Unmanned aerial vehicle homeward voyage searching method and system applied to mobile airport

Abstract

The invention provides an unmanned aerial vehicle homeward voyage searching method and system applied to a mobile airport. The method comprises the following steps: acquiring positions and motion parameters of the mobile airport and an unmanned aerial vehicle; estimating the state of the mobile airport based on the position and motion parameters of the mobile airport by using a Kalman filter dynamic model to obtain an estimated predicted value; comparing the estimated predicted value with the current position and motion parameters of the unmanned aerial vehicle, and determining the motion direction and motion speed of the unmanned aerial vehicle; judging whether the endurance time of the unmanned aerial vehicle supports the unmanned aerial vehicle to seek to the mobile airport according to the determined movement direction and movement speed, and if so, controlling the unmanned aerial vehicle to move by taking the determined movement direction and movement speed as a control instruction; if not, an intelligent landing strategy is adopted, a landing point is selected, and a control instruction is formed. The unmanned aerial vehicle can automatically track the moving airport, the moving airport does not need to wait for the unmanned aerial vehicle in situ, and the inspection efficiency is improved.

Description

Technical Field

[0001] The present invention belongs to the field of unmanned aerial vehicle landing control, and in particular relates to a method and system for tracking the return of unmanned aerial vehicles applied to a mobile airport. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] Drone inspections are characterized by high efficiency, high precision, and high safety. Drone airport automated inspections have become a relatively common automated inspection method. Traditional drone airports are fixed airports that need to be installed in fixed locations and can only inspect areas within the inspection radius. Although this gets rid of the problem that inspectors need to master drone operation skills, it also increases the limitations of the inspection area to a certain extent. Therefore, mobile airport products have been derived. Mobile airports have realized the mobility of airports and got rid of the limitations of traditional fixed airport inspection areas. At the same time, the mobile airport's off-site take-off and landing function (taking off from point A, the mobile airport moves to point B and waits for the drone to land at point B) has also greatly improved the inspection efficiency of mobile airports. However, there are still some problems when returning and landing in a different location.

[0004] The off-site landing of drones at mobile airports currently available on the market means that after the mobile airport stops moving in a safe area, the current coordinates of the mobile airport are updated to the drone, and the drone returns to the current location of the mobile airport for landing. During this process, since the drone's own interface for refreshing the return point is called, the drone must exit the return state (i.e., stationary), so it is necessary to ensure that the vehicle is stationary and wait for the drone to return. The vehicle cannot move in real time (i.e., update the return coordinates for the drone in real time. If the return coordinates are updated in real time, the drone will "fly-stop-fly-stop..."), there will be too long a wait, resulting in a waste of inspection time, and there will also be a situation where there is no time to react to the sudden need to move the mobile airport.

[0005] For the on-site environment of mobile airport applications or long-distance target tracking, although the existing drone target tracking method can achieve target tracking through vision, it also has this defect in the existing scenario. The first requirement that drones must meet for tracking targets through vision is that the drone can identify the target in real time. Before the drone can achieve target tracking, the characteristic information of the target must be entered to ensure that the drone can identify the target to be followed. At the same time, the drone cannot be too far away from the target to be followed. At a long distance, the target in the vision is easily lost.

[0006] In addition, the method of simply relying on GPS positioning to track the target is too mechanical. The drone will only fly in a straight line to the acquired target point, such as Figure 1As shown, a lot of flight time is wasted. Summary of the invention

[0007] In order to solve the above problems, the present invention proposes a method and system for tracking the return of a UAV applied to a mobile airport. The present invention enables the UAV to automatically track the moving mobile airport, and the mobile airport does not have to wait for the UAV on the spot, thereby improving the inspection efficiency.

[0008] According to some embodiments, the present invention adopts the following technical solutions:

[0009] A method for tracking the return of a drone at a mobile airport includes the following steps:

[0010] Get the location and motion parameters of mobile airports and drones;

[0011] Using the Kalman filter dynamic model, the state of the mobile airport is estimated based on the location and motion parameters of the mobile airport to obtain an estimated prediction value;

[0012] Comparing the estimated prediction value with the current position and motion parameters of the UAV to determine the motion direction and motion speed of the UAV;

[0013] Determine whether the drone's flight time supports the drone to track to the mobile airport in the determined direction and speed of movement. If so, use the determined direction and speed of movement as control instructions to control the movement of the drone; if not, adopt an intelligent landing strategy, select a landing point, and form a control instruction.

[0014] As an optional implementation, the motion parameters of the mobile airport include the acceleration components of the mobile airport along three orthogonal axes, the absolute direction of the mobile airport and the current speed of the mobile airport, and the motion parameters of the drone include the acceleration and speed in the horizontal and vertical directions.

[0015] As an optional implementation, the process of estimating the state of the mobile airport based on the position and motion parameters of the mobile airport using the Kalman filter dynamic model includes:

[0016] Set the initial state estimate and initial covariance matrix;

[0017] At each time step, the position, velocity and acceleration of the mobile airport at the current time step are formed into a state vector as the observation equation;

[0018] Based on the observation equation, the Kalman gain is calculated, the state estimate and covariance matrix are updated, and the estimated prediction value is obtained.

[0019] As an optional implementation, the process of comparing the estimated prediction value with the current position and motion parameters of the drone to determine the motion direction and motion speed of the drone includes:

[0020] The absolute direction of the mobile airport relative to the drone is calculated by combining the estimated predicted value of the mobile airport coordinates with the current position coordinates of the drone. The velocity component of the mobile airport in the absolute direction relative to the drone is calculated by combining the horizontal and vertical components of the current velocity of the mobile airport to determine the velocity value of the drone.

[0021] As an optional implementation, the process of determining whether the drone's flight time supports the drone to track the mobile airport according to the determined movement direction and movement speed includes:

[0022] If the velocity component v of the mobile airport in the absolute direction relative to the drone is greater than the maximum speed Vmax of the drone, the drone cannot catch up with the mobile airport and needs to reduce the moving speed of the mobile airport. If the velocity component v of the mobile airport in the absolute direction relative to the drone is less than the maximum speed Vmax of the drone, the time it takes for the drone to catch up with the mobile airport is calculated based on the difference between the two and the distance between the mobile airport and the drone, and the drone's endurance time is judged based on the time.

[0023] As an optional implementation method, an intelligent landing strategy is adopted to select a landing point. The process of forming a control command includes: the mobile airport obtains the drone's location information and environmental information, and searches for a suitable landing point outward with the drone's current coordinate point as the center of the circle. After removing points that do not meet the landing conditions, the drone selects the nearest landing point to land.

[0024] As an optional implementation method, in the process of controlling the movement of the drone by using a determined movement direction and movement speed as control instructions, if the difference between the heading of the new path planning formed in the control instruction and the original flight path exceeds a predetermined value, a rotation rate limiting strategy is used to limit the rotation speed of the drone, so that the steering process of the drone is smooth and controllable.

[0025] As a further step, the process of limiting the speed of the drone's rotation using the rotation rate limit strategy includes:

[0026] Set the current heading angle of the drone θ current ;

[0027] Set the heading angle θ that the drone needs to turn to target ;

[0028] Calculate the angle difference Δθ = mod(θ target -θ current +180,360)-180;

[0029] Define the maximum rotational angular velocity ω max ;

[0030] In each control cycle, the angle increment Δθ that the drone should turn to is calculated increment =sign(Δθ)*ω max *T s , T s is the sampling time of the control loop;

[0031] Update the current heading angle θ of the drone current =θ current +θ increment ;

[0032] If Δθ>Δθ increment , repeatedly calculate the angle difference to update the current heading angle of the drone.

[0033] A return tracking system for a UAV applied to a mobile airport, comprising:

[0034] Data acquisition module, used to obtain the location and motion parameters of mobile airports and drones;

[0035] A dynamic estimation module is used to estimate the state of the mobile airport based on the position and motion parameters of the mobile airport using a Kalman filter dynamic model to obtain an estimated prediction value;

[0036] A motion calculation module, used to compare the estimated prediction value with the current position and motion parameters of the UAV to determine the motion direction and speed of the UAV;

[0037] The return tracking module is used to determine whether the drone's flight time supports the drone to track to the mobile airport in the determined direction and speed of movement. If so, the determined direction and speed of movement are used as control instructions to control the movement of the drone; if not, an intelligent landing strategy is adopted to select the landing point and form a control instruction.

[0038] A computer-readable storage medium is used to store computer instructions, and when the computer instructions are executed by a processor, the steps in the above method are completed.

[0039] An electronic device comprises a memory and a processor, and computer instructions stored in the memory and executed on the processor. When the computer instructions are executed by the processor, the steps in the above method are completed.

[0040] Compared with the prior art, the present invention has the following beneficial effects:

[0041] The present invention innovatively proposes a method for tracking the return of drones to mobile airports. The Kalman filter algorithm framework is used to predict the movement pattern of mobile airports, so that the drones can fly to the predicted points, which greatly saves the return time. At the same time, through the analysis of the predicted points and the current status data of the drones, it can be concluded whether the drone can successfully return to the mobile airport and the emergency strategy if it cannot return to the airport, which ensures the strategic planning of the drone to catch up with the mobile airport in the shortest time and also ensures the safe landing of the drone.

[0042] The present invention innovatively provides a smooth turning strategy through the route. When controlling the drone to fly to the predicted point, the drone will not turn suddenly. This saves power while protecting the drone motor from sudden acceleration, which will reduce its lifespan. It can also make the pursuit process smoother and improve the user experience.

[0043] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0045] Figure 1 It is a schematic diagram of using GPS positioning to track a target in the prior art;

[0046] Figure 2 The present invention is a schematic diagram of a return tracking process of a UAV applied to a mobile airport according to an embodiment. DETAILED DESCRIPTION

[0047] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0048] It should be noted that the following detailed descriptions are all illustrative and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0049] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0050] In the absence of conflict, the embodiments in this application and the features in the embodiments may be combined with each other.

[0051] Embodiment 1

[0052] The return tracking method for the drone used in the mobile airport of this embodiment means that during the return period of the drone airport after the drone operation, the drone can automatically track the real-time location of the mobile airport according to the current information of the mobile airport, and keep synchronization with the mobile airport, waiting for subsequent landing.

[0053] A method for tracking the return of a UAV at a mobile airport, such as Figure 2 As shown in the figure, the Kalman filter algorithm framework is used to predict the future path of the mobile airport, and the speed and acceleration of the drone are calculated in combination with the current speed and acceleration of the mobile airport to achieve the purpose of tracking the mobile airport. On this basis, a solution is also provided for the hardware device to obtain the state parameters of the mobile airport and how the drone can control a gentle turn through the algorithm.

[0054] A detailed introduction is given below.

[0055] First, obtain the mobile airport and drone parameter status data.

[0056] In this embodiment, a three-axis accelerometer is installed on the mobile airport to obtain the acceleration components of the mobile airport along three orthogonal axes. A magnetometer is installed to obtain the absolute direction of the mobile airport. A speed sensor is installed to obtain the current speed of the mobile airport. An RTK positioning module is installed to obtain the current coordinates of the mobile airport.

[0057] Secondly, the constructed Kalman filter dynamic model is used to predict the state of the mobile airport.

[0058] The Kalman filter dynamic model abstracts the existing equipment state parameters into mathematical variables.

[0059] The state vector of a mobile airport: Contains the horizontal position x at the kth time step k , horizontal speed Vertical position y k and vertical speed

[0060] The horizontal acceleration of the drone: a x,k .

[0061] The vertical acceleration of the drone: a y,k .

[0062] Sampling time interval: T s .

[0063] Maximum speed of the drone: V max .

[0064] Maximum acceleration of the drone: A max .

[0065] The state equation is based on the model to predict the state X of the target at the next time k. k It takes into account the known dynamic model and the state X at the previous time k-1 k-1 The state equation reflects the prior prediction of the target state, that is, the prediction result based on the model in the absence of new observation information.

[0066] Since the mobile airport is carried by a car, an acceleration model suitable for motor vehicles is selected in the model, which is:

[0067] X k =FX k-1 +Ga target,k-1 +w k-1

[0068] Wherein, F is the state transfer matrix, which describes the evolution of the state vector without external influence. For the acceleration model in this embodiment, it can be written as:

[0069]

[0070] Ts is the sampling time interval; G is the control input matrix, which describes the influence of the control input on the state. For the acceleration model, it is:

[0071]

[0072] a target,k-1 is the acceleration vector of the mobile airport at the k-1th time step, including horizontal acceleration and vertical acceleration.

[0073] w k-1 is a noise vector, which reflects the uncertainty of the model. This paper assumes that it is a zero-mean Gaussian white noise, and its covariance matrix is ​​Q.

[0074] The observation equation provides direct measurement or observation information of the target state. Even if the model is accurate, there are always errors in the measurements in the real world, which may be caused by sensor noise, the model not fully matching the real physical process, external disturbances, etc. The observation equation converts these measurements Z k Transformed into the state vector X k The form of correlation allows the Kalman filter to compare the measured values ​​with the model predictions. The observation equation in this embodiment is:

[0075]

[0076] Here, Z k is the observation vector, which contains the measured position, velocity and acceleration of the target; V k is the observation noise vector, which reflects the uncertainty of the measurement. Although the sensor is "precise", there may still be some small errors or deviations.

[0077] The process of the Kalman filter algorithm includes:

[0078] (1) Initialization

[0079] Setting the initial state estimate and the initial covariance matrix P 0|0 .

[0080] (2) Prediction Steps

[0081] At each time step k, the state and covariance are predicted using the following formula:

[0082]

[0083] P k|k-1 =FP k-1|k-1 F T +Q.

[0084] (3) Update steps

[0085] When the measured value Z is received k After that, the following steps are performed to update the state estimate and covariance:

[0086] Calculate the Kalman gain:

[0087] K k =P k|k-1 H T (HP k|k-1 H T +R) -1

[0088] Update the state estimate:

[0089]

[0090] Update the covariance matrix:

[0091] P k|k =(IK k H)P k|k-1 .

[0092] Once the Kalman filter provides an updated state estimate of the mobile airport The drone can determine the direction of the drone's advancement based on this estimated predicted value, so as to achieve the encounter between the shortest flight path of the drone and the mobile airport. By estimating the predicted value of the mobile airport coordinates and the current position coordinates of the drone, the absolute direction β of the mobile airport relative to the drone can be obtained. From the horizontal and vertical components of the current speed of the mobile airport, the speed component v of the mobile airport in the absolute direction β relative to the drone can be obtained, thereby determining the speed value of the drone. If v > Vmax, the drone cannot catch up with the mobile airport, and the driver needs to be reminded to reduce the speed. If v < Vmax, the time for the drone to catch up with the mobile airport can be calculated, and it can also be judged whether the endurance time of the drone is supported. S is the distance from the drone to the car. S / (maximum speed of the drone - vehicle speed) = estimated time. If it does not support returning, the driver needs to be notified to slow down.

[0093] When the drone determines that it cannot catch up with the driver, the drone will send a request for deceleration or stop and wait to the driver through the mobile airport. During this period, the drone will continue to pursue the mobile airport according to the original plan. When the drone's battery reaches the low battery warning, and the drone still cannot catch up with the mobile airport, the drone will adopt an intelligent landing strategy.

[0094] First of all, relying on the service support of the Amap development platform and the RTK precise positioning data on the drone, the mobile airport can obtain the current position of the drone and the environment it is in. Relying on the service support of the high development platform, the mobile airport will use the current coordinate point of the drone as the center of the circle and search outward for suitable landing points. Remove points that do not meet the landing conditions such as lakes, forests, and buildings, and the drone selects the nearest landing point to land. At the same time, the drone turns on the buzzer warning during landing to prevent injury to pedestrians or animals.

[0095] During the flight of the drone to the new predicted position in the new heading, there will be a sudden change in the heading value relative to the original flight path, which will cause the fuselage to shake. At the same time, flying in a straight line will make the method of pursuing the target too mechanical. The drone will only fly in a straight line towards the obtained target point (see the third part, simple GPS return for illustration), resulting in a waste of time. Now, by obtaining the predicted position of the mobile nest, the drone can fly to the predicted point one step ahead. At the same time, the rotation rate limit strategy is adopted to limit the rotation speed of the drone, making the turning process smoother and more controllable, and avoiding sudden movements.

[0096] Set the current heading angle θ of the drone current ;

[0097] Set the heading angle θ that the drone needs to turn to target ;

[0098] Calculate the angle difference Δθ = mod(θ target - θcurrent +180,360)-180;

[0099] Define a maximum rotation angular velocity ω max ;

[0100] In each control cycle, the angle increment Δθ that the drone should turn to is calculated increment =sign(Δθ)*ω max *T s , T s is the sampling time of the control loop;

[0101] Update the current heading angle θ of the drone current =θ current +θ increment ;

[0102] If Δθ>Δθ increment , repeatedly calculate the angle difference to update the current heading angle of the drone.

[0103] Embodiment 2

[0104] A return tracking system for a UAV applied to a mobile airport, comprising:

[0105] Data acquisition module, used to obtain the location and motion parameters of mobile airports and drones;

[0106] A dynamic estimation module is used to estimate the state of the mobile airport based on the position and motion parameters of the mobile airport using a Kalman filter dynamic model to obtain an estimated prediction value;

[0107] A motion calculation module, used to compare the estimated prediction value with the current position and motion parameters of the UAV to determine the motion direction and speed of the UAV;

[0108] The return tracking module is used to determine whether the drone's flight time supports the drone to track to the mobile airport in the determined direction and speed of movement. If so, the determined direction and speed of movement are used as control instructions to control the movement of the drone; if not, an intelligent landing strategy is adopted to select the landing point and form a control instruction.

[0109] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0110] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0111] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0112] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0113] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made by those skilled in the art within the spirit and principle of the present invention without creative labor shall be included in the protection scope of the present invention.

Claims

1. A method for tracking the return of a drone at a mobile airport, characterized in that: The following steps are involved: Get the location and motion parameters of mobile airports and drones; Using the Kalman filter dynamic model, the state of the mobile airport is estimated based on the location and motion parameters of the mobile airport to obtain an estimated prediction value; Comparing the estimated prediction value with the current position and motion parameters of the UAV to determine the motion direction and motion speed of the UAV; Determine whether the drone's flight time supports the drone to track the mobile airport according to the determined movement direction and movement speed. If so, use the determined movement direction and movement speed as control instructions to control the movement of the drone. If not, adopt an intelligent landing strategy, select a landing point, and generate control instructions.

2. A method for tracking the return of a drone at a mobile airport as claimed in claim 1, characterized in that: The motion parameters of the mobile airport include the acceleration components of the mobile airport along three orthogonal axes, the absolute direction of the mobile airport and the current speed of the mobile airport. The motion parameters of the drone include the acceleration and speed in the horizontal and vertical directions.

3. The method for tracking the return of a drone at a mobile airport as claimed in claim 1, characterized in that: Using the Kalman filter dynamic model, the process of estimating the state of a mobile airport based on its position and motion parameters includes: Set the initial state estimate and initial covariance matrix; At each time step, the position, velocity and acceleration of the mobile airport at the current time step are formed into a state vector as the observation equation; Based on the observation equation, the Kalman gain is calculated, the state estimate and covariance matrix are updated, and the estimated prediction value is obtained.

4. The method for tracking the return of a drone at a mobile airport as claimed in claim 1, characterized in that: The process of comparing the estimated prediction value with the current position and motion parameters of the UAV to determine the motion direction and motion speed of the UAV includes: The absolute direction of the mobile airport relative to the drone is calculated by combining the estimated predicted value of the mobile airport coordinates with the current position coordinates of the drone. The velocity component of the mobile airport in the absolute direction relative to the drone is calculated by combining the horizontal and vertical components of the current velocity of the mobile airport to determine the velocity value of the drone.

5. The method for tracking the return of a UAV at a mobile airport as claimed in claim 1, characterized in that: The process of determining whether the drone's flight time supports the drone's pursuit of the mobile airport in a determined direction and speed includes: If the velocity component v of the mobile airport in the absolute direction relative to the drone is greater than the maximum speed Vmax of the drone, the drone cannot catch up with the mobile airport and needs to reduce the moving speed of the mobile airport. If the velocity component v of the mobile airport in the absolute direction relative to the drone is less than the maximum speed Vmax of the drone, the time it takes for the drone to catch up with the mobile airport is calculated based on the difference between the two and the distance between the mobile airport and the drone, and the drone's endurance time is judged based on the time.

6. The method for tracking the return of a UAV at a mobile airport as claimed in claim 1 is characterized in that: The intelligent landing strategy selects the landing point and forms the control command process, including: the mobile airport obtains the UAV location information and environmental information, takes the current coordinate point of the UAV as the center, and searches outward for a suitable landing point. After removing the points that do not meet the landing conditions, the UAV selects the nearest landing point to land.

7. The method for tracking the return of a UAV at a mobile airport as claimed in claim 1, characterized in that: In the process of controlling the movement of the UAV by using the determined movement direction and movement speed as control instructions, if the difference between the heading of the new path planning formed in the control instruction and the original flight path exceeds the preset value, the rotation rate limiting strategy is adopted to limit the rotation speed of the UAV, so that the UAV steering process is smooth and controllable.

8. The method for tracking the return of a UAV at a mobile airport as claimed in claim 7, characterized in that: The process of limiting the speed of the drone's rotation using the rotation rate limit strategy includes: Set the current heading angle of the drone θ current ; Set the heading angle θ that the drone needs to turn to target ; Calculate the angle difference Δθ = mod(θ target -θ current +180,360)-180; Define the maximum rotational angular velocity ω max ; In each control cycle, the angle increment Δθ that the drone should turn to is calculated increment =sign(Δθ)*ω max *T s , T s is the sampling time of the control loop; Update the current heading angle θ of the drone current =θ current +θ increment ; If Δθ>Δθ increment , repeatedly calculate the angle difference to update the current heading angle of the drone.

9. A return tracking system for a UAV used in a mobile airport, characterized in that: include: Data acquisition module, used to obtain the location and motion parameters of mobile airports and drones; A dynamic estimation module is used to estimate the state of the mobile airport based on the position and motion parameters of the mobile airport using a Kalman filter dynamic model to obtain an estimated prediction value; A motion calculation module, used to compare the estimated prediction value with the current position and motion parameters of the UAV to determine the motion direction and speed of the UAV; The return tracking module is used to determine whether the drone's flight time supports the drone to track to the mobile airport according to the determined movement direction and movement speed. If so, the determined movement direction and movement speed are used as control instructions to control the movement of the drone. If not, adopt an intelligent landing strategy, select a landing point, and generate control instructions.

10. An electronic device, characterized in that: The method comprises a memory and a processor, and computer instructions stored in the memory and executed on the processor, wherein when the computer instructions are executed by the processor, the steps in the method according to any one of claims 1 to 8 are completed.

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