Vehicle-mounted unmanned aerial vehicle visual servo precise landing control method considering view field constraint

Abstract

The invention discloses a vehicle-mounted unmanned aerial vehicle visual servo precise landing control method considering view field constraints, and the method comprises the following steps: employing virtual plane feature points to form an image moment feature value, and building a decoupling system dynamics model integrating unmanned aerial vehicle dynamics and image dynamics; according to the image shot by the camera at the current moment and the motion state of the unmanned aerial vehicle, acquiring the system state at the current moment; outer ring control and inner ring control are set based on a model prediction control method, an optimization target is set in the outer ring control, attitude constraints are set in the inner ring control, the system state at the subsequent moment is predicted based on the system state at the current moment, and the optimal control at the current moment is obtained to serve as system input; and performing flight control on the unmanned aerial vehicle based on system input. The method disclosed by the invention ensures that the view field visibility is met in the autonomous landing process of the unmanned aerial vehicle.

Description

Technical Field

[0001] The invention relates to a vehicle-mounted unmanned aerial vehicle visual servo precise landing control method considering field of view constraints, and belongs to the technical field of aircraft control. Background Art

[0002] The visual servo method can still obtain navigation information in a GPS-denied environment, and the visual positioning method is a non-contact measurement with good reliability. Therefore, it is often used in autonomous flight control of UAVs.

[0003] Most of the existing visual servo control methods do not consider the constraints of visibility conditions or the impact of the drone's posture on the image, which often causes the target to appear outside the camera's field of view during actual flight, making it impossible to obtain position information relative to the target, resulting in control failure.

[0004] Although there are visual servo control methods that consider visibility constraints in the prior art, for example, the nonlinear model predictive control method (NMPC) is used in the literature [K. Zhang, Y. Shi and H. Sheng, "Robust Nonlinear Model Predictive Control Based Visual Servoing of Quadrotor UAVs," in IEEE / ASME Transactions on Mechatronics, vol. 26, no. 2, pp. 700-708, April 2021, doi: 10.1109 / TMECH.2021.3053267] to achieve robust control of autonomous landing of UAVs, but its visibility constraints are analyzed based on the virtual image plane; the literature [H. Sheng, E. Shi and K. Zhang, "Image-Based Visual Servoing of a Quadrotor with Improved Visibility Using Model Predictive Control," 2019 IEEE 28th International Symposium on Industrial Electronics(ISIE),Vancouver,BC,Canada,2019,pp.551-556,doi:10.1109 / ISIE.2019.8781212】A visual servo controller based on MPC with field of view constraints is designed for UAV landing control. However, directly introducing attitude angle constraints may result in the failure to meet the field of view constraints. It can be seen that the existing visual servo method still cannot guarantee control stability.

[0005] Therefore, it is necessary to conduct more in-depth research on existing visual servoing methods to solve the above problems. Summary of the invention

[0006] In order to overcome the above problems, the inventors conducted in-depth research and proposed a vehicle-mounted UAV visual servo precision landing control method considering field of view constraints, comprising the following steps: S1, using virtual plane feature points to form image moment eigenvalues, and establishing a decoupled system dynamics model integrating UAV dynamics and image dynamics;

[0007] S2. Obtain the current system status based on the image captured by the camera and the motion status of the drone;

[0008] S3. Setting the outer loop control and the inner loop control based on the model predictive control method, wherein the optimization target is set in the outer loop control, and the posture constraint is set in the inner loop control. Based on the system state at the current moment, the system state at the subsequent moment is predicted, and the optimal control at the current moment is obtained as the system input;

[0009] S4. Based on the system input, the UAV is controlled in flight.

[0010] In a preferred embodiment, in S1, the virtual plane is a plane established between the drone and the horizontal plane and parallel to the horizontal plane.

[0011] In a preferred embodiment, the feature point is an intersection point of the edges of any square on the virtual plane.

[0012] In a preferred embodiment, the image moment eigenvalue s is set to

[0013] s=[s 1 ,s 2 ,s 3 ,s 4 ]

[0014]

[0015] Among them, s 1 、s 2 、s 3 、s 4 is an intermediate variable, [u vc ,n vc ] represents the centroid coordinates of the feature point, f is the focal length of the camera, a represents the acceleration of the drone, and a d Desired altitude for the drone The corresponding a value, u r1 Indicates the horizontal coordinate of the first point corresponding to the feature point in the real image, n r1 Indicates the ordinate of the first point corresponding to the feature point in the real image, ur2 Indicates the horizontal coordinate of the second point corresponding to the feature point in the real image, n r2 Indicates the ordinate of the second point corresponding to the feature point in the real image, u rc Indicates the horizontal coordinates of the centroid of the four points corresponding to the feature points in the real image, n rc Indicates the horizontal coordinates of the centroids of the four points corresponding to the feature points in the real image.

[0016] In a preferred embodiment, the image dynamics is expressed as:

[0017]

[0018] in, represents the speed of the UAV in the virtual camera coordinate system, and ψ represents the yaw angle of the UAV.

[0019] In a preferred embodiment, the drone dynamics are expressed as:

[0020]

[0021] Among them, p represents the position of the UAV in the ground system, v represents the speed of the UAV in the ground system, represents the rotation matrix between the drone system and the inertial system, T represents the total lift generated by the blades, and E 3 =[0,0,1] T represents the direction of lift T in the drone system, m represents the mass of the drone, g represents the acceleration due to gravity, [·] × represents the operator that transforms a vector into a skew-symmetric matrix, Ω represents the angular velocity of the UAV, I represents the moment of inertia of the UAV, and τ represents the input torque.

[0022] In a preferred embodiment, the decoupled system dynamics model is expressed as:

[0023]

[0024] Among them, x represents the system state, is the first-order derivative of the system state x, and u represents the system input.

[0025] In a preferred embodiment, the outer loop control is set as follows:

[0026]

[0027] u(k+i|k)∈u set

[0028] in, is the objective function of predictive control, To optimize the index, represents the process cost function, represents the terminal cost function, x d represents the expected system state, x(k+i|k) represents the prediction of the system state at the k+i step based on the system input u(k|k) at the k step; represents the error between the prediction made at step k for step k+i and the expected value, represents the first-order derivative of the prediction made at step k for step k+i, u(k+i|k) represents the system input of step k+i predicted at step k, i∈{0,···,N}, N is the length of the prediction time step interval, u set Indicates the value range of u.

[0029] In a preferred embodiment, the inner loop control is set as follows:

[0030]

[0031] e Ω =Ω-R T R d Ω d

[0032] τ=-k R e R -k Ω e Ω +Ω^JΩ

[0033] θ d =θ-e R (2)

[0034] φ d =φ-e R (1)

[0035] b=[cos(θ),sin(θ),0]′

[0036] Among them, R d is the desired rotation matrix, r d1 , r d2 , r d3 is the component of the desired rotation matrix, b is the intermediate parameter, Ω d is the intermediate matrix of angular velocity, e R is the deviation between the expected attitude angle and the actual attitude angle, e Ω represents the deviation between the expected angular velocity and the actual angular velocity, τ represents the torque of the drone, and k R , k Ω is a settable parameter, J is the moment of inertia, the superscript T represents transpose, and the superscripts ^ and ∨ represent matrix transformation symbols. ^ represents transforming a 3*1 matrix into a 3*3 matrix, and ∨ represents restoring a 3*3 matrix into a 3*1 matrix.

[0037] In a preferred embodiment, the maximum allowable pitch angle θ is set max 、Maximum allowable roll angle φ max To constrain the posture:

[0038] When θ max <θ d When R (2) = -(θ max -θ), otherwise e R (2) is the deviation between the expected pitch angle and the actual pitch angle;

[0039] φ max <φ d When R (1) = -(φ max -φ), otherwise e R (1) is the deviation between the desired roll angle and the actual roll angle.

[0040] The beneficial effects of the present invention include:

[0041] (1) Ensure the visibility of the UAV during autonomous landing;

[0042] (2) High control stability and high landing accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 A schematic flow chart of a method for controlling a vehicle-mounted UAV visual servo precise landing considering field of view constraints according to a preferred embodiment of the present invention is shown;

[0044] Figure 2 The horizontal position change curve of the UAV in the static target landing simulation of Example 1 is shown;

[0045] Figure 3 The vertical position change curve of the UAV in the static target landing simulation of Example 1 is shown;

[0046] Figure 4 The three-dimensional tracking trajectory of the drone landing in the static target landing simulation of Example 1 is shown;

[0047] Figure 5 The change of the X-axis coordinates of the image feature points in the static target landing simulation in Example 1 is shown;

[0048] Figure 6 The pitch angle change of the drone in the static target landing simulation of Example 1 is shown;

[0049] Figure 7 The horizontal position change curve of the UAV in the dynamic target landing simulation of Example 1 is shown;

[0050] Figure 8 The vertical position change curve of the UAV in the dynamic target landing simulation of Example 1 is shown;

[0051] Fig. 9 The three-dimensional tracking trajectory of the drone landing in the dynamic target landing simulation of Example 1 is shown;

[0052] Fig.10 The change of the X-axis coordinates of the image feature points in the dynamic target landing simulation in Example 1 is shown;

[0053] Fig.11 The pitch angle change of the drone in the dynamic target landing simulation of Example 1 is shown;

[0054] Fig.12 The change of the X-axis coordinates of the image feature points in the static target landing simulation in Example 2 is shown;

[0055] Fig.13 The pitch angle change of the drone in the static target landing simulation of Example 2 is shown;

[0056] Fig.14 The change of the X-axis coordinates of the image feature points in the dynamic target landing simulation in Example 2 is shown;

[0057] Fig.15 The change in the pitch angle of the drone in the dynamic target landing simulation of Example 2 is shown. DETAILED DESCRIPTION

[0058] The present invention will be further described in detail below through the accompanying drawings and embodiments. Through these descriptions, the characteristics and advantages of the present invention will become more clear and distinct.

[0059] The word "exemplary" is used exclusively herein to mean "serving as an example, embodiment, or illustration." Any embodiment described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments. Although various aspects of the embodiments are shown in the drawings, the drawings are not necessarily drawn to scale unless otherwise noted.

[0060] According to the present invention, a vehicle-mounted UAV visual servo precise landing control method considering field of view constraints is provided, which realizes the autonomous landing of the UAV for stationary targets and moving targets. Figure 1 As shown, including:

[0061] S1. Using virtual plane feature points to construct image moment eigenvalues, a decoupled system dynamics model integrating UAV dynamics and image dynamics is established;

[0062] S2. Obtain the current system status based on the image captured by the camera and the motion status of the drone;

[0063] S3. Setting the outer loop control and the inner loop control based on the model predictive control method, wherein the optimization target is set in the outer loop control, and the posture constraint is set in the inner loop control. Based on the system state at the current moment, the system state at the subsequent moment is predicted, and the optimal control at the current moment is obtained as the system input;

[0064] S4. Based on the system input, the UAV is controlled in flight.

[0065] In S1, the virtual plane is a plane parallel to the horizontal plane established between the drone and the horizontal plane. In the present invention, by setting the virtual plane, the influence of the drone's posture can be eliminated in the subsequent solution process, reducing variables and thus reducing the amount of calculation.

[0066] In the present invention, the feature point may be an edge line intersection point of any regular figure on a virtual plane.

[0067] In a preferred embodiment, the feature points are the intersection points of the side lines of any square on the virtual plane. The four intersection points of the side lines of the square are easier to locate and perform subsequent calculations than other points.

[0068] The image moment eigenvalue s is set to

[0069] s=[s 1 ,s 2 ,s 3 ,s 4 ]

[0070]

[0071] Among them, s 1 、s 2 、s 3 、s 4 is an intermediate variable, [u vc ,n vc ] represents the centroid coordinates of the feature point, f is the focal length of the camera, a represents the acceleration of the drone, and a d Desired altitude for the drone The corresponding a value, u r1 Indicates the horizontal coordinate of the first point corresponding to the feature point in the real image, n r1 Indicates the ordinate of the first point corresponding to the feature point in the real image, u r2 Indicates the horizontal coordinate of the second point corresponding to the feature point in the real image, n r2 Indicates the ordinate of the second point corresponding to the feature point in the real image, u rc Indicates the horizontal coordinates of the centroid of the four points corresponding to the feature points in the real image, n rc Represents the horizontal coordinates of the centroid of the four points corresponding to the feature points in the real image.

[0072] Furthermore, a = μ 20 +μ 02

[0073] Among them, μ 20 , μ 02 Obtained by the following formula:

[0074]

[0075] Among them, l represents different feature points, represents the horizontal coordinate of the lth feature point, represents the average value of the horizontal coordinates of the four feature points, The ordinate of the lth feature point, Represents the average value of the vertical coordinates of the four feature points, i,j∈[0,2].

[0076] The image dynamics is expressed as:

[0077]

[0078] in, represents the speed of the UAV in the virtual camera coordinate system, and ψ represents the yaw angle of the UAV.

[0079] The virtual camera coordinate system is to preserve the coordinate system F b In F n The coordinate system of the UAV’s rotational motion is not preserved.

[0080] Further, combined with coordinate transformation and drone dynamics, we have:

[0081]

[0082] The UAV dynamics is expressed as:

[0083]

[0084] Among them, p represents the position of the UAV in the ground system, v represents the speed of the UAV in the ground system, represents the rotation matrix between the drone system and the inertial system, T represents the total lift generated by the blades, and E 3 =[0,0,1] T represents the direction of lift T in the drone system, m represents the mass of the drone, g represents the acceleration due to gravity, [·] × represents the operator that transforms a vector into a skew-symmetric matrix, Ω represents the angular velocity of the UAV, I represents the moment of inertia of the UAV, and τ represents the input torque.

[0085] Based on the image moment eigenvalues, the UAV dynamics and the image dynamics, a decoupled system dynamics model can be obtained. The system dynamics model is based on the image moment eigenvalues ​​s and the velocity v of the UAV in the virtual camera coordinate system. v Get the system state, with acceleration and yaw rate as system input.

[0086] The decoupled system dynamics model is expressed as:

[0087]

[0088] Among them, x represents the system state, is the first-order derivative of the system state x, and u represents the system input.

[0089] Furthermore, the system input is expressed as Among them, a x ,a y ,a z are the components of the drone's acceleration in the x, y, and z directions.

[0090] Furthermore, the lift F of the drone can be obtained according to the acceleration: k , expressed as:

[0091]

[0092] Where m is the mass of the drone.

[0093] Compared with the traditional dynamics model, the decoupled system dynamics model in the present invention can directly analyze the system dynamics characteristics based on the virtual plane without considering the influence of the drone posture on the dynamics.

[0094] In S2, the image moment eigenvalue s is obtained according to the image taken by the camera, and the speed of the drone in the virtual camera coordinate system is obtained according to the speed of the drone.

[0095] In S3, model predictive control (MCP) is a commonly used predictive control method. In the present invention, the model predictive control includes outer loop control and inner loop control.

[0096] Wherein, the outer loop control is set as:

[0097]

[0098]

[0099] u(k+i|k)∈u set

[0100] in, is the objective function of predictive control, To optimize the index, represents the process cost function, represents the terminal cost function, x d represents the expected system state, x(k+i|k) represents the prediction of the system state at the k+i step based on the system input u(k|k) at the k step; represents the error between the prediction made at step k for step k+i and the expected value, represents the first-order derivative of the prediction made at step k for step k+i, u(k+i|k) represents the system input of step k+i predicted at step k, i∈{0,···,N}, N is the length of the prediction time step interval, u set It represents the value range of u, and the specific range can be freely set by those skilled in the art according to actual needs and is not limited in the present invention.

[0101] Preferably, the process cost function and the terminal cost function are set to:

[0102]

[0103] Among them, U k To control the queue, is the error queue, expressed as:

[0104] U k = {u k|k T ,u k+1|k T ,……,u k+N|k T} T

[0105]

[0106] The superscript T indicates transpose, and Q, R, and F are configurable matrix parameters.

[0107] In a preferred embodiment, considering the differences in the detection effects of target points at different heights of the camera in the actual scene, the landing process is divided into two stages according to the landing height:

[0108] When the drone is higher than the preset altitude When Q, R, and F are set to Q 1 , R 1 、F 1 ;

[0109] When the drone altitude is lower than the preset altitude When Q, R, and F are set to Q 2 , R 2 、F 2 .

[0110] In the present invention, Q 1 , R 1 、F 1 , Q 2 , R 2 、F 2 There is no limitation on the specific value of , and technicians in this field can freely set it according to actual needs.

[0111] According to the present invention, the inner loop control is set as follows:

[0112]

[0113] e Ω =Ω-R T R d Ω d

[0114] τ=-k R e R -k Ω e Ω +Ω^JΩ

[0115] θ d =θ-e R (2)

[0116] φ d =φ-e R (1)

[0117] b=[cos(θ),sin(θ),0]′

[0118] Among them, R d is the desired rotation matrix, r d1 , r d2 , r d3 is the component of the desired rotation matrix, b is the intermediate parameter, Ω d is the intermediate matrix of angular velocity, e R is the deviation between the expected attitude angle and the actual attitude angle, e Ω represents the deviation between the expected angular velocity and the actual angular velocity, τ represents the torque of the drone, and k R , k Ω is a settable parameter, J is the moment of inertia, the superscript T represents transpose, and the superscripts ^ and ∨ represent matrix transformation symbols. ^ represents transforming a 3*1 vector into a 3*3 matrix, and ∨ represents restoring a 3*3 matrix into a 3*1 vector.

[0119] Examples of superscript ^ operation:

[0120] Such as a=[a 1 ,a 2 ,a 3 ] T , a∧ Then A ∨ Then it is a.

[0121] According to the present invention, the maximum allowable pitch angle θ is set max 、Maximum allowable roll angle φ max To constrain the posture:

[0122] When θ max <θ d When R (2) = -(θ max -θ), otherwise e R (2) is the deviation between the expected pitch angle and the actual pitch angle;

[0123] φ max <φ d When R (1) = -(φ max -φ), otherwise e R (1) is the deviation between the desired roll angle and the actual roll angle.

[0124] Preferably, the maximum allowable pitch angle θ max Obtained through:

[0125]

[0126] in, represents the distance between the image feature point and the image center, cd is the length corresponding to when the feature point is 1, β is the intermediate parameter, x k+1 (1) represents the first element of the system state quantity predicted at step k+1, x k+1 (3) represents the third element of the system state quantity at the predicted k+1th step.

[0127] Preferably, the maximum allowable roll angle φ max Obtained through:

[0128]

[0129] in, is the intermediate parameter, x k+1 (2) represents the second element of the system state quantity at the predicted k+1th step.

[0130] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the disclosure of the present invention can be performed in parallel, sequentially or in different orders, as long as the desired results of the technical solution disclosed in the present invention can be achieved, and this document does not limit this.

[0131] Example

[0132] Example 1

[0133] Conduct UAV visual servo landing control simulation experiments, including:

[0134] S1. Using virtual plane feature points to construct image moment eigenvalues, a decoupled system dynamics model integrating UAV dynamics and image dynamics is established;

[0135] S2. Obtain the current system status based on the image captured by the camera and the motion status of the drone;

[0136] S3. Setting the outer loop control and the inner loop control based on the model predictive control method, wherein the optimization target is set in the outer loop control, and the posture constraint is set in the inner loop control. Based on the system state at the current moment, the system state at the subsequent moment is predicted, and the optimal control at the current moment is obtained as the system input;

[0137] S4. Based on the system input, the UAV is controlled in flight.

[0138] In S1, the virtual plane is a plane parallel to the horizontal plane established between the drone and the horizontal plane, and the image moment eigenvalue s is set to

[0139] s=[s 1 ,s 2 ,s 3 ,s 4 ]

[0140]

[0141] The image dynamics is expressed as:

[0142]

[0143] The UAV dynamics is expressed as:

[0144]

[0145] The decoupled system dynamics model is expressed as:

[0146]

[0147] The system input is expressed as

[0148] The lift force F of the drone k Expressed as

[0149] In S3, the outer loop control is set as:

[0150]

[0151] u(k+i|k)∈u set

[0152] The process cost function and terminal cost function are set as:

[0153]

[0154] The landing process is divided into two stages according to the landing height:

[0155] When the drone is higher than the preset altitude When Q, R, and F are set to Q 1 , R 1 、F 1 ;

[0156] When the drone altitude is lower than the preset altitude When Q, R, and F are set to Q 2 , R 2 、F 2 .

[0157] The inner loop control is set as:

[0158]

[0159]

[0160] e Ω =Ω-R T R d Ω d

[0161] τ=-k R e R -k Ω e Ω +Ω^JΩ

[0162] θ d =θ-e R (2)

[0163] φ d =φ-e R (1)

[0164] b=[cos(θ),sin(θ),0]′

[0165] Set the maximum allowable pitch angle θ max 、Maximum allowable roll angle φ max To constrain the posture:

[0166] When θ max <θ d When R(2) = -(θ max -θ), otherwise e R (2) is the deviation between the expected pitch angle and the actual pitch angle;

[0167] φ max <φ d When R (1) = -(φ max -φ), otherwise e R (1) is the deviation between the desired roll angle and the actual roll angle.

[0168] Maximum allowable pitch angle θ max Obtained through:

[0169]

[0170] Maximum allowable roll angle φ max Obtained through:

[0171]

[0172]

[0173] In the simulation experiment, the coordinates of the four feature points in the virtual plane in the first stage of landing are P 11 =(6,1,0)m,P 12 =(4,1,0)m,P 13 =(4,-1,0)m,

[0174] P 14 =(6,-1,0)m, the preset height is set to Set the expected moment feature a′ d =14.22; In the second stage, the coordinates of the four feature points in the virtual plane are P 21 =(5.1,0.1,0)m,P 22 =(4.9,0.1,0)m,P 23 =(4.9,-0.1,0)m,

[0175] P 24 =(5.1,-0.1,0)m, the preset height is set to The corresponding expected moment feature a d =1.5802; prediction interval N = 3, sampling time dt = 0.001s. The mass m of the drone is 1.8kg, and the moment of inertia is

[0176] J=diag(0.01562,0.01456,0.02492)kgm 2 , g = 9.81 m / s 2, camera parameters f = 0.02m, cd = 0.015m. The weight matrix Q of the first stage 1 =diag(60,60,60,200,1,0.001,0.001,0.001)、R 1 =diag(0.005,0.005,0.005,0.005) and F 1 =diag(5,5,200,1,5,5,5), the weight matrix Q of the second stage 2 =diag(18,18,0.1,18,0.001,0.001,0.001)、R 2 =diag(0.005,0.005,0.005,0.005) and F 2 =diag(3,3,10,3,3,3,3); Set the desired state vector to x d =[0,0,1,0,0,0,0].

[0177] During the simulation, static target landing simulation and dynamic target landing simulation are set. The static target landing simulation results are as follows: Figure 2-Figure 6 As shown,

[0178] Figure 2 The horizontal position change curve of the UAV is shown. Figure 3 The vertical position change curve of the UAV is shown. Figure 4 The three-dimensional tracking trajectory of the drone landing is shown. Figure 2-4 It can be seen that the horizontal and vertical positions of the drone have converged to the desired positions;

[0179] Figure 5 The X-axis coordinate changes of the image feature points are shown. It can be seen from the figure that the outermost feature points do not exceed the field of view, so the field of view visibility constraint is met;

[0180] Figure 6 The figure shows the change of the pitch angle of the drone. It can be seen from the figure that the actual pitch angle is strictly limited to the range of the maximum pitch angle, so the landing meets the attitude angle constraint;

[0181] Dynamic target landing simulation results are as follows Figure 7-Figure 11 As shown,

[0182] Figure 7 The horizontal position change curve of the UAV is shown. Figure 8 The vertical position change curve of the UAV is shown. Fig. 9 The three-dimensional tracking trajectory of the drone landing is shown. Figure 7-9 It can be seen that the horizontal and vertical positions of the drone have converged to the desired positions;

[0183] Fig.10 The X-axis coordinate changes of the image feature points are shown. It can be seen from the figure that the outermost feature points do not exceed the field of view, so the field of view visibility constraint is met;

[0184] Fig.11 The figure shows the pitch angle change of the drone. It can be seen from the figure that the actual pitch angle is strictly limited to the range of the maximum pitch angle, so the landing meets the attitude angle constraint.

[0185] Example 2

[0186] The same experiment as in Example 1 was performed, except that the maximum allowable pitch angle θ was not set. max 、Maximum allowable roll angle φ max Perform posture constraints.

[0187] In the static target landing simulation results, the X-axis coordinates of the image feature points change as follows: Fig.12 As shown, it can be seen from the figure that the position of the feature point is outside the field of view;

[0188] The pitch angle of the drone changes as follows Fig.13 As shown in the figure, it can be seen that the actual pitch angle is not within the range of the maximum allowable pitch angle, and the actual pitch angle and the maximum allowable pitch angle intersect in the initial stage of landing;

[0189] In the dynamic target landing simulation results, the X-axis coordinates of the image feature points change as follows: Fig.14 As shown in the figure, it can be seen that the position of the feature point is outside the field of view, and the position of the feature point in the image reaches the boundary of the field of view at an earlier time, about 0.45s;

[0190] The pitch angle of the drone changes as follows Fig.15 As shown in the figure, it can be seen that the actual pitch angle curve and the maximum allowable pitch angle curve intersect in the initial stage of landing, that is, the field of view visibility condition is not met.

[0191] Respectively compared with Example 1 Figure 5 , Figure 6 , Fig.10 , Fig.11 In Example 2 Fig.12 , Fig.13 , Fig.14 , Fig.15 It can be clearly seen that in Example 1, by setting the maximum allowable pitch angle θ max 、Maximum allowable roll angle φ max Performing attitude constraints can effectively ensure the visibility of the field of view during the autonomous landing of the UAV, and improve the control stability and landing accuracy of the UAV.

[0192] The present invention has been described above in conjunction with preferred embodiments, but these embodiments are only exemplary and serve only as an illustration. On this basis, the present invention may be subjected to a variety of substitutions and improvements, all of which fall within the scope of protection of the present invention.

Claims

1. A method for precise landing control of a vehicle-mounted UAV visual servo system considering field of view constraints, characterized in that: The following steps are involved: S1. Using virtual plane feature points to construct image moment eigenvalues, a decoupled system dynamics model integrating UAV dynamics and image dynamics is established; S2. Obtain the current system status based on the image captured by the camera and the motion status of the drone; S3. Setting the outer loop control and the inner loop control based on the model predictive control method, wherein the optimization target is set in the outer loop control, and the posture constraint is set in the inner loop control. Based on the system state at the current moment, the system state at the subsequent moment is predicted, and the optimal control at the current moment is obtained as the system input; S4. Based on the system input, the UAV is controlled in flight.

2. The method for precise landing control of a vehicle-mounted UAV visual servo considering field of view constraints according to claim 1 is characterized in that: In S1, the virtual plane is a plane established between the drone and the horizontal plane and parallel to the horizontal plane.

3. The method for precise landing control of a vehicle-mounted UAV visual servo considering field of view constraints according to claim 1 is characterized in that: The feature point is the intersection point of the side lines of any square on the virtual plane.

4. The method for precise landing control of a vehicle-mounted UAV visual servo considering field of view constraints according to claim 1 is characterized in that: The image moment eigenvalue s is set to s=[s1,s2,s3,s4] Among them, s1, s2, s3, and s4 are intermediate variables, [u vc ,n vc ] represents the centroid coordinates of the feature point, f is the focal length of the camera, a represents the acceleration of the drone, and a d Desired altitude for the drone The corresponding a value, u r1 Indicates the horizontal coordinate of the first point corresponding to the feature point in the real image, n r1 Indicates the ordinate of the first point corresponding to the feature point in the real image, u r2 Indicates the horizontal coordinate of the second point corresponding to the feature point in the real image, n r2 Indicates the ordinate of the second point corresponding to the feature point in the real image, u rc Indicates the horizontal coordinates of the centroid of the four points corresponding to the feature points in the real image, n rc Represents the horizontal coordinates of the centroid of the four points corresponding to the feature points in the real image.

5. The method for precise landing control of a vehicle-mounted UAV visual servo considering field of view constraints according to claim 1 is characterized in that: The image dynamics is expressed as: in, represents the speed of the UAV in the virtual camera coordinate system, and ψ represents the yaw angle of the UAV.

6. The method for precise landing control of a vehicle-mounted UAV visual servo considering field of view constraints according to claim 1 is characterized in that: The UAV dynamics is expressed as: Among them, p represents the position of the UAV in the ground system, v represents the speed of the UAV in the ground system, represents the rotation matrix between the drone system and the inertial system, T represents the total lift generated by the blades, E3 = [0,0,1] T represents the direction of lift T in the drone system, m represents the mass of the drone, g represents the acceleration due to gravity, [·] × represents the operator that transforms a vector into a skew-symmetric matrix, Ω represents the angular velocity of the UAV, I represents the moment of inertia of the UAV, and τ represents the input torque.

7. The method for precise landing control of a vehicle-mounted UAV visual servo considering field of view constraints according to claim 1 is characterized in that: The decoupled system dynamics model is expressed as: Among them, x represents the system state, is the first-order derivative of the system state x, and u represents the system input.

8. The method for precise landing control of a vehicle-mounted UAV visual servo considering field of view constraints according to claim 1 is characterized in that: The outer loop control is set as: u(k+i|k)∈u set in, is the objective function of predictive control, To optimize the index, represents the process cost function, represents the terminal cost function, x d represents the expected system state, x(k+i|k) represents the prediction of the system state at the k+i step based on the system input u(k|k) at the k step; represents the error between the prediction made at step k for step k+i and the expected value, represents the first-order derivative of the prediction made at step k for step k+i, u(k+i|k) represents the system input of step k+i predicted at step k, i∈{0,···,N}, N is the length of the prediction time step interval, u set Indicates the value range of the input u.

9. The method for precise landing control of a vehicle-mounted UAV visual servo considering field of view constraints according to claim 1, characterized in that: The inner loop control is set as: e Ω =Ω-R T R d Oh d τ=-k R a R -k Ω a Ω +Ω^JΩ i d =θ-e R (2) f d =φ-e R (1) b=[cos(θ),sin(θ),0]′ Among them, R d is the desired rotation matrix, r d1 , r d2 , r d3 is the component of the desired rotation matrix, b is the intermediate parameter, Ω d is the intermediate matrix of angular velocity, e R is the deviation between the expected attitude angle and the actual attitude angle, e Ω represents the deviation between the expected angular velocity and the actual angular velocity, τ represents the torque of the drone, and k R , k Ω is a settable parameter, J is the moment of inertia, the superscript T represents transpose, and the superscripts ^ and ∨ represent matrix transformation symbols. ^ represents transforming a 3*1 vector into a 3*3 matrix, and ∨ represents restoring a 3*3 matrix into a 3*1 vector.

10. The method for precise landing control of a vehicle-mounted UAV visual servo considering field of view constraints according to claim 1, characterized in that: Set the maximum allowable pitch angle θ max 、Maximum allowable roll angle φ max To constrain the posture: When θ max <θ d When R (2) = -(θ max -θ), otherwise e R (2) is the deviation between the expected pitch angle and the actual pitch angle; φ max <φ d When R (1)=-(φ max -φ), otherwise e R (1) is the deviation between the desired roll angle and the actual roll angle.

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