, the intermediate relative position is constructed based on the position relationship between the target drone and the two nearest drones, and the actual position, estimated relative position and intermediate relative position are integrated to perform position prediction, finally obtaining the target drone's expected drone position in the next time slot and controlling the target drone to fly. By introducing target upwash data construction based on upwash velocity, the target UAV can perceive and respond to the spatial distribution and aerodynamic interference characteristics of other UAVs in the formation. During the entire position prediction process, the target UAV not only makes flow field response judgments based on the upwash velocity generated by the reference UAV, but also introduces multi-point information fusion by introducing the relative estimated position with neighboring UAVs, thereby achieving more robust and directional coordinated adjustment. Through the method of the embodiment of the present application, the position changes, upwash induced velocity and historical trajectory of other UAVs have a real-time, dynamic and physically constrained impact on the target UAV, prompting the target UAV to gradually approach the optimal energy-saving area, thereby driving the formation as a whole to gradually converge to a flight form with a better aerodynamic structure, and ultimately effectively improving the overall energy-saving performance, attitude stability and coordinated flight efficiency of the formation.

[0114] For example, in some embodiments, the first formation is set to consist of 19 drones, whose positions are set at meters. Please refer to Figure 10 , Figure 10 This is a schematic diagram of simulation results provided by an embodiment of the present application. The first row of two-dimensional images shows the coordinates of the actual position of each drone in the formation on plane xoy at different time slots. The horizontal axis represents the x-coordinate, and the vertical axis represents the y-coordinate. The second row of three-dimensional images shows the total upwash observed corresponding to each drone's position coordinate.

[0115] In other embodiments, a drone formation consisting of 9 drones is set, and the positions are set at [ meters. Please refer to Figure 11 , Figure 11 This is another simulation result diagram provided by the embodiment of the present application. Figure 10 and Figure 11What is certain is that as the flight time progresses, the aerodynamic interference areas of each UAV in the early time slot are more complex, and most of the UAVs are distributed in the downwash area caused by the reference UAV, which causes a larger induced drag effect, significantly increases the power required per unit propulsion, and leads to a higher overall flight energy consumption level.

[0116] To address the above issues, each drone executes the drone formation control method provided in the embodiment of the present application, dynamically updates its own desired position based on historical flight information and upwash velocity data, and exchanges status information with adjacent drones in real time to achieve coordinated position updates. Ultimately, the entire drone formation gradually forms a stable "V"-shaped structure during the convergence phase, which is consistent with the energy-saving formation form adopted by migrating birds in biological groups, thereby maximizing the upwash effect generated by each reference drone. Figure 10 Compared to the formation shown in Figure 11 The formation shown here reached convergence in less than 5 seconds. This is because the second formation consists of only nine drones, which means less information exchange between adjacent drones. This structure significantly reduces the additional power consumption caused by induced drag during individual propulsion, effectively optimizing the overall flight energy consumption of the drone formation.

[0117] The embodiments described in the embodiments of this application are intended to more clearly illustrate the technical solutions of the embodiments of this application and do not constitute a limitation on the technical solutions provided by the embodiments of this application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of this application are also applicable to similar technical problems.

[0118] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0119] Those skilled in the art will appreciate that all or some of the steps in the methods, systems, and functional modules / units in the devices disclosed above may be implemented as software, firmware, hardware, or appropriate combinations thereof.

[0120] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0121] It should be understood that in this application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following items" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0122] In the several embodiments provided in this application, it should be understood that the disclosed methods can be implemented in other ways. The units described above as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0123] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0124] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes multiple instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: various media that can store programs, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0125] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but are not intended to limit the scope of the present invention. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and essence of the present invention should be within the scope of the present invention.

Claims

1. A method for controlling a UAV formation, characterized in that: Applied to a target drone among at least two drones in a drone formation, wherein the drone formation flies in a fixed direction, the method comprises: Obtaining the actual position of each drone in the first time slot; According to the actual position of the UAV, determining the UAV closest to the target UAV in the fixed direction to obtain a reference UAV; Calculating an upwash velocity based on a position difference between the actual position of the target UAV and the actual position of each of the UAVs to obtain a first upwash velocity in the first time slot; performing partial derivative calculation on the relative position between the target UAV and the reference UAV according to the first upwash velocity to obtain a regression vector; Target upwash data is constructed based on the historical upwash velocity, the historical relative position in the historical time slot, the regression vector, and the first upwash velocity; wherein the historical time slot is the time slot before the first time slot, the historical relative position is the relative position between the target UAV and the reference UAV in the historical time slot, and the historical upwash velocity is the upwash velocity in all time slots before the first time slot; Calculating an estimated relative position based on the regression vector, the target upwash data, and the historical relative position; According to the actual positions of all the drones, two drones closest to the target drone are determined as neighboring drones, and the estimated relative positions of the neighboring drones are obtained as intermediate relative positions; Position prediction is performed based on the actual position of the reference UAV, the actual position of the target UAV, the estimated relative position and the intermediate relative position to obtain the expected position of the target UAV in the second time slot, and the target UAV is controlled to fly according to the expected position of the UAV, wherein the second time slot is the next time slot of the first time slot.

2. The method according to claim 1, characterized in that The constructing target upwash data according to the historical upwash velocity, the historical relative position in the historical time slot, the regression vector, and the first upwash velocity includes: The maximum value of the historical rushing velocity is taken as the basic rushing velocity; Multiplying the transposed matrix of the regression vector by the historical relative position to obtain a change value of the upwash velocity; The target upwash data is obtained by performing an integrated calculation based on the basic upwash velocity, the upwash velocity change value and the first upwash velocity.

3. The method according to claim 2, characterized in that The calculating the estimated relative position according to the regression vector, the target upwash data and the historical relative position includes: performing a difference calculation between the target upwash data and the upwash velocity change value to obtain upwash velocity error data; Multiplying the regression vector, the upwash velocity error data, and a preset step value to obtain position correction data; Position superposition is performed based on the historical relative position and the position correction data to obtain the estimated relative position.

4. The method according to claim 1, wherein The performing position prediction based on the actual position of the reference UAV, the actual position of the target UAV, the estimated relative position, and the intermediate relative position to obtain the expected UAV position of the target UAV in the second time slot includes: Performing a weighted summation on the estimated relative position and the intermediate relative position according to preset predicted quantity weight data to obtain a target position change value; The actual position of the target UAV is updated according to the target position change value and the actual position of the reference UAV to obtain the expected position of the target UAV in the second time slot.

5. The method according to claim 4, characterized in that The updating of the actual position of the target UAV according to the target position change value and the actual position of the reference UAV to obtain the expected UAV position of the target UAV in the second time slot includes: Obtaining the flight speed of the target UAV in the fixed direction; Determining a first position change amount in the fixed direction and a second position change amount in a horizontal direction based on the target position change value; wherein the horizontal direction is perpendicular to the fixed direction; Performing position calculation based on the first position change, the actual position of the target UAV, and the actual position of the reference UAV to obtain a first target coordinate in the fixed direction; Performing position calculation based on the second position change, the flight speed, the actual position of the target UAV, and the actual position of the reference UAV to obtain a second target coordinate in the horizontal direction; The desired position of the drone is determined according to the first target coordinates and the second target coordinates.

6. The method according to claim 1, characterized in that The step of determining the drone closest to the target drone in the fixed direction according to the actual position of the drone to obtain a reference drone includes: Calculating the flight distance between the target drone and any other drone based on the actual drone position of the target drone and the actual drone position of any other drone; Taking the minimum value of the flight distances as the target distance; The reference drone is determined from the drone formation based on the target distance.

7. The method according to claim 6, characterized in that Calculating the flight distance between the target UAV and any other UAV based on the actual UAV position of the target UAV and the actual UAV position of any other UAV includes: Determining a first position coordinate of the target drone in the fixed direction and a second position coordinate of the target drone in the horizontal direction based on the actual position of the target drone; wherein the horizontal direction is perpendicular to the fixed direction; Determining a third position coordinate of the drone in the fixed direction and a fourth position coordinate of the drone in the horizontal direction based on the actual position of any other drone; Calculating a first distance component for the first position coordinate and the third position coordinate, and calculating a second distance component for the preset distance weight, the second position coordinate, and the fourth position coordinate; The flight distance is obtained by summing the first distance component and the second distance component.

8. The method according to any one of claims 1 to 5, characterized in that The calculating the upwash velocity according to the position difference between the actual position of the target UAV and the actual position of each UAV to obtain a first upwash velocity in the first time slot includes: Calculating a difference between the actual position of the target UAV and the actual position of the reference UAV to obtain a first relative position difference; Calculating a difference between the actual position of each drone and the actual position of the reference drone to obtain a second relative position difference; Calculating an induced velocity based on the first relative position difference and each of the second relative position differences to obtain an intermediate upwash velocity; All the intermediate upwash flow velocities are summed up to obtain the first upwash flow velocity.

9. An electronic device, characterized in that: The electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

Images