Low-altitude unmanned aerial vehicle autonomous obstacle avoidance path planning system and control method

Abstract

The invention relates to the field of unmanned aerial vehicles, in particular to a low-altitude unmanned aerial vehicle autonomous obstacle avoidance path planning system and a control method.The low-altitude unmanned aerial vehicle autonomous obstacle avoidance path planning system comprises a data acquisition module, a data processing module, a path planning module, a control module and an unmanned aerial vehicle; the environment data comprises optical data and magnetic field data, the data processing module is used for processing the data acquired by the data acquisition module and obtaining a distribution diagram of obstacles around the unmanned aerial vehicle, and the path planning module is used for adjusting a moving path of the unmanned aerial vehicle according to a processing result of the data processing module. And the control module is used for controlling the unmanned aerial vehicle according to the moving path acquired by the path planning module. According to the scheme, the obstacle is detected by introducing the geomagnetic field, detection errors caused by traditional optical detection equipment can be made up, meanwhile, the obstacle is detected through the magnetic field, and the obstacle shielded by the visual field can be detected.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of unmanned aerial vehicles, in particular to a low-altitude unmanned aerial vehicle autonomous obstacle avoidance path planning system and control method. BACKGROUND

[0002] With the rapid development of unmanned aerial vehicle technology, low-altitude unmanned aerial vehicles have been widely used in agricultural inspection, power line inspection, geological exploration, urban security, emergency rescue and other fields. Therefore, unmanned aerial vehicle autonomous obstacle avoidance and path planning technology has become a research hotspot.

[0003] The prior art such as CN119440080A discloses a low-altitude obstacle avoidance system for unmanned aerial vehicles, which belongs to the technical field of control systems. The low-altitude obstacle avoidance system for unmanned aerial vehicles comprises a multi-sensor fusion module, a data processing module, an obstacle avoidance decision module and a control execution module. The multi-sensor fusion module comprises a plurality of sensors for collecting environmental information. The data processing module is used for real-time analysis and processing of the data collected by the multi-sensor fusion module to generate an obstacle identification result. The obstacle avoidance decision module is used for generating an optimal obstacle avoidance path based on the obstacle identification result and the flight state of the unmanned aerial vehicle. The control execution module is used for generating control commands based on the optimal obstacle avoidance path to control the flight of the unmanned aerial vehicle.

[0004] At present, the existing unmanned aerial vehicles generally perform obstacle avoidance according to the images captured by optical sensors. In strong electromagnetic field areas such as transformer substations and high-voltage lines, such sensors are easily disturbed and fail. In order to solve the problems existing in the field, the present application is made. SUMMARY

[0005] The present application aims to overcome the existing problems and provides a low-altitude unmanned aerial vehicle autonomous obstacle avoidance path planning system and control method.

[0006] In order to overcome the shortcomings of the prior art, the present application adopts the following technical solutions:

[0007] A low-altitude unmanned aerial vehicle autonomous obstacle avoidance path planning system comprises a data acquisition module, a data processing module, a path planning module, a control module and an unmanned aerial vehicle. The data acquisition module is used for collecting environmental data around the unmanned aerial vehicle, and the environmental data comprises optical data and magnetic field data. The data processing module is used for processing the collected data of the data acquisition module and obtaining an obstacle distribution map around the unmanned aerial vehicle. The path planning module is used for adjusting the movement path of the unmanned aerial vehicle according to the processing result of the data processing module. The control module is used for controlling the unmanned aerial vehicle according to the movement path obtained by the path planning module.

[0008] Further, the data acquisition module comprises an optical sensor, a magnetic field sensor and a filter, the optical sensor is used to acquire optical data of the environment around the unmanned aerial vehicle, the magnetic field sensor is used to detect magnetic field data of the environment around the unmanned aerial vehicle, the magnetic field data comprises environmental magnetic field data and geomagnetic field data, and the filter is used to filter the magnetic field data detected by the magnetic field sensor, so as to eliminate signals related to the environmental magnetic field data.

[0009] Further, the data processing module comprises a communication unit, a 3D map modeling unit and an obstacle identification unit, the communication unit is used to communicate with the satellite, the 3D map modeling unit is used to generate a 3D map of the position near the unmanned aerial vehicle according to the communication data of the communication unit with the satellite and generate a magnetic field distribution map according to the magnetic field data, the obstacle identification unit comprises an image recognition unit, a marking unit and a calculation unit, the image recognition unit is used to identify obstacles in the environment around the unmanned aerial vehicle according to the optical data, the calculation unit is used to obtain boundary points of the obstacles according to the magnetic field data, so as to identify the obstacles in the environment around the unmanned aerial vehicle, and the marking unit is used to mark the positions of the obstacles obtained by the image recognition unit and the positions of the obstacles obtained by the calculation unit on the 3D map, so as to obtain an obstacle distribution map.

[0010] Further, the path planning module comprises a storage unit and an obstacle avoidance unit, the storage unit is used to save the set initial flight path of the unmanned aerial vehicle, and the obstacle avoidance unit stores an obstacle avoidance algorithm, the obstacle avoidance unit is used to adjust the flight path of the unmanned aerial vehicle according to the positions of the obstacles marked on the 3D map, so as to realize obstacle avoidance.

[0011] Further, the control module comprises a posture control unit and a position control unit, the posture control unit is used to control the pitch angle, roll angle and yaw angle of the unmanned aerial vehicle according to the path generated by the path planning module, and the position control unit is used to control the flight position of the unmanned aerial vehicle according to the path generated by the path planning module and the positioning of the unmanned aerial vehicle.

[0012] A control method of a low-altitude unmanned aerial vehicle autonomous obstacle avoidance path planning system is applied to the low-altitude unmanned aerial vehicle autonomous obstacle avoidance path planning system, and comprises the following steps:

[0013] S1, the data acquisition module acquires environmental data around the unmanned aerial vehicle;

[0014] S2, the data processing module processes the collected data of the data acquisition module and obtains an obstacle distribution map around the unmanned aerial vehicle;

[0015] S3, the path planning module adjusts the flight path of the unmanned aerial vehicle according to the initial flight path of the unmanned aerial vehicle and the obstacle distribution;

[0016] S4, the control module controls the flight of the unmanned aerial vehicle according to the path acquired by the path planning module.

[0017] Further, the data processing module processes the data acquired by the data acquisition module and acquires the obstacle distribution map around the unmanned aerial vehicle, and the steps include:

[0018] S21, the communication unit is used for communicating with the satellite;

[0019] S22, the 3D map modeling unit generates the 3D map of the position near the unmanned aerial vehicle according to the communication data of the communication unit and the satellite, and generates the magnetic field distribution map according to the magnetic field data;

[0020] S23, the image recognition unit recognizes the obstacles in the environment around the unmanned aerial vehicle according to the optical data acquired by the data acquisition module;

[0021] S24, the calculation unit calculates the position of the boundary point of the obstacles in the environment around the unmanned aerial vehicle according to the magnetic field data acquired by the data acquisition module, and further recognizes the obstacles in the environment around the unmanned aerial vehicle;

[0022] S25, the marking unit marks the position of the obstacles acquired by the image recognition unit and the position of the obstacles acquired by the calculation unit on the 3D map

[0023] The beneficial effects obtained by the present application are: 1. By introducing the geomagnetic field to detect obstacles, it is beneficial to make up for the detection error caused by the traditional optical detection equipment, and at the same time, by detecting the obstacles through the magnetic field, compared with the ordinary optical detection, it is beneficial to detect the obstacles blocked by the field of view, and can predict the subsequent obstacle situation in advance.

[0024] 2. By introducing the electronic noise gradient to acquire the magnetic field gradient parameter, it is beneficial to introduce specific error value in the calculation to judge the boundary point of the obstacle, and improve the accuracy of the judgment. BRIEF DESCRIPTION OF DRAWINGS

[0025] The present application can be further understood from the following description in conjunction with the accompanying drawings. The components in the drawings are not necessarily drawn to scale, but emphasis is placed on showing the principles of the embodiments. In different views, the same reference numerals designate the same parts.

[0026] Figure 1 The present application is a structural schematic diagram.

[0027] Figure 2 The present application is a workflow diagram.

[0028] Figure 3 The present application is a flowchart of the data processing module processing the data acquired by the data acquisition module and acquiring the obstacle distribution map around the unmanned aerial vehicle.

[0029] Figure 4 Figure 1 is a diagram of the relationship between the obstacle evaluation index of the present application and the image similarity. DETAILED DESCRIPTION

[0030] The following is a detailed description of the embodiments of the present application, and those skilled in the art can understand the advantages and effects of the present application from the disclosed content. The present application can be implemented or applied by other different embodiments, and various modifications and changes can be made to the details in the specification based on different views and applications without departing from the spirit of the present application. In addition, the drawings of the present application are only simple schematic illustrations and are not drawn according to the actual size, and it is declared in advance. The following embodiments will further illustrate the related technical content of the present application, but the disclosed content is not used to limit the protection scope of the present application.

[0031] Embodiment one: according to Figure 1 , Figure 2 , Figure 3 and Figure 4 , the embodiment provides a low-altitude unmanned aerial vehicle autonomous obstacle avoidance path planning system, characterized in that it comprises a data acquisition module, a data processing module, a path planning module, a control module and an unmanned aerial vehicle. The data acquisition module is used to acquire environmental data around the unmanned aerial vehicle, the environmental data comprising optical data and magnetic field data. The data processing module is used to process the collected data of the data acquisition module and obtain an obstacle distribution map around the unmanned aerial vehicle. The path planning module is used to adjust the moving path of the unmanned aerial vehicle according to the processing result of the data processing module. The control module is used to control the unmanned aerial vehicle according to the moving path obtained by the path planning module.

[0032] Further, the data acquisition module comprises an optical sensor, a magnetic field sensor and a filter. The optical sensor is used to acquire optical data of the environment around the unmanned aerial vehicle. The magnetic field sensor is used to detect magnetic field data of the environment around the unmanned aerial vehicle. The magnetic field data comprises environmental magnetic field data and geomagnetic field data. The filter is used to filter the magnetic field data detected by the magnetic field sensor, so as to eliminate the signals related to the environmental magnetic field data.

[0033] Specifically, the environmental magnetic field refers to the magnetic field generated by strong electromagnetic field regions such as transformer substations and high-voltage lines.

[0034] Specifically, the optical sensor can be an RGB-D camera, the magnetic field sensor can be a three-axis magnetometer, and the filter is used to filter the signal with a frequency of 50Hz in the data collected by the magnetic field sensor. In general, 50Hz power frequency alternating field will be generated in areas such as substations and high-voltage lines. By filtering the signal at this frequency, the environmental magnetic field data generated by substations and high-voltage lines is avoided to affect the data processing module in identifying obstacles based on geomagnetic field data.

[0035] Further, the data processing module includes a communication unit, a 3D map modeling unit, and an obstacle recognition unit. The communication unit is used to communicate with the satellite, the 3D map modeling unit is used to generate a 3D map of the position near the unmanned aerial vehicle according to the communication data of the communication unit with the satellite and generate a magnetic field distribution map according to the magnetic field data. The obstacle recognition unit includes an image recognition unit, a marking unit, and a calculation unit. The image recognition unit is used to identify the obstacles in the environment around the unmanned aerial vehicle according to the optical data. The calculation unit is used to obtain the boundary points of the obstacles according to the magnetic field data, and further identify the obstacles in the environment around the unmanned aerial vehicle. The marking unit is used to mark the positions of the obstacles obtained by the image recognition unit and the positions of the obstacles obtained by the calculation unit on the 3D map to obtain an obstacle distribution map.

[0036] Further, the path planning module includes a storage unit and an obstacle avoidance unit. The storage unit is used to save the set initial flight path of the unmanned aerial vehicle. The obstacle avoidance unit stores an obstacle avoidance algorithm. The obstacle avoidance unit is used to adjust the flight path of the unmanned aerial vehicle according to the marked obstacle positions on the 3D map to achieve obstacle avoidance.

[0037] Further, the control module includes a posture control unit and a position control unit. The posture control unit is used to control the pitch angle, roll angle, and yaw angle of the unmanned aerial vehicle according to the path generated by the path planning module. The position control unit is used to control the flight position of the unmanned aerial vehicle according to the path generated by the path planning module and the positioning of the unmanned aerial vehicle.

[0038] Specifically, the posture control unit realizes its functions through various posture control algorithms.

[0039] A control method of a low-altitude unmanned aerial vehicle autonomous obstacle avoidance path planning system is applied to a low-altitude unmanned aerial vehicle autonomous obstacle avoidance path planning system, including the following steps:

[0040] S1, the data acquisition module acquires environmental data around the unmanned aerial vehicle;

[0041] S2, the data processing module processes the collected data of the data acquisition module and obtains an obstacle distribution map around the unmanned aerial vehicle;

[0042] S3, the path planning module adjusts the flight path of the unmanned aerial vehicle according to the initial flight path of the unmanned aerial vehicle and the obstacle distribution;

[0043] S4, the control module controls the flight of the unmanned aerial vehicle according to the path obtained by the path planning module.

[0044] Further, the data processing module processes the data collected by the data collection module and obtains the obstacle distribution map around the unmanned aerial vehicle, including the following steps:

[0045] S21, the communication unit is used for communication with the satellite;

[0046] S22, the 3D map modeling unit generates a 3D map of the position near the unmanned aerial vehicle according to the communication data of the communication unit with the satellite, and generates a magnetic field distribution map according to the magnetic field data;

[0047] S23, the image recognition unit identifies the obstacles in the environment around the unmanned aerial vehicle according to the optical data collected by the data collection module;

[0048] S24, the calculation unit calculates the position of the boundary point of the obstacles in the environment around the unmanned aerial vehicle according to the magnetic field data collected by the data collection module, and further identifies the obstacles in the environment around the unmanned aerial vehicle;

[0049] S25, the marking unit marks the positions of the obstacles obtained by the image recognition unit and the positions of the obstacles obtained by the calculation unit on the 3D map.

[0050] Specifically, the calculation unit determines whether a point in the magnetic field distribution map is a boundary point according to the following formula:

[0051]

[0052] wherein BOUNDARY is a boundary point judgment index, when BOUNDARY is greater than or equal to 1, it is considered that a certain point is a boundary point, is a magnetic field gradient parameter, is the magnetic field gradient module of the point, and the coordinates of the point are (x, y, z), is the rate of change of the X-direction magnetic field component Bx along the x-axis, is the rate of change of the Y-direction magnetic field component By along the y-axis, is the rate of change of the Z-direction magnetic field component Bz along the z-axis, is used to calculate the magnetic field gradient module of the point, and Δθ is the magnetic field vector deflection angle, B obs is the magnetic field vector of the point, B ref is the magnetic field vector of the background point, and the background point is the point with the minimum magnetic field intensity in the magnetic field distribution map, B obs *B ref is the calculation of the dot product of the vector, |Bobs |For B obs The model, |B ref |For B ref The model, Used to calculate the deflection angle of the magnetic field vector, BSET is the reference value for the magnetic field gradient, when the magnetic field gradient parameter... When the value is greater than this, the point is considered to be very likely to be the boundary of an obstacle. θSET is a reference value for the deflection angle of the magnetic field vector. When the deflection angle of the magnetic field vector is greater than this value, the point is considered to be very likely to be the boundary of an obstacle. BSET and θSET are set by those skilled in the art based on experience. One possible setting is BSET to 0.5 uT / m and θSET to 10°.

[0053] SN represents the power spectral density of external noise detected by the magnetic field sensor, and SB represents the power spectral density of the magnetic field detected by the magnetic field sensor. This can be achieved by setting... Thus, the greater the external noise right The smaller the contribution, the more e becomes, which is the natural constant. This is the electronic noise gradient, where the electronic noise is generated by the movement of electrons inside the magnetic field sensor, and is determined by setting... This results in a larger ratio between the electronic noise and the magnetic field gradient magnitude at that point. right The smaller the contribution, the less ZS stan The electronic noise limit is used to characterize the amount of noise contained in a signal at a square root of hertz (in units of 1000 Hz). The limit is obtained by taking multiple measurements under zero input magnetic field and statistically analyzing the standard deviation of the magnetic field readings per square root of Hertz. L is the spatial resolution of the magnetic field sensor (in meters), and Δt is the signal integration time (in seconds). The spatial resolution and signal integration time are determined by the inherent performance of the magnetic field sensor.

[0054] Specifically, on both sides of the line connecting the boundary points, the side with the stronger magnetic field was detected as an obstacle.

[0055] In existing technologies, drones generally rely solely on image recognition for obstacle avoidance. However, in areas with strong electromagnetic fields such as substations and high-voltage lines, traditional optical detection equipment is prone to image distortion or excessive noise, which is detrimental to drone obstacle avoidance. This solution introduces the Earth's magnetic field to detect obstacles. First, it filters out errors caused by the environmental magnetic field. Then, it uses the different magnetic field gradients on different obstacles to detect obstacle boundaries, which helps compensate for the detection errors of traditional optical detection equipment. Furthermore, using magnetic fields to detect obstacles is more effective than ordinary optical detection in detecting obstacles obstructed by the field of view, allowing for early prediction of future obstacle situations. Moreover, this solution incorporates electronic noise gradients during the calculation process. to obtain the magnetic field gradient parameter The noise caused by the electron itself in the zero magnetic field is considered to adjust the obtained magnetic field gradient module to obtain the magnetic field gradient parameter Compared with the prior art which only reduces errors by single zero-point calibration, the specific error value is introduced in the calculation to determine the boundary point of the obstacle, and the accuracy of the determination is improved.

[0056] The beneficial effects of the scheme are: 1. The geomagnetic field is introduced to detect the obstacle, which is helpful to make up for the detection error caused by the traditional optical detection device. At the same time, compared with ordinary optical detection, the obstacle that is blocked by the field of view can be detected by the magnetic field, and the subsequent obstacle situation can be predicted in advance.

[0057] 2. The electronic noise gradient is introduced to obtain the magnetic field gradient parameter, which is helpful to introduce the specific error value in the calculation to determine the boundary point of the obstacle, and the accuracy of the determination is improved.

[0058] Embodiment two: this embodiment should be understood as including all the features of any one of the preceding embodiments, and further improving on the basis thereof, and further comprising a method for screening the obstacle recognized by the image recognition unit. In embodiment one, the obstacles in a wide range are recognized by combining image recognition and magnetic field detection. For these obstacles, the risk encountered by the unmanned aerial vehicle can be reduced by taking the obstacle avoidance mode as much as possible. However, in a narrow terrain, it is sometimes difficult to avoid obstacles as much as possible. If only the obstacles detected by the magnetic field are relied on at this time, it may also be impossible to completely avoid obstacles (this is because small animals may not be able to reach the Or Or both are greater than or equal to 1), so the results of image recognition need to be selected according to the results of magnetic field detection. For a certain coordinate, if the results of magnetic field detection and image recognition are both obstacles or only the results of magnetic field detection are obstacles, the point needs to be avoided; if both are not obstacles, the point does not need to be avoided; if only the results of image recognition are obstacles, the point may be the case of small animals mentioned above, at this time, whether the point P is an obstacle is determined according to the following formula:

[0059]

[0060] ZAW is an obstacle evaluation index, B P is the magnetic field gradient parameter of the point P (obtained in the manner of reference embodiment one), θ PLet P be the magnetic field vector deflection angle at point P, BREF be the magnetic field gradient parameter of the background point (the point with the weakest magnetic field in the magnetic field distribution map), θREF be the magnetic field vector deflection angle of the background point, e be the natural constant, fc be the Laplacian variance of the recognized image by the image recognition unit (a larger variance indicates a clearer image), FC be the Laplacian variance of the image with the highest similarity to the recognized image among images taken by the UAV under conditions without an ambient magnetic field, and SSIM be its corresponding SSIM value (obtainable through existing technology); a larger SSIM value indicates a higher image similarity. This can be achieved by setting... Used based on magnetic field data ( and The mean value (of points P) reflects the base probability that point P is an obstacle. The greater the difference from the background point, the higher the probability that it is an obstacle. This can be achieved by setting... This is used to reflect the reliability of image recognition results based on image quality, by setting... This is helpful in judging image quality based on the difference between the recognized image and the image taken under no magnetic field conditions, and in controlling its contribution to ZAW based on the SSIM value. The square root sign is used to avoid the data being too extreme. Since point P is a non-obstacle according to the magnetic field detection result, its B... P and θ P Both are relatively small (B) P Less than 0.5 uT / m, θ P Less than 10°) and slightly greater than the corresponding BREF and θREF, therefore Slightly greater than 1, and The value of ZAW is between 0 and 1, so that when ZAW is greater than 1, the point is considered an obstacle, and vice versa.

[0061] like Figure 4 As shown, Figure 4 Assumption The value is 4, and for The relationship between obstacle evaluation indicators and image similarity is shown in the figure.

[0062] The beneficial effects of this embodiment are: combining magnetic field detection and image recognition to determine whether a point is an obstacle, and further judging the image recognition results, which is beneficial for accurately identifying obstacles in narrow terrain areas and avoiding situations where drones have nowhere to go.

[0063] The above disclosed is only the preferred feasible embodiment of the present application, and does not limit the protection scope of the present application, so any equivalent technical change made by applying the content of the present application specification and drawings is included in the protection scope of the present application, and furthermore, the elements can be updated as the technology develops. The above units are only an example, and the corresponding units can be used in different designs according to actual needs when the present solution is implemented by the person skilled in the art.

Claims

1. A low-altitude unmanned aerial vehicle autonomous obstacle avoidance path planning system, characterized in that, The application relates to an unmanned aerial vehicle (UAV) control system, which comprises a data acquisition module, a data processing module, a path planning module, a control module and the UAV, wherein the data acquisition module is used for collecting environmental data around the UAV, the environmental data comprises optical data and magnetic field data, the data processing module is used for processing the collected data of the data acquisition module and obtaining an obstacle distribution map around the UAV, the path planning module is used for adjusting the moving path of the UAV according to the processing result of the data processing module, and the control module is used for controlling the UAV according to the moving path obtained by the path planning module. 2.The low-altitude unmanned aerial vehicle autonomous obstacle avoidance path planning system according to claim 1, characterized in that, The data acquisition module comprises an optical sensor, a magnetic field sensor and a filter, the optical sensor is used for collecting optical data of the environment around the UAV, the magnetic field sensor is used for detecting magnetic field data of the environment around the UAV, the magnetic field data comprises environmental magnetic field data and geomagnetic field data, and the filter is used for filtering the magnetic field data detected by the magnetic field sensor so as to eliminate the signals related to the environmental magnetic field data. 3.The low-altitude unmanned aerial vehicle autonomous obstacle avoidance path planning system according to claim 1, characterized in that, The data processing module comprises a communication unit, a 3D map modeling unit and an obstacle identification unit, the communication unit is used for communicating with a satellite, the 3D map modeling unit is used for generating a 3D map of the position near the UAV according to the communication data of the communication unit and the satellite and generating a magnetic field distribution map according to the magnetic field data, the obstacle identification unit comprises an image identification unit, a marking unit and a calculation unit, the image identification unit is used for identifying the obstacles of the environment around the UAV according to the optical data, the calculation unit is used for obtaining the boundary points of the obstacles according to the magnetic field data, thereby identifying the obstacles of the environment around the UAV, and the marking unit is used for marking the obstacle positions obtained by the image identification unit and the obstacle positions obtained by the calculation unit on the 3D map, thereby obtaining the obstacle distribution map.

4. The low-altitude unmanned aerial vehicle autonomous obstacle avoidance path planning system according to claim 1, wherein, The path planning module comprises a storage unit and an obstacle avoidance unit, the storage unit is used for storing the set initial flight path of the UAV, the obstacle avoidance unit stores an obstacle avoidance algorithm, and the obstacle avoidance unit is used for adjusting the flight path of the UAV according to the marked obstacle positions on the 3D map, thereby realizing obstacle avoidance.

5. The low-altitude unmanned aerial vehicle autonomous obstacle avoidance path planning system according to claim 1, wherein The control module comprises a posture control unit and a position control unit, the posture control unit is used for controlling the pitch angle, the roll angle and the yaw angle of the UAV according to the path generated by the path planning module, and the position control unit is used for controlling the flight position of the UAV according to the path generated by the path planning module and the positioning of the UAV.

6. The control method of the low-altitude unmanned aerial vehicle autonomous obstacle avoidance path planning system is applied to the low-altitude unmanned aerial vehicle autonomous obstacle avoidance path planning system of claim 1, characterized in that, The application further discloses an unmanned aerial vehicle control method, which comprises the following steps: S1, collecting environmental data around the UAV by the data acquisition module; S2, processing the collected data of the data acquisition module by the data processing module and obtaining an obstacle distribution map around the UAV; S3, adjusting the flight path of the UAV according to the initial flight path of the UAV and the obstacle distribution by the path planning module; S4, controlling the flight of the UAV according to the path obtained by the path planning module.

7. The control method of claim 6, wherein, The step of processing the collected data of the data acquisition module by the data processing module and obtaining an obstacle distribution map around the UAV comprises the following steps: S21, the communication unit is used for communication with the satellite; S22, the 3D map modeling unit generates a 3D map of the position near the unmanned aerial vehicle according to the communication data of the communication unit with the satellite and generates a magnetic field distribution map according to the magnetic field data; S23, the image recognition unit recognizes the obstacles in the environment around the unmanned aerial vehicle according to the optical data collected by the data collection module; S24, the calculation unit calculates the position of the boundary point of the obstacles in the environment around the unmanned aerial vehicle according to the magnetic field data collected by the data collection module, and further recognizes the obstacles in the environment around the unmanned aerial vehicle; S25, the marking unit marks the position of the obstacles obtained by the image recognition unit and the position of the obstacles obtained by the calculation unit on the 3D map.

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