A method and system for autonomous take-off and landing of unmanned aerial vehicles based on BeiDou satellite collaborative control

Abstract

This invention provides a method and system for autonomous take-off and landing of unmanned aerial vehicles (UAVs) based on BeiDou satellite collaborative control. The method includes: constructing an intelligent UAV nest near a BeiDou reference station, sharing the reference station's power supply, network communication, security, and other infrastructure to enhance the intelligent nest's basic support capabilities. Utilizing the characteristic of BeiDou relative positioning technology that higher positioning accuracy and reliability occur with shorter distances between the reference station and the terminal, this invention solves the problem of high-precision, high-reliability, and high-trustworthiness positioning during UAV take-off and landing. Combined with the sensing capabilities of a high-definition camera, it intelligently identifies docking platforms and obstacles, improving the UAV's autonomous navigation capabilities, precise positioning capabilities, and obstacle avoidance capabilities in complex environments. During autonomous take-off and landing, the intelligent nest provides high-quality communication support for the UAV, ensuring information synchronization and real-time transmission of control commands, thus guaranteeing safe take-off and landing.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) technology, and in particular to a method and system for autonomous take-off and landing of UAVs based on BeiDou satellite collaborative control. Background Technology

[0002] A smart drone nest is an intelligent infrastructure that provides drones with services such as docking, charging, communication, control, and maintenance. Essentially, it's a remote, precise take-off and landing platform for drones, a stable "home" capable of withstanding severe weather such as strong winds and heavy rain. Autonomous take-off and landing is a crucial step for drones to achieve automated operations using smart nests. To ensure safe take-off and landing, highly reliable centimeter-level or even millimeter-level positioning accuracy is required to precisely control the drone's flight attitude and avoid collisions.

[0003] Existing high-precision navigation and positioning methods for UAVs include: (1) Precise Point Positioning (PPP) technology, which can achieve centimeter-level to decimeter-level positioning without relying on a large number of Beidou reference stations. However, its convergence time is relatively long, and its positioning accuracy is low and unstable in highly dynamic environments, making it difficult to meet the transient precision control requirements of UAVs during take-off and landing. (2) Real-Time Kinematic (RTK) technology, which constructs a regional error model by deploying multiple Beidou reference stations in and around the operating area, and transmits error correction information to the UAV through 4G, 5G networks or satellite communication to achieve centimeter-level positioning, which is sufficient to meet the autonomous navigation and control requirements of UAVs during flight. However, when the distance between the smart UAV nest and the Beidou reference station is far, the spatial correlation of the error between the two will be significantly reduced, resulting in a decrease in the accuracy, reliability and dependability of the network RTK positioning, making it difficult to guarantee the precision take-off and landing of UAVs. Summary of the Invention

[0004] This invention provides a method and system for autonomous take-off and landing of unmanned aerial vehicles (UAVs) based on BeiDou satellite collaborative control, which solves the shortcomings of low control accuracy for UAV take-off and landing in the prior art and realizes precise take-off and landing of UAVs in intelligent UAV nests.

[0005] In a first aspect, the present invention provides a method for autonomous take-off and landing of unmanned aerial vehicles (UAVs) based on BeiDou satellite collaborative control, comprising:

[0006] Receive data from BeiDou reference stations and preprocess the data to obtain preprocessed BeiDou reference station data;

[0007] The real-time spatial position of the UAV is obtained by solving the preprocessed BeiDou reference station data using the relative positioning mode.

[0008] The real-time spatial position is transformed from the BeiDou positioning coordinate system to the carrier coordinate system to obtain the carrier coordinate position of the UAV;

[0009] By combining BeiDou positioning information and IMU attitude information, the pose information of the UAV can be obtained;

[0010] By integrating BeiDou positioning information, IMU attitude information, and visual navigation information, the UAV pose information is optimized to obtain the optimized UAV pose information.

[0011] By utilizing the optimized UAV pose information, combined with the intelligent UAV take-off and landing platform and the BeiDou reference station, coordinated take-off and landing control of UAVs can be achieved.

[0012] According to the present invention, an autonomous take-off and landing method for unmanned aerial vehicles based on BeiDou satellite cooperative control is provided, which receives BeiDou reference station data and preprocesses the BeiDou reference station data to obtain preprocessed BeiDou reference station data, including:

[0013] The communication function of the intelligent drone nest is adopted, and the drone data acquisition module receives and decodes the Beidou reference station data sent by the Beidou reference station in real time.

[0014] The BeiDou reference station data is subjected to real-time gross error detection and removal, real-time cycle slip detection and repair, and real-time data quality analysis in sequence to obtain the preprocessed BeiDou reference station data.

[0015] According to the present invention, an autonomous take-off and landing method for unmanned aerial vehicles (UAVs) based on BeiDou satellite cooperative control is provided. This method uses a relative positioning mode to solve for the preprocessed BeiDou reference station data to obtain the real-time spatial position of the UAV, including:

[0016] Based on the BeiDou relative positioning model, the spatial position of the UAV in the BeiDou positioning coordinate system is obtained in real time using the Kalman filter algorithm. ;

[0017] During the takeoff and landing phase of the UAV, by utilizing the infinitely close distance between the UAV and the BeiDou reference station, the double-difference observation equation is obtained:

[0018]

[0019] In the formula, superscript , The subscript is used to identify the observation satellite p and the reference satellite q. , For the identification of BeiDou reference stations and UAV terminals, For carrier wavelength, For carrier frequency, It is a double difference operator. For integer ambiguity, These are pseudorange observations. For carrier phase observations, The distance from the satellite to the drone, including the drone's spatial location. .

[0020] According to the present invention, an autonomous take-off and landing method for unmanned aerial vehicles (UAVs) based on BeiDou satellite cooperative control is provided, which transforms the real-time spatial position from the BeiDou positioning coordinate system to the carrier coordinate system to obtain the carrier coordinate position of the UAV, including:

[0021]

[0022] In the formula, This refers to the spatial position of the drone in the BeiDou positioning coordinate system. The spatial position of the UAV in the carrier coordinate system. Let be the transformation matrix, and be the known quantities that have been pre-calibrated.

[0023] The present invention provides an autonomous take-off and landing method for unmanned aerial vehicles (UAVs) based on BeiDou satellite cooperative control, which combines BeiDou position information and IMU attitude information to obtain UAV attitude information, including:

[0024] Based on the spatial position of the UAV provided by BeiDou positioning and the attitude information provided by the IMU, the rotation matrix between adjacent epochs is obtained. Translation matrix .

[0025] The present invention provides an autonomous take-off and landing method for unmanned aerial vehicles (UAVs) based on BeiDou satellite cooperative control, which integrates BeiDou position information, IMU attitude information, and visual navigation information to optimize the UAV pose information, resulting in optimized UAV pose information, including:

[0026]

[0027] In the formula, This is the camera's intrinsic parameter matrix, which needs to be calibrated periodically. The spatial position of the target in the camera coordinate system. and The coordinates are in the image plane coordinate system. The depth of the camera is the distance from the target to the camera.

[0028] According to the present invention, an autonomous take-off and landing method for unmanned aerial vehicles (UAVs) based on BeiDou satellite cooperative control is provided. This method utilizes optimized UAV pose information, combined with an intelligent UAV take-off and landing platform and a BeiDou reference station, to achieve cooperative take-off and landing control of the UAV, including:

[0029] It uses a high-definition camera to autonomously perceive the smart nest, identify surrounding obstacles, and determine the relative height of the drone to the smart nest based on SLAM function;

[0030] The relative altitude, along with the positioning information provided by the BeiDou reference station and the known precise location of the intelligent drone nest, is used to coordinate the take-off and landing control of the drone.

[0031] Secondly, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the autonomous take-off and landing method for unmanned aerial vehicles based on BeiDou satellite collaborative control as described above.

[0032] Thirdly, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the autonomous take-off and landing method for unmanned aerial vehicles based on BeiDou satellite cooperative control as described above.

[0033] This invention provides a method and system for autonomous take-off and landing of unmanned aerial vehicles (UAVs) based on BeiDou satellite collaborative control. Through an innovative multi-source sensor fusion architecture, it enables intelligent take-off and landing operations of UAVs in complex environments. By geospatially coupling the intelligent UAV nest with a BeiDou reference station, and sharing power supply systems, network communication facilities, and security protection devices, an integrated support system is constructed. This not only reduces construction and maintenance costs but also significantly improves the environmental adaptability and system reliability of the intelligent nest. Leveraging the close deployment advantage of the BeiDou reference station and the intelligent nest, centimeter-level real-time dynamic positioning is achieved, solving the problems of high-precision, high-reliability, and high-trustworthiness positioning during UAV take-off and landing. Combined with the sensing capabilities of high-definition cameras, it intelligently identifies docking platforms and obstacles, enhancing the UAV's autonomous navigation capabilities, precise positioning capabilities, and obstacle avoidance capabilities in complex environments. During autonomous take-off and landing, the intelligent nest provides high-quality communication support for the UAV, ensuring information synchronization and real-time transmission of control commands, thus guaranteeing safe take-off and landing. Attached Figure Description

[0034] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0035] Figure 1 This is a flowchart illustrating the autonomous take-off and landing method for unmanned aerial vehicles (UAVs) based on BeiDou satellite collaborative control provided by the present invention.

[0036] Figure 2 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0037] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0038] To address the shortcomings of existing technologies, this invention proposes an autonomous take-off and landing method for unmanned aerial vehicles (UAVs) based on the collaborative control of a BeiDou reference station, an intelligent drone nest, and a high-definition camera. This method integrates BeiDou carrier phase observations, visual pose calculation data, and flight control system information from multiple sources to form a navigation enhancement signal with spatiotemporal synchronization characteristics, enabling precise take-off and landing of UAVs in an intelligent drone nest.

[0039] Figure 1 This is a flowchart illustrating the autonomous take-off and landing method for unmanned aerial vehicles (UAVs) based on BeiDou satellite collaborative control provided in this embodiment of the invention. Figure 1 As shown, it includes:

[0040] Step 100: Receive BeiDou reference station data and preprocess the BeiDou reference station data to obtain preprocessed BeiDou reference station data;

[0041] Step 200: Solve the preprocessed BeiDou reference station data using the relative positioning mode to obtain the real-time spatial position of the UAV;

[0042] Step 300: Convert the real-time spatial position from the BeiDou positioning coordinate system to the carrier coordinate system to obtain the carrier coordinate position of the UAV;

[0043] Step 400: Combine BeiDou positioning information and IMU attitude information to obtain UAV pose information;

[0044] Step 500: Integrate BeiDou positioning information, IMU attitude information, and visual navigation information to optimize the UAV pose information and obtain the optimized UAV pose information;

[0045] Step 600: Utilize the optimized UAV pose information, combined with the intelligent UAV take-off and landing platform and the BeiDou reference station, to achieve coordinated take-off and landing control of the UAV.

[0046] Optionally, step 100 includes:

[0047] The communication function of the intelligent drone nest is adopted, and the drone data acquisition module receives and decodes the Beidou reference station data sent by the Beidou reference station in real time.

[0048] The BeiDou reference station data is subjected to real-time gross error detection and removal, real-time cycle slip detection and repair, and real-time data quality analysis in sequence to obtain the preprocessed BeiDou reference station data.

[0049] Specifically, in this embodiment of the invention, the communication function provided by the intelligent drone nest is utilized so that the UAV data acquisition module can receive and decode observation data from the Beidou reference station in real time and carry out data preprocessing, including real-time gross error detection and removal, real-time cycle slip detection and repair, and real-time data quality analysis.

[0050] Among them, the core method for real-time gross error detection and elimination is usually dynamic monitoring based on observed residuals or statistical tests. By establishing a mathematical model of pseudorange and carrier phase observations, the observation residuals are calculated in real time, and outliers are identified using sliding window statistics (such as the 3σ criterion) or hypothesis testing (such as the chi-square test). This approach features real-time processing capabilities for multi-frequency, multi-mode GNSS data, combining pseudorange and phase observations to distinguish between systematic errors such as ionospheric disturbances and multipath effects, and random gross errors. An adaptive threshold adjustment mechanism is typically employed, dynamically setting thresholds based on satellite elevation angle and signal strength to avoid misjudgments in low signal-to-noise ratio or complex environments. Real-time cycle slip detection and repair generally utilize the TurboEdit optimization algorithm and the Score test model. The TurboEdit optimization algorithm introduces a sliding polynomial fitting method to extract the ionospheric delay trend term in the epoch difference model without geometric distance combinations, enhancing sensitivity to small cycle slips (such as 1 cycle). The Score test model separates cycle slips from gross errors by constructing statistical test measures to avoid misjudgments caused by data anomalies. Corresponding repair methods include the minimum norm 1 criterion and the reference satellite method. Key indicators for real-time data quality analysis include monitoring observation environmental factors and data integrity.

[0051] For example, DJI drones and smart drone nests can be used to achieve this. The DJI drone's data acquisition module receives and decodes observation data from the BeiDou reference station in real time, uses the ntrip network transmission protocol, the data format is RTCM3.4, and performs data preprocessing.

[0052] Optionally, step 200 includes:

[0053] Based on the BeiDou relative positioning model, the spatial position of the UAV in the BeiDou positioning coordinate system is obtained in real time using the Kalman filter algorithm. ;

[0054] During the takeoff and landing phase of the UAV, by utilizing the infinitely close distance between the UAV and the BeiDou reference station, the double-difference observation equation is obtained:

[0055]

[0056] In the formula, superscript , The subscript is used to identify the observation satellite p and the reference satellite q. , For the identification of BeiDou reference stations and UAV terminals, For carrier wavelength, For carrier frequency, It is a double difference operator. For integer ambiguity, These are pseudorange observations. For carrier phase observations, The distance from the satellite to the drone, including the drone's spatial location. .

[0057] Optionally, step 300 includes:

[0058]

[0059] In the formula, This refers to the spatial position of the drone in the BeiDou positioning coordinate system. The spatial position of the UAV in the carrier coordinate system. Let be the transformation matrix, and be the known quantities that have been pre-calibrated.

[0060] Optionally, step 400 includes:

[0061] Based on the spatial position of the UAV provided by BeiDou positioning and the attitude information provided by the IMU, the rotation matrix between adjacent epochs is obtained. Translation matrix .

[0062] Understandably, rotation and translation matrices between adjacent epochs are mathematical tools used to describe the transformation relationship of the three-dimensional coordinate system between two consecutive moments (epochs).

[0063] Optionally, step 500 includes:

[0064]

[0065] In the formula, This is the camera's intrinsic parameter matrix, which needs to be calibrated periodically. The spatial position of the target in the camera coordinate system. and The coordinates are in the image plane coordinate system. The depth of the camera is the distance from the target to the camera.

[0066] Optionally, step 600 includes:

[0067] It uses a high-definition camera to autonomously perceive the smart nest, identify surrounding obstacles, and determine the relative height of the drone to the smart nest based on SLAM function;

[0068] The relative altitude, along with the positioning information provided by the BeiDou reference station and the known precise location of the intelligent drone nest, is used to coordinate the take-off and landing control of the drone.

[0069] This invention uses a high-definition camera to autonomously perceive the intelligent drone nest parking platform, automatically identify surrounding obstacles, accurately determine the relative height of the drone to the nest based on SLAM function, and combine the high-precision positioning information provided by the Beidou reference station with the known precise position of the intelligent drone nest to perform autonomous and precise take-off and landing.

[0070] Figure 2 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 2 As shown, the electronic device may include: a processor 210, a communication interface 220, a memory 230, and a communication bus 240, wherein the processor 210, the communication interface 220, and the memory 230 communicate with each other through the communication bus 240. The processor 210 can call logic instructions in the memory 230 to execute a method for autonomous take-off and landing of a UAV based on BeiDou satellite cooperative control. This method includes: receiving data from a BeiDou reference station; preprocessing the BeiDou reference station data to obtain preprocessed BeiDou reference station data; solving the preprocessed BeiDou reference station data using a relative positioning mode to obtain the real-time spatial position of the UAV; converting the real-time spatial position from the BeiDou positioning coordinate system to the carrier coordinate system to obtain the carrier coordinate position of the UAV; combining BeiDou position information and IMU attitude information to obtain UAV pose information; fusing BeiDou position information, IMU attitude information, and visual navigation information to optimize the UAV pose information to obtain optimized UAV pose information; and using the optimized UAV pose information, combined with an intelligent UAV take-off and landing platform and the BeiDou reference station, to achieve cooperative take-off and landing control of the UAV.

[0071] Furthermore, the logical instructions in the aforementioned memory 230 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0072] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the above-described method for autonomous take-off and landing of unmanned aerial vehicles (UAVs) based on BeiDou satellite cooperative control. This method includes: receiving BeiDou reference station data; preprocessing the BeiDou reference station data to obtain preprocessed BeiDou reference station data; solving the preprocessed BeiDou reference station data using a relative positioning mode to obtain the real-time spatial position of the UAV; converting the real-time spatial position from the BeiDou positioning coordinate system to the carrier coordinate system to obtain the carrier coordinate position of the UAV; combining BeiDou position information and IMU attitude information to obtain UAV pose information; fusing BeiDou position information, IMU attitude information, and visual navigation information to optimize the UAV pose information to obtain optimized UAV pose information; and using the optimized UAV pose information, combined with an intelligent UAV take-off and landing platform and a BeiDou reference station, to achieve cooperative take-off and landing control of the UAV.

[0073] In another aspect, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon. When executed by a processor, the computer program implements the above-described method for autonomous take-off and landing of unmanned aerial vehicles (UAVs) based on BeiDou satellite cooperative control. This method includes: receiving data from a BeiDou reference station; preprocessing the BeiDou reference station data to obtain preprocessed BeiDou reference station data; solving the preprocessed BeiDou reference station data using a relative positioning mode to obtain the real-time spatial position of the UAV; converting the real-time spatial position from the BeiDou positioning coordinate system to the carrier coordinate system to obtain the carrier coordinate position of the UAV; combining BeiDou position information and IMU attitude information to obtain UAV pose information; fusing BeiDou position information, IMU attitude information, and visual navigation information to optimize the UAV pose information to obtain optimized UAV pose information; and using the optimized UAV pose information, combined with an intelligent UAV take-off and landing platform and a BeiDou reference station, to achieve cooperative take-off and landing control of the UAV.

[0074] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. 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 modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0075] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0076] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for autonomous take-off and landing of unmanned aerial vehicles (UAVs) based on BeiDou satellite collaborative control, characterized in that, include: Receive data from BeiDou reference stations and preprocess the data to obtain preprocessed BeiDou reference station data; The real-time spatial position of the UAV is obtained by solving the preprocessed BeiDou reference station data using the relative positioning mode. The real-time spatial position is transformed from the BeiDou positioning coordinate system to the carrier coordinate system to obtain the carrier coordinate position of the UAV; By combining BeiDou positioning information and IMU attitude information, the pose information of the UAV can be obtained; By integrating BeiDou positioning information, IMU attitude information, and visual navigation information, the UAV pose information is optimized to obtain the optimized UAV pose information. By utilizing the optimized UAV pose information, combined with the intelligent UAV take-off and landing platform and the BeiDou reference station, coordinated take-off and landing control of UAVs can be achieved. The real-time spatial position of the UAV is obtained by solving the preprocessed BeiDou reference station data using a relative positioning mode, including: Based on the BeiDou relative positioning model, the spatial position of the UAV in the BeiDou positioning coordinate system is obtained in real time using the Kalman filter algorithm. ; During the takeoff and landing phase of the UAV, by utilizing the infinitely close distance between the UAV and the BeiDou reference station, the double-difference observation equation is obtained: In the formula, superscript , The subscript is used to identify the observation satellite p and the reference satellite q. , For the identification of BeiDou reference stations and UAV terminals, For carrier wavelength, It is a double difference operator. For integer ambiguity, These are pseudorange observations. For carrier phase observations, The distance from the satellite to the drone, including the drone's spatial location. .

2. The method for autonomous take-off and landing of unmanned aerial vehicles based on BeiDou satellite collaborative control according to claim 1, characterized in that, Receive data from BeiDou reference stations, and preprocess the BeiDou reference station data to obtain preprocessed BeiDou reference station data, including: The communication function of the intelligent drone nest is adopted, and the drone data acquisition module receives and decodes the Beidou reference station data sent by the Beidou reference station in real time. The BeiDou reference station data is subjected to real-time gross error detection and removal, real-time cycle slip detection and repair, and real-time data quality analysis in sequence to obtain the preprocessed BeiDou reference station data.

3. The method for autonomous take-off and landing of unmanned aerial vehicles based on BeiDou satellite collaborative control according to claim 1, characterized in that, The real-time spatial position is transformed from the BeiDou positioning coordinate system to the carrier coordinate system to obtain the carrier coordinate position of the UAV, including: In the formula, This refers to the spatial position of the drone in the BeiDou positioning coordinate system. The spatial position of the UAV in the carrier coordinate system. Let be the transformation matrix, and be the known quantities that have been pre-calibrated.

4. The method for autonomous take-off and landing of unmanned aerial vehicles based on BeiDou satellite collaborative control according to claim 1, characterized in that, By combining BeiDou positioning information and IMU attitude information, the UAV pose information is obtained, including: Based on the spatial position of the UAV provided by BeiDou positioning and the attitude information provided by the IMU, the rotation matrix between adjacent epochs is obtained. Translation matrix .

5. The method for autonomous take-off and landing of unmanned aerial vehicles based on BeiDou satellite collaborative control according to claim 4, characterized in that, By integrating BeiDou positioning information, IMU attitude information, and visual navigation information, the UAV pose information is optimized to obtain the optimized UAV pose information, including: In the formula, This is the camera's intrinsic parameter matrix, which needs to be calibrated periodically. The spatial position of the target in the camera coordinate system. and The coordinates are in the image plane coordinate system. The depth of the camera is the distance from the target to the camera.

6. The method for autonomous take-off and landing of unmanned aerial vehicles based on BeiDou satellite collaborative control according to claim 1, characterized in that, By utilizing optimized UAV pose information, combined with an intelligent UAV takeoff and landing platform and a BeiDou reference station, coordinated takeoff and landing control of UAVs is achieved, including: It uses a high-definition camera to autonomously perceive the smart nest, identify surrounding obstacles, and determine the relative height of the drone to the smart nest based on SLAM function; The relative altitude, along with the positioning information provided by the BeiDou reference station and the known precise location of the intelligent drone nest, is used to coordinate the take-off and landing control of the drone.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the autonomous take-off and landing method for unmanned aerial vehicles based on BeiDou satellite collaborative control as described in any one of claims 1 to 6.

8. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the autonomous take-off and landing method for unmanned aerial vehicles based on BeiDou satellite collaborative control as described in any one of claims 1 to 6.

9. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the autonomous take-off and landing method for unmanned aerial vehicles based on BeiDou satellite collaborative control as described in any one of claims 1 to 6.

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