A parking apron of a drone and an adaptive control method thereof, and a medium

Abstract

The application discloses a UAV (Unmanned Aerial Vehicle) parking apron and an adaptive control method and medium thereof, and relates to the technical field of UAVs.The standard deviation of the Euclidean distance between the relative position coordinates and the average relative position coordinates is used to quantify the position fluctuation; the maximum value of the resultant velocity is used to quantify the velocity fluctuation; the maximum value of the Euclidean distance between the attitude angle and the average attitude angle is used to quantify the attitude fluctuation; the three are weighted and fused to obtain the smoothness, and the real smooth state of the UAV is determined in combination with the multi-window smoothness checking; the electromagnet is started only after continuous smoothness, so that the disturbance superposition and fuselage deviation risk caused by forcibly applying external magnetic force when the UAV shakes violently are avoided.Through the construction of a multi-dimensional fusion smoothness quantitative evaluation system, the starting condition of the electromagnet and the flight smoothness of the UAV are deeply coupled, and the landing stability and success rate of the UAV in windy weather and moving carrier scenarios are significantly improved.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) technology, and in particular to a UAV landing pad and its adaptive control method and medium. Background Technology

[0002] In existing technologies, a magnetic receiver is installed on a micro-drone, and multiple electromagnet modules are arranged on a recovery platform. By real-time acquisition of three dimensions of flight state parameters—relative position, velocity, and attitude—between the micro-drone and the recovery platform, and combining these with algorithms such as PID control, Kalman filtering, and LSTM trajectory prediction, the output current of the electromagnets is dynamically adjusted. Simultaneously, the micro-drone's own flight attitude is adjusted based on these flight state parameters, thereby achieving adaptive control of the attraction force as the micro-drone approaches the platform, ultimately completing the recovery of the micro-drone. The shortcomings of existing technologies are: The recovery platform is a fixed structure. Its magnetic adjustment is mainly based on the distance between the UAV and the recovery platform and the flight status parameters of the UAV for basic closed-loop adjustment. It can only achieve the adsorption and recovery of UAVs on static platforms or in environments with minimal disturbance. However, when landing on platforms of mobile carriers such as ships or vehicles, or in severe weather conditions such as strong winds, the relative attitude deviation between the UAV and the landing pad is caused by the swaying of the carrier or airflow disturbance, resulting in a low landing success rate and poor landing stability. Summary of the Invention

[0003] This invention provides a landing pad for unmanned aerial vehicles (UAVs) and its adaptive control method and medium to solve the problems in the prior art where, when landing on a landing pad of a mobile carrier such as a ship or vehicle, or under severe weather conditions such as strong winds, there is a severe relative attitude deviation between the UAV and the landing pad due to carrier swaying or airflow disturbance, resulting in a low landing success rate and poor landing stability.

[0004] In a first aspect, the present invention provides an adaptive control method for a landing pad of an unmanned aerial vehicle (UAV), the adaptive control method for the landing pad of the UAV comprising: Step 100: Obtain the relative position coordinates and relative velocity of the UAV relative to the helipad at each moment within M time windows, as well as the attitude angle of the UAV, where M is a positive integer greater than 1. Step 200: Based on the standard deviation of the Euclidean distance between the relative position coordinates at each moment within the M time windows and the average relative position coordinates, obtain the position fluctuation of the UAV within the M time windows. Step 300: Take the square root of the sum of the squares of the instantaneous velocity components of the relative velocity at each moment in each dimension of the three-dimensional space within the M time windows, and take the maximum value to obtain the velocity fluctuation of the UAV in the M time windows. Step 400: Based on the maximum value of the Euclidean distance between the attitude angle and the average attitude angle at each moment within the M time windows, obtain the attitude fluctuation of the UAV in the M time windows. Step 500: After performing a weighted summation of the position fluctuation, velocity fluctuation, and attitude fluctuation for each time window, the stability of the UAV for M time windows is obtained. Step 600: Determine the flight state of the UAV based on the stability and the preset stability threshold; Step 700: When the UAV is in a stable flight state, the electromagnet installed on the landing pad is activated to attract the UAV.

[0005] In a second aspect, the present invention provides a landing pad for a drone, the landing pad including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements an adaptive control method for the drone landing pad as described in the first aspect.

[0006] Thirdly, the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the adaptive control method for the landing pad of an unmanned aerial vehicle as described in the first aspect.

[0007] The aforementioned UAV landing pad and its adaptive control method and medium quantify position fluctuations by using the standard deviation of the Euclidean distance between relative position coordinates and average relative position coordinates, effectively eliminating interference caused by the overall drift of the UAV and accurately reflecting the degree of oscillation of the UAV around its equilibrium position. It uses the maximum value of the synthetic velocity to quantify velocity fluctuations, accurately capturing instantaneous impacts or velocity changes that may occur during the UAV's landing. It uses the maximum value of the Euclidean distance between the attitude angle and the average attitude angle to quantify attitude fluctuations, effectively identifying attitude deviations of the UAV in pitch, roll, and yaw dimensions. The weighted fusion of these three factors yields stability, and combined with multi-window stability verification, it determines the true stable state of the UAV, effectively filtering out misjudgments caused by gusts, carrier swaying, or instantaneous sensor noise. Electromagnetic adsorption is only activated after sustained stability, avoiding the risk of disturbance superposition and fuselage offset caused by forcibly applying external magnetic force when the UAV is violently swaying. Compared to existing technologies, this invention, by constructing a multi-dimensional fusion stability quantification evaluation system, deeply couples the activation conditions of the electromagnet with the UAV's own flight stability, significantly improving the landing stability and success rate of the UAV in windy weather and moving carrier scenarios. Attached Figure Description

[0008] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0009] Figure 1 This is a schematic diagram of an application environment for the adaptive control method of the UAV landing pad in Embodiment 1 of the present invention; Figure 2 This is a flowchart of an adaptive control method for the landing pad of an unmanned aerial vehicle (UAV) in Embodiment 1 of the present invention; Figure 3 This is a schematic diagram of the structure of the helipad provided in Embodiment 9 of the present invention. Detailed Implementation

[0010] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0011] The adaptive control method for a drone landing pad provided in this embodiment of the invention can be applied to, for example... Figure 1 In the application environment shown, specifically, the adaptive control method for the UAV's helipad is applied in a helipad control system, which includes, for example, […]. Figure 1 The diagram shows a drone and a helipad. The drone and the helipad communicate via a network to enable real-time updated helipad control. The drone refers to an unmanned aerial vehicle (UAV) with pre-installed iron landing points on its landing gear, capable of autonomous landing on the helipad. This includes, but is not limited to, multi-rotor drones and VTOL fixed-wing drones. The helipad refers to a drone landing platform equipped with a six-degree-of-freedom platform, electromagnet components, millimeter-wave radar, memory, and processor. It is capable of real-time sensing of the drone's flight status, adaptively adjusting its spatial attitude, performing stability calculations and state determinations, and performing electromagnetic adsorption. This includes, but is not limited to, vehicle-mounted, ship-mounted, and fixed helipads.

[0012] In Example 1, as Figure 2 As shown, this embodiment provides an adaptive control method for a drone's landing pad, which is applied to... Figure 1 Taking the helipad in the example, the adaptive control method for the helipad of the UAV includes: Step 100: Obtain the relative position coordinates and relative velocity of the UAV relative to the helipad at each moment within M time windows, as well as the attitude angle of the UAV, where M is a positive integer greater than 1. Here, a time window refers to a continuously sampled interval with a certain time span that can be dynamically adjusted. Each moment refers to multiple discrete sampling time points within each time window. Relative position coordinates refer to the three-dimensional spatial coordinates of the UAV relative to the helipad in a local three-dimensional spatial coordinate system established with the helipad center as the origin. Relative velocity refers to the instantaneous velocity of the UAV relative to the helipad in a local three-dimensional spatial coordinate system established with the helipad center as the origin. Attitude angle refers to the spatial attitude angle of the UAV in a local three-dimensional spatial coordinate system established with the helipad center as the origin.

[0013] In this embodiment, the helipad continuously monitors the relative position coordinates, relative velocity, and attitude angle of the UAV relative to the helipad at various moments within M time windows using an onboard millimeter-wave radar. The relative position coordinates are [x(t), y(t), z(t)], where t is any moment within any of the M time windows, and x(t), y(t), and z(t) are the position components of the UAV's relative position coordinates relative to the helipad at moment t in the X, Y, and Z dimensions of a local three-dimensional spatial coordinate system established with the helipad center as the origin. The relative velocity is [v...]. x (t),v y (t),v z [(t)], where v x (t), v y (t) and v z (t) represents the instantaneous velocity components of the UAV's relative velocity to the helipad at time t in the X, Y, and Z dimensions of a local three-dimensional coordinate system established with the helipad center as the origin. The UAV's attitude angles are φ(t), θ(t), and ψ(t), where φ(t), θ(t), and ψ(t) are the UAV's roll angle, pitch angle, and yaw angle at time t, respectively.

[0014] Step 200: Based on the standard deviation of the Euclidean distance between the relative position coordinates at each moment within the M time windows and the average relative position coordinates, obtain the position fluctuation of the UAV within the M time windows. The average relative position coordinates refer to the arithmetic mean of the position components in the X, Y, and Z dimensions of the relative position coordinates at any sampling time within any of the M time windows. Position fluctuation is a quantitative indicator used to characterize the severity of the oscillation between the real-time relative position of the UAV and the average position within that time window in a local three-dimensional spatial coordinate system established with the helipad center as the origin.

[0015] In this embodiment, the formula for calculating the position fluctuation of any one of the M time windows is: , Where, σ p Let x be the positional fluctuation of any one of the M time windows, k be the total number of sampling time points within any one of the M time windows, i be any sampling time point within any one of the M time windows, and x be the positional fluctuation of any one of the M time windows. i y i and z i Let be the position components of the relative position coordinates at time i in the X, Y, and Z dimensions, respectively. , and These are the positional components of the average relative position coordinates within the time window in the X, Y, and Z dimensions, respectively.

[0016] Step 300: Take the square root of the sum of the squares of the instantaneous velocity components of the relative velocity at each moment in each dimension of the three-dimensional space within the M time windows, and take the maximum value to obtain the velocity fluctuation of the UAV in the M time windows. The instantaneous velocity component refers to the relative velocity components in the X, Y, and Z dimensions at any sampling moment within any of the M time windows. Velocity fluctuation is a quantitative indicator used to characterize the strength of velocity disturbances caused by instantaneous motion impacts on a UAV in a local three-dimensional spatial coordinate system established with the helipad center as the origin.

[0017] In this embodiment, the formula for calculating the velocity fluctuation in any one of the M time windows is: , Where, σ v Let v be the velocity fluctuation within any one of the M time windows. x,i v y,i and v z,i These are the instantaneous velocity components of the relative velocity at time i in the X, Y, and Z dimensions, respectively.

[0018] Step 400: Based on the maximum value of the Euclidean distance between the attitude angle and the average attitude angle at each moment within the M time windows, obtain the attitude fluctuation of the UAV in the M time windows. The average attitude angle refers to the arithmetic mean of the attitude angles at all sampling moments within any of the M time windows. Attitude fluctuation is a quantitative indicator used to characterize the degree of tilting and jitter of the UAV's real-time attitude angle deviating from the average attitude angle within that time window in a local three-dimensional spatial coordinate system established with the helipad center as the origin.

[0019] In this embodiment, the formula for calculating the attitude fluctuation of the UAV in any one of the M time windows is: , Where, σ att Let φ be the attitude fluctuation of the UAV in any one of the M time windows. i θ i and ψ i Let i represent the roll angle, pitch angle, and yaw angle of the UAV at time i. , and These are the average roll angle, average pitch angle, and average yaw angle within the time window, respectively.

[0020] Step 500: After performing a weighted summation of the position fluctuation, velocity fluctuation, and attitude fluctuation for each time window, the stability of the UAV for M time windows is obtained. Among them, stability refers to a comprehensive quantitative index used to characterize the stability of the flight state of a UAV within any one of the M time windows.

[0021] Step 600: Determine the flight state of the UAV based on the stability and the preset stability threshold; The preset stability threshold refers to a critical value for determining whether the drone's flight state is stable, pre-set based on the drone's landing scenario (e.g., land-based vehicle-mounted or sea-based ship-mounted). The drone's flight state refers to the category of its flight stability, including stable and non-stationary states.

[0022] Step 700: When the UAV is in a stable flight state, the electromagnet installed on the landing pad is activated to attract the UAV.

[0023] In this embodiment, when the processor of the helipad determines that the UAV is in a stable flight state, and after the six-degree-of-freedom platform of the helipad completes attitude alignment with the UAV and actively approaches the UAV, it sends a power control command to the electromagnet component set on the helipad to generate an adsorption magnetic force. Through the generated adsorption magnetic force, the iron landing point pre-installed on the UAV's landing gear is fixedly connected to the surface of the six-degree-of-freedom platform of the helipad, thus completing the adsorption of the UAV by the helipad.

[0024] In this embodiment, when the processor of the helipad determines that the UAV's flight state is stable, the helipad adjusts its current attitude angle to a target attitude angle that matches the UAV's current attitude angle. The specific expression is: φ p target =φ,θ p target =θ,ψ p target =ψ, where φ p target θ p target and ψ p target The target roll angle, target pitch angle, and target yaw angle of the helipad are respectively, and φ, θ, and ψ are the current roll angle, current pitch angle, and current yaw angle of the UAV at the current moment. The actuators of the six-degree-of-freedom platform of the helipad are driven by the PID control algorithm to make the helipad align with the attitude of the UAV.

[0025] When the helipad actively approaches the UAV, the expression for the vertical displacement of the six-degree-of-freedom platform is: Δz = zz target Where Δz is the vertical displacement of the six-degree-of-freedom platform, z is the current flight altitude of the UAV, and z target The target approach distance (e.g., 5cm) for the helipad to actively approach the drone. The six-degree-of-freedom platform moves vertically at a velocity V. z plat Approaching the drone upwards while maintaining horizontal alignment, the expression for the vertical velocity component of the six-degree-of-freedom platform is: V z plat =k p ×Δz, where, V z plat Let k be the velocity component of the six-degree-of-freedom platform in the vertical direction. p Δz is a preset proportional coefficient used to control the approach speed of the six-degree-of-freedom platform, and Δz is the displacement of the six-degree-of-freedom platform in the vertical direction.

[0026] In other embodiments, a control terminal independent of the helipad can be set up. This control terminal performs the calculations and judgments in steps 100 to 600. When the flight state of the UAV is determined to be stable, it sends a control command to the UAV to land on the helipad via wireless communication, and sends a control command to the helipad to activate the electromagnet to attract the UAV. After receiving the control command to land on the helipad, the UAV's own flight control system is responsible for aligning its position with the helipad in the horizontal direction and planning its landing trajectory. After receiving the control command to activate the electromagnet to attract the UAV, the helipad performs the above-mentioned attitude alignment and active approach, and then activates the electromagnet to complete the attraction and fixation of the UAV.

[0027] The adaptive control method for the UAV landing pad in this embodiment quantifies position fluctuations by using the standard deviation of the Euclidean distance between the relative position coordinates and the average relative position coordinates. This effectively eliminates interference caused by the overall drift of the UAV and accurately reflects the degree of oscillation of the UAV around its equilibrium position. It quantifies speed fluctuations by using the maximum value of the synthetic velocity, accurately capturing any instantaneous impacts or speed changes that may occur during the UAV's landing. It quantifies attitude fluctuations by using the maximum value of the Euclidean distance between the attitude angle and the average attitude angle, effectively identifying attitude deviations in pitch, roll, and yaw dimensions. The weighted fusion of these three factors yields a stability score, which, combined with multi-window stability verification, determines the UAV's true stable state, effectively filtering out misjudgments caused by gusts, carrier swaying, or instantaneous sensor noise. Electromagnetic adsorption is only activated after sustained stability is achieved, avoiding the risk of disturbance superposition and fuselage offset caused by forcibly applying external magnetic force during severe UAV swaying. By constructing a multi-dimensional fusion stability quantification evaluation system, the activation conditions of the electromagnet are deeply coupled with the UAV's own flight stability, significantly improving the landing stability and success rate of the UAV in windy weather and moving carrier scenarios.

[0028] In Embodiment 2, step 500 includes: Step 501: Obtain the first output current of the electromagnet at each moment within each time window; The first output current refers to the instantaneous current value actually output by the electromagnet installed on the helipad at any sampling moment within each time window.

[0029] Step 502: Calculate the updated reference position fluctuation threshold based on the first output current, the preset initial reference position fluctuation threshold, and the preset maximum safe current of the electromagnet. The preset initial reference position fluctuation threshold refers to a pre-set initial position fluctuation reference value used to perform dimensionless processing of position fluctuation when the electromagnet is not engaged. The preset maximum safe current of the electromagnet refers to a pre-set upper limit of the safe current that the electromagnet coil is allowed to output during long-term operation.

[0030] In this embodiment, the formula for calculating the updated reference position fluctuation threshold at time t is: , Where, σ p,ref (t) represents the updated reference position fluctuation threshold at time t; σ p,ref0 α is the preset initial reference position fluctuation threshold; α is the preset correction coefficient (ranging from 0 to 1), used to control the sensitivity of the electromagnet's real-time output current to the reference fluctuation threshold; I(t) is the first output current at time t. max This is the preset maximum safe current for the electromagnet.

[0031] Step 503: Calculate the updated reference speed fluctuation threshold based on the first output current, the preset initial reference speed fluctuation threshold, and the preset maximum safe current of the electromagnet. The preset initial reference speed fluctuation threshold refers to the initial speed fluctuation reference value that is pre-set and used to perform dimensionless processing of the speed fluctuation amount when the electromagnet is not involved.

[0032] In this embodiment, the formula for calculating the updated reference velocity fluctuation threshold at time t is: , Where, σ v,ref (t) represents the updated reference velocity fluctuation threshold at time t; σ v,ref0 α is the preset initial reference speed fluctuation threshold; α is the preset correction coefficient (ranging from 0 to 1), used to control the sensitivity of the electromagnet's real-time output current to the reference fluctuation threshold; I(t) is the first output current at time t. max This is the preset maximum safe current for the electromagnet.

[0033] Step 504: Calculate the updated reference attitude fluctuation threshold based on the first output current, the preset initial reference attitude fluctuation threshold, and the preset maximum safe current of the electromagnet. The preset initial reference attitude fluctuation threshold refers to the initial attitude fluctuation reference value that is pre-set and used to perform dimensionless processing of attitude fluctuation when the electromagnet is not involved.

[0034] In this embodiment, the formula for calculating the updated reference attitude fluctuation threshold at time t is: , Where, σ att,ref (t) represents the updated reference attitude fluctuation threshold at time t; σ att,ref0 α is the preset initial reference attitude fluctuation threshold; α is a preset correction coefficient (ranging from 0 to 1), used to control the sensitivity of the electromagnet's real-time output current to the correction of the reference fluctuation threshold; I(t) is the first output current at time t. max This is the preset maximum safe current for the electromagnet.

[0035] Step 505: After performing a weighted summation on the ratios of the position fluctuation to the updated reference position fluctuation threshold, the velocity fluctuation to the updated reference velocity fluctuation threshold, and the attitude fluctuation to the updated reference attitude fluctuation threshold, the stability of the UAV over M time windows is obtained.

[0036] In this embodiment, the formula for calculating the stability of the UAV in any one of the M time windows is: , Where Φ represents the stability of the UAV in any one of the M time windows; σ p σ v and σ att Let σ represent the position fluctuation, velocity fluctuation, and attitude fluctuation within any one of the M time windows; p,ref σ v,ref and σ att,ref These are the arithmetic mean of the updated reference position fluctuation threshold, the arithmetic mean of the updated reference velocity fluctuation threshold, and the arithmetic mean of the updated reference attitude fluctuation threshold, respectively, for all sampling times within the time window; ω p ω v and ω att These are the weighting coefficients corresponding to position fluctuations, velocity fluctuations, and attitude fluctuations, respectively. These coefficients can be dynamically adjusted based on the UAV's landing scenario (e.g., land-based vehicle-mounted or sea-based ship-mounted), and ω... p +ω v +ω att =1.

[0037] The adaptive control method for the UAV landing pad in this embodiment dynamically corrects the initial reference fluctuation thresholds for position, velocity, and attitude by real-time acquisition of the first output current of the electromagnet. The updated reference fluctuation thresholds are then used to recalculate stability, enabling the stability assessment results to reflect the electromagnet's actual ability to correct the UAV's attitude in real time. This achieves bidirectional coupled control between the electromagnet's output state and stability determination: when the electromagnet is activated and the output current is large, the landing pad's ability to correct the UAV's attitude is enhanced, and the reference fluctuation threshold decreases accordingly. In this case, the stability determination conditions are adaptively relaxed to avoid overly conservative landing failures. When the electromagnet is not activated or the current is small, the reference fluctuation threshold increases accordingly. In this case, the stability determination conditions are adaptively tightened to ensure sufficient stability for the UAV without auxiliary attitude correction.

[0038] In Embodiment 3, step 505, when performing a weighted summation of the ratios of the position fluctuation to the updated reference position fluctuation threshold, the velocity fluctuation to the updated reference velocity fluctuation threshold, and the attitude fluctuation to the updated reference attitude fluctuation threshold, includes: When the ambient wind speed is greater than a preset wind speed threshold, the weight corresponding to the ratio of the position fluctuation to the updated reference position fluctuation threshold is reduced, the weight corresponding to the ratio of the attitude fluctuation to the updated reference attitude fluctuation threshold is reduced, and the weight corresponding to the ratio of the speed fluctuation to the updated reference speed fluctuation threshold is increased accordingly.

[0039] In this embodiment, when the ambient wind speed is greater than the preset wind speed threshold (high wind speed), the UAV will inevitably drift horizontally and sway back and forth due to airflow disturbances. The position fluctuation and attitude fluctuation will increase significantly. At this time, the weight of the ratio of position fluctuation to the updated reference position fluctuation threshold and the weight of the ratio of attitude fluctuation to the updated reference attitude fluctuation threshold should be reduced, and the weight of the ratio of speed fluctuation to the updated reference speed fluctuation threshold should be increased accordingly.

[0040] In this embodiment, when the UAV lands on a ship's deck or a moving vehicle, the six-degree-of-freedom platform mounted on the helipad will reciprocate and sway back and forth with the movement of the carrier, which will cause high-frequency instantaneous speed changes in the UAV and significantly increase the speed fluctuation. At this time, the weight corresponding to the ratio of speed fluctuation to the updated reference speed fluctuation threshold should be increased, the weight corresponding to the ratio of position fluctuation to the updated reference position fluctuation threshold should be decreased accordingly, and the weight corresponding to the ratio of attitude fluctuation to the updated reference attitude fluctuation threshold should be decreased accordingly to cope with the interference of the six-degree-of-freedom platform moving with the carrier.

[0041] The adaptive control method for the UAV landing pad in this embodiment achieves dynamic matching between stability assessment and environmental disturbances by adaptively adjusting the weight coefficients of position, speed, and attitude dimensions when wind speeds are high. This effectively weakens the impact of airflow fluctuations and fuselage swaying on the stability assessment, preventing excessively stringent stability judgment conditions caused by wind disturbances that could prevent the UAV from landing. Furthermore, it focuses the assessment on instantaneous speed changes, matching the stability judgment conditions with the actual physical motion of the UAV without increasing landing risk. This significantly improves the UAV's environmental adaptability under complex weather conditions, effectively ensures docking and adsorption safety, and increases the landing success rate.

[0042] In Embodiment 4, step 600 includes: When the stability of the UAV in M ​​time windows is less than the preset stability threshold, the flight state of the UAV is determined to be stable; otherwise, the flight state of the UAV is determined to be non-stable, and the process returns to step 100.

[0043] In this embodiment, when the stability of the UAV is less than the preset stability threshold for M consecutive time windows, the UAV's flight state is determined to be stable; otherwise, the UAV's flight state is determined to be non-stable, the UAV triggers the re-flight mechanism, abandons the current landing attempt, flies back to a safe altitude, adjusts its flight attitude, and prepares for the next landing attempt; the six-degree-of-freedom platform suspends the active approach and maintains the current position and attitude, the electromagnet remains in standby state and cancels the current adsorption command, all data of the current round is cleared, and the process returns to step 100 where the relative position coordinates, relative speed, and attitude angle of the UAV relative to the helipad are continuously monitored and updated by the millimeter-wave radar mounted on the helipad, and the UAV enters the next round of stability determination.

[0044] The adaptive control method for the UAV landing pad in this embodiment employs a multi-time-window cumulative judgment mechanism. A stable state is only determined when the stability of M consecutive time windows is all below the stability threshold. This avoids the susceptibility to misjudgments caused by occasional factors such as gusts or sensor noise in single-window instantaneous judgments, significantly improving the anti-interference capability of UAV flight status identification. Once the UAV's flight status is determined to be non-stationary, the system immediately returns to the attitude monitoring initial step, continuously monitoring the UAV's flight status in a loop, clearing all data from the current round, and restarting the accumulation of new time-window data for the next round of stability judgment. This effectively avoids initiating electromagnet attraction and docking under conditions of severe UAV shaking, ensuring the safety and reliability of the entire UAV landing and docking process.

[0045] In Example 5, step 100, the process of determining any one of the M time windows, includes: Step 110: Determine the preset altitude of the UAV based on the relative position coordinates of the UAV with respect to the helipad at a preset time. The preset time refers to a specific sampling time that is pre-set to determine the preset altitude of the drone. The preset altitude refers to the vertical altitude of the drone at the preset time.

[0046] In this embodiment, the current time is selected as the preset time, and the corresponding preset height is the vertical height of the drone at the current time.

[0047] Step 120: Determine the length of the first time window based on the preset altitude of the UAV, the preset starting altitude at which the UAV begins to perform stability judgment, the preset critical altitude at which the UAV triggers close-range control, the preset length of the maximum time window, and the preset length of the minimum time window. The preset starting altitude for the UAV to begin stability assessment refers to the upper limit of the altitude at which the UAV initiates the stability assessment process. The preset critical altitude for the UAV to trigger proximity control refers to the lower limit of the altitude at which the UAV triggers proximity landing control. The preset maximum time window length refers to the longest sampling time allowed for stability calculation. The preset minimum time window length refers to the shortest sampling time allowed for stability calculation. The length of the first time window refers to the length of the time window dynamically adjusted based on the UAV's preset altitude.

[0048] In this embodiment, the formula for calculating the length of the first time window is: , Where ΔT1 is the length of the first time window, ΔT min ΔT is the preset minimum time window length. max z is the preset maximum time window length. p z is the preset altitude for the drone. min The preset critical altitude (e.g., 0.5m) for triggering close-range control of the drone, z max Set the starting height (e.g., 10m) for the preset drone to begin stability assessment.

[0049] In this embodiment, when the preset altitude of the drone is greater than the preset altitude threshold (higher altitude) and the stability is less than the stability threshold, the urgency of the drone landing is low. At this time, a longer time window can be used to fully suppress the influence of random noise and improve the reliability of stability judgment. When the drone approaches the landing pad, the urgency of the drone landing is high. At this time, the time window needs to be shortened and the flight status refresh rate of the drone needs to be accelerated to improve the response speed.

[0050] Step 130: Obtain the stability of the first time window; based on the stability of the first time window and the preset stability threshold, update the length of the first time window to obtain the length of the second time window. The length of the second time window refers to the length of the time window obtained by dynamic adjustment based on stability.

[0051] In this embodiment, the formula for calculating the length of the second time window is: , Where ΔT2 is the length of the second time window, ΔT1 is the length of the first time window, λ is an adjustment coefficient (ranging from 0 to 1) used to control the sensitivity of the stability to the time window length correction, Φ1 is the stability of the first time window, and Φ th This is the preset stability threshold.

[0052] In this embodiment, when the UAV's flight state is non-stationary (stability greater than or equal to the stability threshold), the time window needs to be shortened to quickly capture the UAV's pose change trend; when the UAV's flight state is stationary (stability less than the stability threshold), the time window can be appropriately extended to filter out the influence of random noise from the sensor and improve the reliability of the stability determination.

[0053] Step 140: Obtain the second output current of the electromagnet at each moment within the second time window; The second output current refers to the instantaneous current value actually output by the electromagnet installed on the helipad at each sampling moment within the second time window.

[0054] Step 150: Based on the second output current and the preset maximum safe current of the electromagnet, update the length of the second time window to obtain the length of any one of the M time windows.

[0055] Among them, the length of any one of the M time windows refers to the sampling window duration that is finally determined after adjusting the three dimensions of the UAV's altitude, stability, and electromagnet intervention current in sequence.

[0056] In this embodiment, the formula for calculating the length of any one of the M time windows is: , Where ΔT is the length of any one of the M time windows, ΔT2 is the length of the second time window, μ is the adjustment coefficient used to control the sensitivity of the electromagnet's intervention current to the time window length, and I2 is the arithmetic mean of the second output current at each moment within the second time window. maxThis is the preset maximum safe current for the electromagnet.

[0057] In this embodiment, when the electromagnet has been activated and output current, it has the ability to actively correct the attitude of the UAV. At this time, the time window can be extended to avoid the instantaneous attitude fluctuations generated during the dynamic adjustment of the electromagnet being misjudged as instability.

[0058] The adaptive control method for UAV landing pads in this embodiment employs a longer time window when the UAV is at a higher altitude to improve the reliability of stability determination, and shortens the time window as the UAV approaches the landing pad to improve response speed, thus matching the time window with the urgency of landing. When the UAV is in a non-stationary state, the time window is shortened to quickly capture attitude changes, and a longer time window is maintained when the UAV is in a stationary state to filter noise, thus matching the time window with flight stability. When the electromagnet intervention current is large, the time window is extended to avoid instantaneous attitude fluctuations generated during the dynamic adjustment of the electromagnet being misjudged as unstable states, thus matching the time window with the electromagnet intervention state. By adaptively and dynamically adjusting the length of the time window step by step based on UAV altitude, flight stability, and electromagnet intervention current, the limitations of a single fixed time window in complex scenarios are effectively avoided, significantly improving the accuracy, robustness, and environmental adaptability of stability determination.

[0059] In Embodiment Six, step 600 further includes: Step 610: When the length of N time windows is greater than a preset window determination threshold, a short time window for the UAV to perform stability determination is determined. This short time window is used to determine whether the UAV has entered a pre-stability state and whether pre-alignment between the landing pad and the UAV is triggered. Wherein, N... <M; The preset window judgment threshold refers to a pre-defined critical value used to distinguish the length of time windows. A short time window is a short sampling interval specifically used for rapid response pre-detection, used to determine whether the UAV has entered a pre-stable state and whether it has triggered pre-alignment between the six-degree-of-freedom platform on the landing pad and the UAV. The pre-stable state refers to the transitional flight state where the UAV's vibration gradually converges but has not yet reached the conditions for formal docking stability. Pre-alignment refers to the six-degree-of-freedom platform adjusting its attitude in advance, gradually approaching the attitude angle of the UAV, before the electromagnet initiates the attraction.

[0060] Step 620: When the length of N time windows is less than or equal to the preset window determination threshold, a long time window for the drone to perform stability determination is determined. The long time window is used to determine whether the drone has entered a stable landing state and whether the electromagnet is triggered to attract the drone. The long-term window refers to a long sampling interval specifically used for stability confirmation, determining whether the UAV has entered a stable landing state and whether the electromagnet has been triggered to attract the UAV. A stable landing state means that the UAV's attitude fluctuations have fully converged, and all indicators meet the formal docking requirements for electromagnet attraction.

[0061] Step 630: When the UAV enters the pre-stable state and the stable landing state, the weighted stability is obtained based on the stability of each time window and the preset time decay factor. The preset time decay factor refers to a pre-set weighting coefficient used to control the contribution of recent and historical data to stability: the earlier the historical time window, the lower the weight, and the newer the time window, the higher the weight.

[0062] In this embodiment, the formula for calculating the weighted stationarity is: , Where, Φ w For weighted stationarity, j is the sequence number of the time window (ranging from 1 to M), M is the total number of time windows, γ is the preset time decay factor (ranging from 0 to 1), and Φ j Let be the stationarity of the j-th time window.

[0063] Step 640: When the weighted stability is less than the preset stability threshold, the current flight state of the UAV is determined to be a stable state; otherwise, the current flight state of the UAV is determined to be a non-stationary state, and the process returns to step 100.

[0064] In this embodiment, when the weighted stability is less than the preset stability threshold, the drone's flight state is determined to be stable; otherwise, the drone's flight state is determined to be non-stable. The drone triggers a re-entry mechanism, abandons the current landing attempt, flies back to a safe altitude, adjusts its flight attitude, and prepares for the next landing attempt. The six-degree-of-freedom platform suspends the active approach and maintains its current position and attitude. The electromagnet remains in standby mode and cancels the current adsorption command. All data for the current round is cleared, and the process returns to step 100, where the millimeter-wave radar mounted on the helipad continuously monitors and updates the drone's relative position coordinates, relative speed, and attitude angle relative to the helipad, before proceeding to the next round of stability determination.

[0065] The adaptive control method for the UAV landing pad in this embodiment adaptively switches between short and long time window judgment modes based on the length of N consecutive time windows and the window judgment threshold. The short window is used to quickly identify the pre-stable state and initiate the six-degree-of-freedom platform pre-alignment in advance, effectively shortening the overall response delay from state judgment to action execution. The long window is used to continuously confirm the stability of the UAV, ensuring reliable triggering of the electromagnet adsorption action. A time decay weight is introduced to perform sliding weighted fusion of the stability of each window, making the stability weight of recent windows higher than that of historical windows. This preserves historical trends to filter out instantaneous noise interference while highlighting the current state to sensitively capture the latest flight status of the UAV, making the stability evaluation results more accurate. This effectively avoids the lag misjudgment and disturbance mis-triggering that easily occur with fixed windows, significantly improving the control robustness of the UAV during the entire landing and docking process in dynamic and complex environments.

[0066] In Embodiment Seven, step 700 includes: Step 701: Obtain the stability of the UAV at a preset time and the distance between the UAV and the landing pad; In this embodiment, the current time is selected as the preset time. The Euclidean distance between the UAV and the helipad is calculated using the relative position coordinates of the UAV and the helipad at the preset time.

[0067] Step 702: When the distance is less than or equal to a preset distance threshold, or when the stability of the UAV at a preset time is greater than or equal to a preset stability judgment trigger threshold, the electromagnet is activated to obtain the initial starting current of the electromagnet. The preset distance threshold refers to a pre-set critical distance value (e.g., 10cm) used to determine whether the drone has entered the effective adsorption range of the electromagnet. The preset stability judgment trigger threshold refers to a pre-set stability threshold value used to trigger the electromagnet's assisted adsorption in advance when the drone's flight is unstable. The initial start-up current refers to the basic maintenance current value output by the electromagnet to ensure that the electromagnet has initial adsorption force.

[0068] Step 703: Based on the distance and the mapping relationship between distance and current, determine the reference adjustment current of the electromagnet; The mapping relationship between distance and current refers to a pre-defined functional formula used to convert the distance between the drone and the landing pad into the current regulation amount of the electromagnet. The reference regulation current refers to the current regulation amount calculated based on the distance between the drone and the landing pad at a preset time.

[0069] In this embodiment, the mapping relationship between distance and current is as follows: Or f(d)=e -βd , Where f(d) is the reference adjustment current of the electromagnet, d is the distance between the UAV and the landing pad at the preset time, ε is the correction term (empirical parameter) to avoid the occurrence of singularities when the distance is 0, and β is the distance attenuation coefficient (empirical parameter).

[0070] In this embodiment, the closer the drone is to the landing pad, the more significant the current regulation effect of the electromagnet becomes.

[0071] Step 704: Based on the initial start-up current, the stability of the UAV at the preset time, and the reference adjustment current, obtain the real-time target adjustment current of the electromagnet. Among them, the real-time target adjustment current refers to the theoretical current demand value that the electromagnet should output in real time after dynamic adjustment of stability and distance.

[0072] In this embodiment, the formula for calculating the real-time target adjustment current is: I target =I base +k Φ ×Φ c ×f(d), Among them, I target For real-time target adjustment of the electromagnet, I base k is the initial starting current. Φ Φ is a preset adjustment coefficient used to control the sensitivity of electromagnetic force to stability, c is a preset time, and Φ c f(d) represents the stability of the drone at a preset time, and f(d) is the reference adjustment current of the electromagnet.

[0073] Step 705: Based on the real-time target adjustment current and the preset maximum safe current of the electromagnet, determine the final output current of the electromagnet, control the electromagnet to adjust from the initial start-up current to the final output current, and use the electromagnetic force generated by the final output current to attract the drone.

[0074] The final output current refers to the actual output current value obtained after limiting the real-time target adjustment current and the maximum safe current.

[0075] In this embodiment, the formula for calculating the final output current is: I final =min(I max ,I target ), Among them, I final I is the final output current of the electromagnet. max I is the preset maximum safe current for the electromagnet. targetThe current is adjusted to the real-time target of the electromagnet.

[0076] In this embodiment, the formula for calculating the electromagnetic force generated by the final output current is: F mag =k mag ×I final , Among them, F mag The electromagnetic force generated by the final output current, k mag I is the magnetic length of the electromagnet (the product of the magnetic flux density and the length of the electromagnet coil) or the force constant (the Ampere force per unit current). final This is the final output current of the electromagnet.

[0077] In this embodiment, when the distance between the drone and the landing pad is less than or equal to a preset distance threshold, or when the drone's stability at a preset time is greater than or equal to a preset stability judgment trigger threshold, the electromagnet is activated to obtain the initial starting current of the electromagnet. When the drone's stability at a preset time is greater than or equal to the preset stability judgment trigger threshold, even if the distance between the drone and the landing pad is greater than the preset distance threshold, the electromagnet can be activated in advance and output a larger final output current. The larger electromagnetic force generated by the final output current is used to actively attract the drone and assist in stabilizing its attitude. When the drone's stability at a preset time is less than the preset stability judgment trigger threshold, after the distance between the drone and the landing pad is less than or equal to the preset distance threshold, the electromagnet is activated and outputs a smaller final output current. The smaller electromagnetic force generated by the final output current is used to gently attract the drone, avoiding excessive magnetic force that could cause the drone's attitude to deviate.

[0078] The adaptive control method for the UAV landing pad in this embodiment obtains a reference adjustment current based on the real-time distance between the UAV and the landing pad. This current is then dynamically corrected based on stability, and the real-time target adjustment current is calculated. A maximum safe current limit protection is applied to obtain the final output current, achieving continuous adaptive adjustment of the electromagnet's attraction force. When the UAV is in an unstable state, the electromagnet can be activated in advance, even at a considerable distance, using the generated electromagnetic force to actively attract the UAV and assist in stabilizing its attitude. This avoids disturbance coupling caused by forcibly applying electromagnetic force when the UAV is swaying significantly. When the UAV is in a stable state, the electromagnet is triggered at close range according to distance conditions, using a smaller electromagnetic force for gentle attraction, avoiding excessive magnetic force that could cause the UAV's attitude to deviate. By constructing an adaptive electromagnet activation and dynamic current adjustment mechanism based on both stability and distance triggering, the stability and reliability of the UAV's autonomous landing and docking process are significantly improved.

[0079] In Example 8, after step 701, the method further includes: Step 710: Obtain the relative speed of the UAV with respect to the landing pad at a preset time and the initial current at the preset time; The initial current at the preset time refers to the instantaneous current value that the electromagnet is currently outputting at the preset time.

[0080] In this embodiment, the current time is selected as the preset time.

[0081] Step 720: When the distance is greater than a preset distance threshold, the stability of the drone at a preset time is less than the preset stability threshold, and the relative speed at a preset time is less than a preset speed threshold, the final output current of the electromagnet is determined based on the preset maximum safe current of the electromagnet, the distance, and the initial current. The electromagnet is controlled to adjust from the initial start-up current to the final output current, and the electromagnetic force generated by the final output current is used to attract the drone.

[0082] The preset speed threshold refers to the critical value set in advance for the descent speed of the drone as it approaches the landing pad.

[0083] In this embodiment, the formula for calculating the final output current is: , Among them, I final I is the final output current of the electromagnet. max I0 is the preset maximum safe current of the electromagnet, I0 is the initial current of the electromagnet at the preset time, δ is the adjustment coefficient used to control the sensitivity of the distance to the correction of the electromagnet current, and d is the distance between the UAV and the landing pad at the preset time.

[0084] In this embodiment, the formula for calculating the electromagnetic force generated by the final output current is: F mag =k mag ×I final , Among them, F mag The electromagnetic force generated by the final output current, k mag I is the magnetic length of the electromagnet (the product of the magnetic flux density and the length of the electromagnet coil) or the force constant (the Ampere force per unit current). final This is the final output current of the electromagnet.

[0085] In this embodiment, under special disturbance conditions such as strong winds or violent movement of the six-degree-of-freedom platform, when the distance between the UAV and the landing pad is greater than a preset distance threshold, the stability of the UAV at a preset time is less than a preset stability threshold, and the relative speed at a preset time is less than a preset speed threshold (the distance between the UAV and the landing pad cannot be reduced due to the movement of the six-degree-of-freedom platform with the carrier), the UAV will have difficulty approaching the six-degree-of-freedom platform autonomously. Electromagnetic force can be actively used to assist in attracting the UAV, helping it overcome environmental disturbances and actively approach the six-degree-of-freedom platform until it enters the normal adsorption conditions (the distance between the UAV and the landing pad is less than or equal to the preset distance threshold, or the stability of the UAV at a preset time is greater than or equal to the preset stability judgment trigger threshold).

[0086] The adaptive control method for the UAV landing pad in this embodiment collects the relative speed of the UAV relative to the landing pad and the initial current of the electromagnet in real time. Based on the three conditions of distance, stability and relative speed, it jointly determines the special working condition of the UAV being stable at low speed but unable to autonomously approach the landing pad. Combining the maximum safe current, distance and initial current to dynamically match the final output current of the electromagnet, it realizes long-distance flexible electromagnetic assisted adsorption. It can actively assist the UAV to approach the platform in complex disturbance environments, avoiding the UAV landing stall or inability to approach due to carrier movement or strong wind interference, and effectively improves the success rate and stability of autonomous landing and docking of UAV in dynamic disturbance scenarios.

[0087] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0088] In embodiment nine, a landing pad for a drone is provided, such as Figure 3 As shown, it includes a six-degree-of-freedom platform, a millimeter-wave radar (not shown), an electromagnet (not shown), a memory (not shown), a processor (not shown), and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the adaptive control method for the UAV's landing pad described in any of the embodiments one to eight above, for example... Figure 2 Steps 100 to 700 shown are omitted here to avoid repetition.

[0089] like Figure 3 The diagram shown is a structural schematic of the helipad provided in this embodiment, where 1 is a six-degree-of-freedom platform and 2 is a support frame.

[0090] In Embodiment 10, a computer-readable storage medium is provided, on which a computer program is stored. When executed by a processor, the computer program implements the adaptive control method for the UAV landing pad described in any of Embodiments 1 to 8 above, for example... Figure 2 Steps 100 to 700 shown are omitted here to avoid repetition.

[0091] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0092] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0093] While specific embodiments of the invention have been described in detail by way of example, those skilled in the art should understand that the examples are for illustrative purposes only and not intended to limit the scope of the invention. Those skilled in the art should also understand that various modifications can be made to the embodiments without departing from the scope and spirit of the invention. The scope of this invention is defined by the appended claims.

Claims

1. An adaptive control method for a UAV landing pad, characterized in that, The adaptive control method for the UAV's landing pad includes: Step 100: Obtain the relative position coordinates and relative velocity of the UAV relative to the helipad at each moment within M time windows, as well as the attitude angle of the UAV, where M is a positive integer greater than 1. Step 200: Based on the standard deviation of the Euclidean distance between the relative position coordinates at each moment within the M time windows and the average relative position coordinates, obtain the position fluctuation of the UAV within the M time windows. Step 300: Take the square root of the sum of the squares of the instantaneous velocity components of the relative velocity at each moment in each dimension of the three-dimensional space within the M time windows, and take the maximum value to obtain the velocity fluctuation of the UAV in the M time windows. Step 400: Based on the maximum value of the Euclidean distance between the attitude angle and the average attitude angle at each moment within the M time windows, obtain the attitude fluctuation of the UAV in the M time windows. Step 500: After performing a weighted summation of the position fluctuation, velocity fluctuation, and attitude fluctuation for each time window, the stability of the UAV for M time windows is obtained. Step 600: Determine the flight state of the UAV based on the stability and the preset stability threshold; Step 700: When the UAV is in a stable flight state, the electromagnet installed on the landing pad is activated to attract the UAV.

2. The adaptive control method for the landing pad of an unmanned aerial vehicle (UAV) according to claim 1, characterized in that, Step 500 includes: Step 501: Obtain the first output current of the electromagnet at each moment within each time window; Step 502: Calculate the updated reference position fluctuation threshold based on the first output current, the preset initial reference position fluctuation threshold, and the preset maximum safe current of the electromagnet. Step 503: Calculate the updated reference speed fluctuation threshold based on the first output current, the preset initial reference speed fluctuation threshold, and the preset maximum safe current of the electromagnet. Step 504: Calculate the updated reference attitude fluctuation threshold based on the first output current, the preset initial reference attitude fluctuation threshold, and the preset maximum safe current of the electromagnet. Step 505: After performing a weighted summation on the ratios of the position fluctuation to the updated reference position fluctuation threshold, the velocity fluctuation to the updated reference velocity fluctuation threshold, and the attitude fluctuation to the updated reference attitude fluctuation threshold, the stability of the UAV over M time windows is obtained.

3. The adaptive control method for the landing pad of an unmanned aerial vehicle (UAV) according to claim 2, characterized in that, Step 505, when performing a weighted summation of the ratios of the position fluctuation to the updated reference position fluctuation threshold, the velocity fluctuation to the updated reference velocity fluctuation threshold, and the attitude fluctuation to the updated reference attitude fluctuation threshold, includes: When the ambient wind speed is greater than a preset wind speed threshold, the weight corresponding to the ratio of the position fluctuation to the updated reference position fluctuation threshold is reduced, the weight corresponding to the ratio of the attitude fluctuation to the updated reference attitude fluctuation threshold is reduced, and the weight corresponding to the ratio of the speed fluctuation to the updated reference speed fluctuation threshold is increased accordingly.

4. The adaptive control method for the landing pad of an unmanned aerial vehicle (UAV) according to claim 1, characterized in that, Step 600 includes: When the stability of the UAV in M ​​time windows is less than the preset stability threshold, the flight state of the UAV is determined to be stable; otherwise, the flight state of the UAV is determined to be non-stable, and the process returns to step 100.

5. The adaptive control method for the landing pad of an unmanned aerial vehicle (UAV) according to claim 1, characterized in that, Step 100, the process of determining any one of the M time windows, includes: Step 110: Determine the preset altitude of the UAV based on the relative position coordinates of the UAV with respect to the helipad at a preset time. Step 120: Determine the length of the first time window based on the preset altitude of the UAV, the preset starting altitude at which the UAV begins to perform stability judgment, the preset critical altitude at which the UAV triggers close-range control, the preset length of the maximum time window, and the preset length of the minimum time window. Step 130: Obtain the stability of the first time window; based on the stability of the first time window and the preset stability threshold, update the length of the first time window to obtain the length of the second time window. Step 140: Obtain the second output current of the electromagnet at each moment within the second time window; Step 150: Based on the second output current and the preset maximum safe current of the electromagnet, update the length of the second time window to obtain the length of any one of the M time windows.

6. The adaptive control method for the landing pad of an unmanned aerial vehicle (UAV) according to claim 1, characterized in that, Step 600 also includes: Step 610: When the length of N time windows is greater than a preset window determination threshold, a short time window for the UAV to perform stability determination is determined. This short time window is used to determine whether the UAV has entered a pre-stability state and whether pre-alignment between the landing pad and the UAV is triggered. Wherein, N... <M; Step 620: When the length of N time windows is less than or equal to the preset window determination threshold, a long time window for the drone to perform stability determination is determined. The long time window is used to determine whether the drone has entered a stable landing state and whether the electromagnet is triggered to attract the drone. Step 630: When the UAV enters the pre-stable state and the stable landing state, the weighted stability is obtained based on the stability of each time window and the preset time decay factor. Step 640: When the weighted stability is less than the preset stability threshold, the current flight state of the UAV is determined to be a stable state; otherwise, the current flight state of the UAV is determined to be a non-stationary state, and the process returns to step 100.

7. The adaptive control method for the landing pad of an unmanned aerial vehicle (UAV) according to claim 1, characterized in that, Step 700 includes: Step 701: Obtain the stability of the UAV at a preset time and the distance between the UAV and the landing pad; Step 702: When the distance is less than or equal to a preset distance threshold, or when the stability of the UAV at a preset time is greater than or equal to a preset stability judgment trigger threshold, the electromagnet is activated to obtain the initial starting current of the electromagnet. Step 703: Based on the distance and the mapping relationship between distance and current, determine the reference adjustment current of the electromagnet; Step 704: Based on the initial start-up current, the stability of the UAV at the preset time, and the reference adjustment current, obtain the real-time target adjustment current of the electromagnet. Step 705: Based on the real-time target adjustment current and the preset maximum safe current of the electromagnet, determine the final output current of the electromagnet, control the electromagnet to adjust from the initial start-up current to the final output current, and use the electromagnetic force generated by the final output current to attract the drone.

8. The adaptive control method for the landing pad of an unmanned aerial vehicle (UAV) according to claim 6, characterized in that, Following step 701, the following also includes: Step 710: Obtain the relative speed of the UAV with respect to the landing pad at a preset time and the initial current at the preset time; Step 720: When the distance is greater than a preset distance threshold, the stability of the drone at a preset time is less than the preset stability threshold, and the relative speed at a preset time is less than a preset speed threshold, the final output current of the electromagnet is determined based on the preset maximum safe current of the electromagnet, the distance, and the initial current. The electromagnet is controlled to adjust from the initial start-up current to the final output current, and the electromagnetic force generated by the final output current is used to attract the drone.

9. A landing pad for an unmanned aerial vehicle (UAV), 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 computer program, it implements the adaptive control method for the landing pad of the UAV as described in any one of claims 1 to 8.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the adaptive control method for the landing pad of the UAV as described in any one of claims 1 to 8.

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