Vehicle-mounted modularized unmanned aerial vehicle intelligent airport system and control method

Abstract

The invention relates to the technical field of unmanned aerial vehicle airport intelligent control, and discloses a vehicle-mounted modular unmanned aerial vehicle intelligent airport system and a control method, based on ranging data of a platform label and an unmanned aerial vehicle label, the ranging data is subjected to weighted least square fitting processing, and a position rough solution of an unmanned aerial vehicle is obtained; the attitude quaternion of the unmanned aerial vehicle is calculated through multi-layer coupling attitude closed analysis in combination with the position rough solution and the ranging data; carrying out extended Kalman filtering fusion processing on the position rough solution, the attitude quaternion and the distance measurement data to determine a position fine solution of the unmanned aerial vehicle; based on the position fine solution, a preset landing point and a preset trajectory parameter, an expected spiral landing trajectory is generated; and outputting a control signal of an unmanned aerial vehicle motor through layered adjustment of inner ring control and outer ring control according to the expected spiral landing trajectory and the position refined solution.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent control of unmanned aerial vehicle (UAV) airports. More specifically, it relates to a vehicle-mounted modular UAV intelligent airport system and a control method thereof. Background Art

[0002] As a movable UAV deployment platform, the vehicle-mounted modular UAV intelligent airport realizes the rapid response and all-weather operation capabilities of UAVs by integrating a UWB positioning base station, an autonomous takeoff and landing guidance system, and an energy supply module.

[0003] However, the following problems still exist in the autonomous landing stability of UAVs in such airports in complex environments: The traditional UWB base station layout cannot provide an attitude reference, resulting in large-angle flip ambiguity in attitude solution. Especially in the case of multipath interference or tag occlusion, the positioning error is large, leading to the risk of unstable landing or even crashing. Summary of the Invention

[0004] The present invention provides a vehicle-mounted modular UAV intelligent airport system and a control method thereof to solve the technical problems existing in the background art.

[0005] In a first aspect, the present invention provides a control method for a vehicle-mounted modular UAV intelligent airport, including: Deploying tags on the vehicle-mounted airport platform and the UAV in a preset structure, where the tags include: platform tags and UAV tags; Initializing and generating a platform coordinate system with the center of the vehicle-mounted airport platform as the origin; When the distance parameter between any one platform tag and any one UAV tag is less than a preset guidance distance, triggering a landing guidance process, including: Step 1: Based on the ranging data of the platform tags and the UAV tags, processing the ranging data through weighted least squares fitting to obtain a rough solution of the UAV's position; Step 2: Combining the rough position solution and the ranging data, and solving for the attitude quaternion of the UAV through multi-layer coupled attitude closed-loop analysis; Step 3: Processing the rough position solution, the attitude quaternion, and the ranging data through extended Kalman filter fusion to determine the precise solution of the UAV's position; Step 4: Generating an expected spiral landing trajectory based on the precise position solution, a preset landing point, and preset trajectory parameters; Step 5: According to the expected spiral landing trajectory and the precise position solution, outputting a control signal for the UAV motor through hierarchical adjustment of inner-loop control and outer-loop control.

[0006] Further, the preset structure includes: Central platform label arranged at the preset height in the center of the vehicle-mounted airport platform; Peripheral platform labels centered on the central platform label and distributed coplanarly in a regular pentagon; UAV labels distributed in a non-coplanar triangular pyramid on the UAV.

[0007] Further, the weighted least squares fitting process includes: Obtain the position coordinates of the platform label in the platform coordinate system; Based on the position coordinates of the platform label, perform least-cost fitting processing on the ranging data corresponding to each platform label and the UAV label to form a ranging geometric equation; Analyze the ranging geometric equation through least squares to obtain a rough solution of the UAV's position.

[0008] Further, the multi-layer coupled attitude closure analysis includes: Eliminate the outlier observations in the ranging data; Based on the distance matching degree and direction coupling degree between the platform labels, combined with the rough position solution, construct a weighted covariance matrix; Through eigenvalue decomposition and iterative refinement algorithm on the weighted covariance matrix, use the rough position solution as the initial condition to assist in the solution, and obtain the attitude quaternion of the UAV.

[0009] Further, the extended Kalman filter fusion process includes: Take the rough position solution, attitude quaternion, and ranging data as observation quantities, and output the precise position of the UAV through the extended Kalman filter fusion algorithm.

[0010] Further, based on the precise position solution, preset landing point, and preset trajectory parameters, generate an expected spiral landing trajectory, including: The preset trajectory parameters include: a radial distance attenuation model and a rotation angle change model based on height changes; Centered on the preset landing point, combined with the precise position solution, radial distance attenuation model, and rotation angle change model, generate an expected spiral landing trajectory; the expected spiral landing trajectory represents the expected coordinates corresponding to the precise position solution in the platform coordinate system.

[0011] Further, through hierarchical regulation of inner loop control and outer loop control, including: The outer loop controller generates an expected attitude according to the error between the precise position solution and the expected coordinates; The inner loop controller generates a motor speed adjustment signal according to the error between the attitude quaternion and the expected attitude.

[0012] In the second aspect, a vehicle-mounted modular UAV intelligent airport control system is applied to any one of the vehicle-mounted modular UAV intelligent airport control methods described above, and includes: UWB tag module, used to deploy tags on vehicle-mounted airport platforms and drones; Coordinate initialization module, used to establish the platform coordinate system; Landing guidance module, including: The data acquisition unit is used to process the ranging data based on the platform tag and the drone tag through weighted least squares fitting to obtain a rough solution of the drone's position; The data processing unit is used to combine the rough position solution and the ranging data, and obtain the attitude quaternion of the UAV through multi-layer coupled attitude closure analysis; The data fusion unit is used to fuse the rough position solution, attitude quaternion and ranging data through extended Kalman filtering to determine the precise position solution of the UAV; A trajectory prediction unit, for generating an expected spiral landing trajectory based on the position analysis, the preset landing point and the preset trajectory parameters; The motor control unit is used to output the control signal of the drone motor through layered adjustment of inner and outer loop controls according to the expected spiral landing trajectory and position.

[0013] The beneficial effects of the present invention are as follows: a geometric coupling system is constructed by deploying a regular pentagonal coplanar UWB tag array on a vehicle-mounted airport platform and a triangular pyramid non-coplanar tag array on the drone end; combining weighted least squares position rough solution, multi-layer coupled attitude closure analysis and extended Kalman filter fusion, the key problem of autonomous landing of the drone under the vehicle-mounted dynamic platform is solved, high-precision positioning and unique attitude solution are achieved, and the attitude flip ambiguity and multipath interference of the traditional solution are eliminated, so that the drone can land stably in complex scenes such as vehicle movement and bumps. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 It is a vehicle-mounted modularized UAV intelligent airport control method of the present invention; Figure 2 The invention relates to a vehicle-mounted modularized unmanned aerial vehicle intelligent airport control system. DETAILED DESCRIPTION

[0015] The subject matter described herein will now be discussed with reference to example embodiments. It should be understood that the discussion of these embodiments is only to enable those skilled in the art to better understand and implement the subject matter described herein, and the functions and arrangements of the elements discussed may be changed without departing from the scope of protection of the contents of this specification. Each example may omit, replace or add various processes or components as needed. In addition, the features described relative to some examples may also be combined in other examples.

[0016] like Figures 1 to 2As shown in the figure, a vehicle-mounted modular UAV intelligent airport control method includes: Deploying tags on the vehicle-mounted airport platform and the UAV in a preset structure, where the tags include: platform tags and UAV tags; In an embodiment of the present invention, the preset structure includes: A central platform tag deployed at a preset height at the center of the vehicle-mounted airport platform; Peripheral platform tags distributed in a coplanar regular pentagon with the central platform tag as the center; UAV tags distributed in a non-coplanar triangular pyramid on the UAV.

[0017] Specifically, the platform tags include: 、 、 、 、 And ; The UAV tags include: 、 And ; The platform tag Located at the center height z of the vehicle-mounted airport platform As the center, the platform tags at the vertices of the regular pentagon constructed 、 、 、 And ; The UAV tag 、 And , 、 And Are distributed in a non-coplanar triangular pyramid extending upward.

[0018] It should be noted that the platform tag Located at a high center can reduce occlusion (such as propeller interference) and ensure signal stability; The platform tags 、 、 、 And Form a coplanar array of regular pentagons, providing an attitude reference plane, and multi-vertex redundant observations improve positioning reliability; The UAV tags 、 And Are distributed in a non-coplanar triangular pyramid, so that the six degrees of freedom of the UAV can be uniquely solved by ranging any three points, eliminating mirror ambiguity. This deployment structure realizes high-precision and unambiguous positioning and attitude solution through geometric characteristic complementarity, ensuring the accuracy and stability of the landing of the vehicle-mounted UAV.

[0019] Taking the center of the vehicle-mounted airport platform as the origin, initialize and generate a platform coordinate system; In response to the distance parameter between any one platform tag and any one drone tag being less than a preset guidance distance, trigger a landing guidance process, including: Step 1, based on the ranging data of the platform tag and the drone tag, perform weighted least squares fitting on the ranging data to obtain a rough solution of the drone's position; In an embodiment of the present invention, the weighted least squares fitting process includes: Obtain the position coordinates of the platform tag in the platform coordinate system; Based on the position coordinates of the platform tag, perform minimum cost fitting on the ranging data corresponding to each platform tag and the drone tag to form a ranging geometric equation; Analyze the ranging geometric equation through the least squares method to obtain a rough solution of the drone's position.

[0020] In an embodiment of the present invention, obtaining a rough solution of the drone's position by performing weighted least squares fitting on the UWB distance set includes: Determine the position of each platform tag in the platform coordinate system , , , , and represent the abscissa, ordinate, and altitude coordinate of the k-th platform tag in the platform coordinate system , represents the transpose of the vector, facilitating subsequent matrix operations; Determine the UWB distance (ranging data) between the drone tag and the platform tag , ; For each UWB distance, construct a ranging geometric equation, , , represents the rough solution of the position to be solved, , and represent the position of the rough solution of the position to be solved with respect to the axis, axis, and axis in the platform coordinate system, represents the ranging error; Linearize the ranging geometric equation to: ; where , and represent the th platform tag and the central platform tag In the platform coordinate system axis, axis and the coordinate differences of the axis, , indicating the position of the central platform label in the platform coordinate system; Construct the observation matrix , where the element ; Construct the observation vector , ; where, represents the position of the c-th platform label in the platform coordinate system, represents the UWB distance between the c-th platform label and the i-th UAV label; Based on distance weighting, construct the diagonal weighting matrix , , , represents function; Construct the objective function as: ; Solve the objective function by the least squares method to obtain the rough position solution , .

[0021] It should be noted that to determine the position of the platform label , establish the coordinate reference of the platform label. respectively represent the horizontal, vertical, and height coordinates of the k-th platform label in the platform coordinate system P, and clarify the spatial position of the label. Determine the UWB distance (including 15 distance parameters) to obtain the ranging observation values between the UAV and the platform label as the positioning input. represents the platform label and the UAV label The measured distance is the key observation data for positioning.

[0022] Construct the geometric equation to establish a positioning model based on geometric relationships and describe the connection between the measured distance and the coordinate difference. is the theoretical distance between the UAV and the platform label, is the ranging error, and the geometric equation is the basic model of measured value = theoretical value + error. Linearize the processing to convert the non-linear geometric equation into a linear form for easy least squares solution. By squaring and organizing both sides of the geometric equation and linearizing using the coordinate difference, obtain the observation matrix and the vector b, and the observation matrix The sum vector b facilitates the transformation of geometric equations and the adaptation to the least squares algorithm. A weighted matrix is constructed to weight the distance observations, such that observations with smaller errors (usually closer distances) have a greater weight in the solution, improving the accuracy. The objective function is in the standard form of weighted least squares, and a rough position solution is obtained by minimizing the weighted sum of squares of the observation residuals.

[0023] Step 2: Combine the rough position solution with the ranging data, and through multi-layer coupled attitude closure analysis, calculate the attitude quaternion of the UAV. In an embodiment of the present invention, the multi-layer coupled attitude closure analysis includes: Eliminate the outlier observations in the ranging data; Based on the distance matching degree and direction coupling degree between platform tags, combined with the rough position solution, construct a weighted covariance matrix; Through eigenvalue decomposition and iterative refinement algorithm on the weighted covariance matrix, using the rough position solution as the initial condition for auxiliary calculation, obtain the attitude quaternion of the UAV.

[0024] In an embodiment of the present invention, performing multi-layer coupled attitude closure analysis on the rough position solution of the UAV and the UWB distance set to obtain the attitude quaternion of the UAV includes: Calculate the distance measurement value between each platform tag and the UAV tag and the distance residual of the rough UAV position, so as to eliminate outlier observations according to the distance residual, as follows: Distance residual: ; Eliminate outlier observations: ; Wherein, represents the distance residual between the platform tag and the UAV tag , represents the set of effective observation indices, represents the standard deviation of the UWB distances in the UWB distance set; Construct the coupling factor , as follows:

[0025]

[0026] Wherein, represents the coupling factor between the platform tag and the UAV tag , represents the nominal baseline distance, , Indicates the platform label spacing, Indicates the weight of the platform label spacing, , Indicates the coupling adjustment constant, Indicates the platform label and the axial coupling degree of the UAV label ; Indicates the UAV coordinate system constructed with as the origin The position of the UAV label under ; Based on the coupling factor , based on the effective observation index set , construct the weighted covariance matrix with the mean removed, as follows:

[0027]

[0028]

[0029] Among them, The weighted average position of the UAV label calculated based on the effective observation index set and the weight in the platform coordinate system ; Indicates the weighted average position of the UAV label in the UAV coordinate system ; Indicates the weighted covariance matrix with the mean removed, Indicates the coordinates of the UAV label in the UAV coordinate system B in the platform coordinate system P, Indicates the UAV coordinate system The coordinates of the UAV label under ; Based on the weighted covariance matrix , construct the feature matrix , as follows: , ; Among them, Indicates the trace of the weighted covariance matrix ; Indicates the identity matrix, and the weighted covariance matrix is a matrix of size 3×3, , , , , and are all the weighted covariance matrix The elements in is a column vector composed of specific element differences of the weighted covariance matrix The column vector participates in operations such as eigenvalue decomposition by extracting the difference information between elements in the weighted covariance matrix to help solve the attitude quaternion of the UAV; denotes the transpose of denotes the transpose of Perform eigenvalue decomposition on the feature matrix to obtain the eigenvector corresponding to the largest eigenvalue, and normalize the eigenvector to obtain the initial quaternion; Iteratively process the initial quaternion through the 6D attitude estimation coupled iterative refinement algorithm to obtain the attitude quaternion.

[0030] It should be noted that the distance residuals are calculated and the outlier observations are removed to avoid data interference with excessive errors by removing outliers, so as to screen out effective observations and improve the reliability of attitude solution. Among them, is used to measure the deviation between the observed value and the theoretical value. The set of effective observation indices is used to retain the observations with residuals . Among them, is the UWB distance standard deviation, which reflects the data dispersion degree.

[0031] Construct a coupling factor to quantify the contribution weights of different observations to attitude solution and highlight the role of effective observations. It comprehensively reflects the coupling strength between the platform tag and the UAV tag. Among them, is the nominal baseline distance, which is used to measure the matching degree between the observed distance and the theoretically designed baseline; characterizes the axial coupling degree (i.e., direction consistency) of the two tags in their respective coordinate systems, is an adjustment constant.

[0032] Combine the distance matching degree with the direction consistency . The closer the distance is to the nominal value and the higher the direction coupling degree, , the higher the weight of this observation in attitude solution. This design breaks through the limitation of traditional single-distance weighting, distinguishes the observation quality from the essence of geometric relationships, and innovatively realizes high-precision attitude solution.

[0033] Construct a de-meaned weighted covariance matrix to extract the statistical correlation between coordinates from effective observations and establish a mathematical model for attitude solution. and are respectively in the platform coordinate system P and the UAV coordinate system B, based on the effective observations and The weighted average position, with the mean removed to highlight the coordinate deviation characteristics. is the covariance matrix with the mean removed, capturing the co-variation relationship of the label coordinate deviations in two coordinate systems, and is the core matrix connecting the two coordinate systems and solving the attitude transformation. By weighted averaging to eliminate the position offset and then calculating the covariance matrix, it meets the requirement of extracting the correlation between variables in statistics. This matrix transforms the discrete label coordinates into structured statistical features.

[0034] Construct the feature matrix K and decompose it, so as to transform the geometric features of the covariance matrix into an algebraic form and efficiently extract the attitude information. , where is the trace of matrix S (the sum of the diagonal elements, representing the energy of the matrix), is composed of the skew-symmetric elements of matrix S, and I is the identity matrix. The construction of the feature matrix K is based on matrix theory, and its eigenvalue decomposition can extract the main geometric features of the covariance matrix S. The eigenvector corresponding to the largest eigenvalue contains the main transformation direction between the two coordinate systems, and after normalizing it as the initial quaternion, it essentially transforms the complex three-dimensional rotation problem into algebraic feature extraction, improving the efficiency and accuracy of attitude solution.

[0035] By iteratively refining the initial quaternion, eliminate the errors caused by data noise or simplified models, and improve the accuracy of attitude solution. Although the initial quaternion is based on the main eigenvector, there may be local optimality or noise effects. Through the 6D attitude estimation coupled iterative refinement algorithm, gradually correct the quaternion to make it more consistent with the true attitude. It should be noted that the 6D attitude estimation coupled iterative refinement algorithm is an existing technology and will not be elaborated here.

[0036] Step 3, fuse and process the rough position solution, attitude quaternion and ranging data through the extended Kalman filter to determine the precise position solution of the UAV; In an embodiment of the present invention, the extended Kalman filter fusion processing includes: Taking the rough position solution, attitude quaternion and ranging data as the observed quantities, and outputting the precise position solution of the UAV through the extended Kalman filter fusion algorithm.

[0037] In an embodiment of the present invention, fusing and processing the rough position solution, attitude quaternion and UWB distance set through the extended Kalman filter to determine the precise position solution of the UAV includes: Taking the rough position solution, attitude quaternion and UWB distance set as the observed quantities, and obtaining the precise position solution of the UAV based on the extended Kalman filter fusion algorithm .

[0038] It should be noted that the rough solution of the UAV position obtained in the early stage, the attitude quaternion representing the UAV attitude, and the set of UWB distances between the UAV and each tag of the platform are used as the observation inputs together. These observations are processed by means of the extended Kalman filter fusion algorithm, which can effectively handle the uncertainties and noises in the nonlinear system. Through the fusion analysis and optimal estimation of multi-source data, the accuracy of position estimation is further improved, and finally a more accurate refined solution of the UAV position is obtained, providing more reliable position information support for the UAV to achieve precise landing.

[0039] It should be noted that the extended Kalman fusion filter algorithm is a commonly used technical means in the field of multi-source data fusion, so it will not be elaborated here.

[0040] Step 4: Generate an expected spiral landing trajectory based on the refined position, the preset landing point, and the preset trajectory parameters; In an embodiment of the present invention, generating an expected spiral landing trajectory based on the refined position, the preset landing point, and the preset trajectory parameters includes: The preset trajectory parameters include: a radial distance attenuation model and a rotation angle change model based on the height change; With the preset landing point as the center, combining the refined position, the radial distance attenuation model, and the rotation angle change model, an expected spiral landing trajectory is generated; the expected spiral landing trajectory represents the expected coordinates corresponding to the refined position in the platform coordinate system.

[0041] In an embodiment of the present invention, generating an expected spiral landing trajectory based on the refined position of the UAV, the preset landing point, and the preset landing trajectory parameters includes: Refined position Is expressed as: , , And Represent the abscissa, ordinate, and height coordinate of the UAV in the platform coordinate system ; The coordinates of the preset landing point are any point on the vehicle-mounted airport platform, which is: ; where Represents the abscissa of the preset landing point, Represents the ordinate of the preset landing point; The preset landing trajectory parameters include: the initial radius , the attenuation coefficient , the initial angle And the initial angular velocity ; The expected spiral landing trajectory includes: the radial distance, the rotation angle, the abscissa of the spiral trajectory, and the ordinate of the spiral trajectory, as follows: The radial distance of the expected spiral landing trajectory is: , is the natural base; The rotation angle of the expected spiral landing trajectory is: ; The abscissa of the spiral trajectory of the expected spiral landing trajectory is: ; The ordinate of the spiral trajectory of the expected spiral landing trajectory is: .

[0042] It should be noted that the position is precisely solved: the position is precisely solved is expressed as . Thus, the precise horizontal, vertical, and height coordinates of the UAV in the platform coordinate system P are clarified, providing a real-time position reference for trajectory generation.

[0043] The preset landing point coordinates are user-defined and set as . The preset landing point coordinates provide an end-point guidance for the trajectory, ensuring that the UAV can finally reach the designated position on the vehicle-mounted airport platform.

[0044] Preset landing trajectory parameters: including the initial radius , the attenuation coefficient , the initial angle and the initial angular velocity . These parameters are the control elements of the spiral trajectory. The initial radius determines the starting scale of the trajectory, the attenuation coefficient controls the contraction rate of the radial distance with height, and the initial angle and the initial angular velocity determine the starting state and speed of rotation, jointly realizing the shape and dynamic control of the spiral trajectory.

[0045] Radial distance formula: As the height decreases ( decreases), the radial distance exponentially decays, meeting the logic that the UAV gradually approaches the landing point during descent and ensuring that the horizontal distance contracts synchronously during the height reduction process.

[0046] Rotation angle formula The initial angle determines the starting direction, causes the rotation angle to gradually increase as the height decreases, and the change rate matches , , forming a smooth spiral rotation effect and ensuring the continuity and regularity of the trajectory.

[0047] Horizontal and vertical coordinate formula: and . With a preset landing point as the center, through trigonometric functions and decompose the radial distance into horizontal and vertical directions, forming a spiral trajectory around the landing point, strictly following the geometric definition of a helix to ensure that the drone approaches the landing point along a reasonable path.

[0048] Step 5, according to the desired spiral landing trajectory and position precise solution, through hierarchical adjustment of inner loop control and outer loop control, output the control signal of the drone motor.

[0049] In an embodiment of the present invention, the hierarchical adjustment through inner loop control and outer loop control includes: The outer loop controller generates a desired attitude according to the error between the position precise solution and the expected coordinates; The inner loop controller generates a motor speed adjustment signal according to the error between the attitude quaternion and the desired attitude.

[0050] In an embodiment of the present invention, inner loop control and outer loop control are respectively performed according to the desired spiral landing trajectory and position precise solution to determine the control signal of the motor of the target drone, including: Obtain the position precise solution The predicted position precise solution corresponding to the desired spiral landing trajectory ; Based on the position error between the position precise solution and the predicted position precise solution , to determine the outer loop control output through the outer loop controller as follows:

[0051] Wherein, represents the outer loop control output, , and are respectively the proportional coefficient, integral coefficient and differential coefficient of the outer loop controller, represents the integral term of the outer loop controller, represents the differential term of the outer loop controller; Based on the drone dynamics model, convert the outer loop control output into the desired attitude quaternion ; Calculate the attitude error between the attitude quaternion and the desired attitude quaternion ; Based on the attitude error between the attitude quaternion and the desired attitude quaternion , to determine the inner-loop control output through the inner-loop controller as follows:

[0052] Wherein, represents the inner-loop control output, , and are respectively the proportional coefficient, integral coefficient, and derivative coefficient of the inner-loop controller, represents the integral term of the inner-loop controller, represents the derivative term of the inner-loop controller; Based on the UAV motor model, convert the inner-loop control output into the control signal of the motor of the target UAV.

[0053] It should be noted that the predicted position refinement on the expected spiral landing trajectory corresponding to the current position refinement of the UAV is determined to clarify the target position that the UAV should reach. Then, based on the position error between the actual position refinement and the predicted position refinement of the UAV, the outer-loop control output is calculated using the outer-loop controller (adopting the proportional, integral, and derivative adjustment mechanism) to correct the position deviation and make the UAV approach the position of the expected trajectory. Next, the attitude error between the current attitude quaternion and the expected attitude quaternion of the UAV is calculated, and then the inner-loop control output is determined through the inner-loop controller (also using proportional, integral, and derivative adjustment) to adjust the attitude of the UAV to conform to the expected attitude. Finally, according to the UAV motor model, the inner-loop control output is converted into the actual motor control signal, so as to drive the UAV to fly along the expected spiral landing trajectory, ensuring that its position and attitude can accurately track the set trajectory and achieve precise landing. The whole process uses hierarchical control of the inner and outer loops to precisely adjust the position and attitude respectively, ensuring the stability and accuracy of the UAV flight.

[0054] It should be noted that the outer-loop control output reflects the position error adjustment requirement. Based on the dynamic model, it needs to be converted into the attitude required to achieve this position control. For example, the required total lift and moment are calculated according to the outer-loop control output, and then the expected attitude (including roll and pitch angles) that meets this mechanical condition is deduced through the dynamic equation and finally represented by quaternion, providing the target for the inner-loop attitude control.

[0055] It should be noted that the inner-loop control output is for attitude error adjustment, and the motor model converts it into specific motor control signals. For example, the inner-loop control output determines the required torque or rotational speed. Through the relationship between the rotational speed and the PWM signal in the motor model, as well as the relationship between the lift and the rotational speed, the corresponding PWM control signal is generated to drive the motor to adjust the rotational speed and generate the required lift and torque, realizing precise attitude adjustment and ensuring that the UAV flies along the expected trajectory.

[0056] A vehicle-mounted modular UAV intelligent airport control system, which is applied to any one of the vehicle-mounted modular UAV intelligent airport control methods described above, includes: A UWB tag module, used to deploy tags on the vehicle-mounted airport platform and the UAV; A coordinate initialization module, used to establish a platform coordinate system; A landing guidance module, including: A data acquisition unit, used to process the ranging data based on the platform tag and the UAV tag through weighted least squares fitting to obtain a rough solution of the UAV's position; A data processing unit, used to combine the rough position solution and the ranging data, and calculate the attitude quaternion of the UAV through multi-layer coupled attitude closed-loop analysis; A data fusion unit, used to fuse the rough position solution, the attitude quaternion and the ranging data through extended Kalman filter to determine the accurate position solution of the UAV; A trajectory prediction unit, used to generate an expected spiral landing trajectory based on the accurate position solution, a preset landing point and preset trajectory parameters; A motor control unit, used to output control signals for the UAV motors through hierarchical adjustment of inner-loop control and outer-loop control according to the expected spiral landing trajectory and the accurate position solution.

[0057] The above describes the embodiments of this example, but this example is not limited to the above specific implementation manners. The above specific implementation manners are only illustrative and not restrictive. Under the inspiration of this example, those of ordinary skill in the art can also make many forms, all of which fall within the protection scope of this example.

Claims

1. An in-vehicle modular UAV intelligent airport control method, characterized in that, Comprising: Deploying tags on the vehicle-mounted airport platform and the drone in a preset structure, the tags including: platform tags and drone tags; Initializing and generating a platform coordinate system with the center of the vehicle-mounted airport platform as the origin; In response to the distance parameter between any one platform tag and any one drone tag being less than a preset guiding distance, triggering a landing guidance process, including: Step 1, based on the ranging data of the platform tag and the drone tag, performing weighted least squares fitting processing on the ranging data to obtain a rough solution of the drone's position; Step 2, combining the rough position solution and the ranging data, and through multi-layer coupled attitude closed-loop analysis, calculating and obtaining the attitude quaternion of the drone; Step 3, performing extended Kalman filter fusion processing on the rough position solution, the attitude quaternion, and the ranging data to determine the precise solution of the drone's position; Step 4, based on the precise position solution, a preset landing point, and preset trajectory parameters, generating an expected spiral landing trajectory; Step 5, according to the expected spiral landing trajectory and the precise position solution, through hierarchical adjustment of inner-loop control and outer-loop control, outputting control signals for the drone's motors.

2. The on-vehicle modular UAV intelligent airport control method according to claim 1, characterized in that, The preset structure includes: A central platform tag arranged at a preset height at the center of the vehicle-mounted airport platform; Peripheral platform tags distributed coplanarly in a regular pentagon with the central platform tag as the center; Drone tags distributed in a non-coplanar triangular pyramid on the drone.

3. The on-vehicle modular UAV intelligent airport control method according to claim 2, characterized in that, The weighted least squares fitting processing includes: Obtaining the position coordinates of the platform tag in the platform coordinate system; Based on the position coordinates of the platform tag, performing minimum cost fitting processing on the ranging data corresponding to each platform tag and the drone tag to form a ranging geometric equation; Analyzing the ranging geometric equation through the least squares method to obtain a rough solution of the drone's position.

4. The on-vehicle modular UAV intelligent airport control method according to claim 3, characterized in that, The multi-layer coupled attitude closed-loop analysis includes: Removing outlier observations in the ranging data; Based on the distance matching degree and direction coupling degree between platform tags, combining the rough position solution, and constructing a weighted covariance matrix; Through eigenvalue decomposition and iterative refinement algorithm on the weighted covariance matrix, using the rough position solution as the initial condition for auxiliary calculation to obtain the attitude quaternion of the drone.

5. The on-vehicle modular UAV intelligent airport control method according to claim 4, wherein, The extended Kalman filter fusion processing includes: Taking the rough position solution, the attitude quaternion, and the ranging data as observation quantities, and through the extended Kalman filter fusion algorithm, outputting the precise solution of the drone's position.

6. The vehicle-mounted modular UAV intelligent airport control method according to claim 5, wherein, Generating an expected spiral landing trajectory based on the precise position solution, a preset landing point, and preset trajectory parameters, including: The preset trajectory parameters include: a radial distance attenuation model and a rotation angle change model based on height change; Taking the preset landing point as the center, combining the precise position solution, the radial distance attenuation model, and the rotation angle change model, generating an expected spiral landing trajectory; the expected spiral landing trajectory represents the expected coordinates corresponding to the precise position solution in the platform coordinate system.

7. A vehicle-mounted modular UAV intelligent airport control method according to claim 6, characterized in that, The hierarchical adjustment through inner-loop control and outer-loop control includes: The outer-loop controller generates an expected attitude according to the error between the precise position solution and the expected coordinates; The inner-loop controller generates a motor speed adjustment signal according to the error between the attitude quaternion and the expected attitude.

8. A vehicle-mounted modular UAV intelligent airport control system, which is applied to the vehicle-mounted modular UAV intelligent airport control method described in any one of claims 1-7, and is characterized in that Comprising: A UWB tag module for deploying tags on the vehicle-mounted airport platform and the drone; A coordinate initialization module for establishing a platform coordinate system; A landing guidance module, including: A data acquisition unit, which is used to process the ranging data based on the ranging data of the platform tag and the UAV tag through weighted least squares fitting to obtain a rough solution of the UAV's position; A data processing unit, which is used to combine the rough position solution and the ranging data, and calculate the attitude quaternion of the UAV through multi-layer coupled attitude closed-loop analysis; A data fusion unit, which is used to fuse the rough position solution, the attitude quaternion and the ranging data through extended Kalman filter to determine the precise position of the UAV; A trajectory prediction unit, which is used to generate an expected spiral landing trajectory based on the precise position solution, the preset landing point and the preset trajectory parameters; A motor control unit, which is used to output the control signal of the UAV motor through hierarchical adjustment of inner loop control and outer loop control according to the expected spiral landing trajectory and the precise position solution.

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