Mode switching control method and device for air-sea amphibious unmanned aerial vehicle

Abstract

The invention discloses a mode switching control method and device for an air-sea amphibious unmanned aerial vehicle, and the method comprises the steps: monitoring the unmanned aerial vehicle through a sensor, and obtaining target collection data; on the basis of an extended Kalman filtering algorithm, state estimation is carried out on the unmanned aerial vehicle through the target acquisition data, and a state estimation result is obtained; monitoring the mode switching state of the unmanned aerial vehicle according to the state estimation result to obtain a state monitoring result; and executing a mode switching strategy according to the state monitoring result to realize mode switching control of the unmanned aerial vehicle. The method can improve the stability and accuracy of mode switching control of the air-sea amphibious unmanned aerial vehicle, and can be widely applied to the technical field of unmanned aerial vehicles.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicles, and in particular to a mode switching control method and device for an air-sea amphibious unmanned aerial vehicle. Background Art

[0002] At present, due to the complex force conditions of air-sea amphibious UAVs in different modes, and the switching of multiple media, such as from air to water, from water to air, etc., the accuracy of describing the force conditions in these different media and the dynamic changes during the mode switching process is not high. In addition, in different modes and during the mode switching process, dynamic adjustments need to be made based on real-time environmental information and its own state information to ensure the stability and control accuracy of the UAV. However, the current control algorithms used to adapt to changes in environmental information and its own state are not very adaptable and robust, which affects the stability and accuracy of the mode switching control of air-sea amphibious UAVs. Summary of the invention

[0003] In view of this, the main purpose of the embodiments of the present invention is to provide a mode switching control method and device for an air-sea amphibious UAV, in order to solve at least one of the problems of the prior art. The present invention can improve the stability and accuracy of the mode switching control of the air-sea amphibious UAV.

[0004] To achieve the above-mentioned purpose, an embodiment of the present invention provides a mode switching control method for an air-sea amphibious UAV, the method comprising:

[0005] Monitor the drone through sensors to obtain target collection data;

[0006] Based on the extended Kalman filter algorithm, the state of the UAV is estimated by collecting data from the target to obtain a state estimation result;

[0007] According to the state estimation result, the mode switching state of the UAV is monitored to obtain a state monitoring result;

[0008] According to the status monitoring result, a mode switching strategy is executed to realize the mode switching control of the UAV.

[0009] In some embodiments, the monitoring of the drone by a sensor to obtain target acquisition data includes the following steps:

[0010] The drone is monitored by a plurality of sensors to obtain a plurality of first collected data;

[0011] Performing sliding average filtering on a plurality of the first collected data to obtain a plurality of second collected data;

[0012] A weighted fusion process is performed on a plurality of the second collected data to obtain the target collected data.

[0013] In some embodiments, performing sliding average filtering on a plurality of the first collected data to obtain a plurality of the second collected data comprises the following steps:

[0014] Acquire a data sequence of each of the sensors;

[0015] According to the data sequence, obtaining a sliding window output;

[0016] According to the sliding window output, the first collected data is subjected to boundary processing to obtain the second collected data.

[0017] In some embodiments, performing weighted fusion processing on a plurality of the second collected data to obtain the target collected data comprises the following steps:

[0018] Obtaining the confidence level of each of the sensors;

[0019] According to the confidence level, obtaining a sensor weight of each of the sensors;

[0020] The second collected data is subjected to weighted fusion processing according to the sensor weight to obtain the target collected data.

[0021] In some embodiments, the state estimation of the UAV is performed based on the extended Kalman filter algorithm by collecting data from the target to obtain a state estimation result, including the following steps:

[0022] Obtaining a system dynamics model of the UAV;

[0023] Predicting the current state of the UAV according to the system dynamics model to obtain a predicted state vector;

[0024] The covariance of the UAV is predicted by using the state transfer Jacobian matrix and the process noise to obtain a predicted state covariance matrix of the attitude of the UAV;

[0025] Collect data according to the target and construct an observation function;

[0026] Constructing a UAV observation model according to the observation function and the observation noise;

[0027] By observing the Jacobian matrix, the Kalman gain is obtained;

[0028] The predicted state vector is updated through the UAV observation model and the Kalman gain to obtain a target state vector;

[0029] The predicted state covariance matrix is ​​updated by the Kalman gain to obtain a target covariance matrix;

[0030] The state estimation result includes the target state vector and the target covariance matrix.

[0031] In some embodiments, monitoring the mode switching state of the drone according to the state estimation result to obtain the state monitoring result includes the following steps:

[0032] According to the state estimation result, obtaining the current position, current speed and current attitude angle of the UAV;

[0033] When the height in the current position is higher than a first state threshold, the vertical speed in the current speed is greater than zero, and the current attitude angle is within a second state threshold, the state monitoring result is a first switching condition for the drone to be in an air mode;

[0034] When the depth in the current position is less than a third state threshold, the vertical speed in the current speed is less than zero, and the current attitude angle is within the second state threshold range, the state monitoring result is a second switching condition that the drone is in the underwater mode;

[0035] When the current attitude angle is within a fourth state threshold range, the state monitoring result is that the UAV is in a transition mode.

[0036] In some embodiments, executing a mode switching strategy according to the state monitoring result to implement mode switching control of the drone includes the following steps:

[0037] According to the status monitoring result, it is determined whether the switching condition is triggered. If the mode switching condition is triggered, the control input of the UAV is adjusted and the dynamic response of the UAV is optimized. If the mode switching condition is not triggered, the UAV is monitored and a preset emergency strategy is executed for the UAV in an abnormal state.

[0038] In some embodiments, adjusting the control input of the drone and optimizing the dynamic response of the drone comprises the following steps:

[0039] Constructing an optimization objective function according to the position error, velocity error, acceleration error, attitude angle error, attitude angular velocity error and weighting coefficient of the UAV;

[0040] By optimizing the objective function, the position error, velocity error, acceleration error, attitude angle error and attitude angular velocity error of the UAV in the mode switching control are minimized;

[0041] Obtain a control gain matrix through a linear quadratic regulator;

[0042] adjusting the control input of the UAV according to the control gain matrix;

[0043] Presetting transition parameters, acceleration feedback gain matrix and angular velocity feedback gain matrix;

[0044] According to the transition parameters, interpolation control is performed to smoothly transition the UAV from the current mode to the target mode;

[0045] Performing vibration suppression control on the UAV through the acceleration feedback gain matrix;

[0046] The attitude torque of the UAV is adjusted by the angular velocity feedback gain matrix.

[0047] To achieve the above-mentioned purpose, another aspect of an embodiment of the present invention provides a mode switching control device for an air-sea amphibious UAV, the device comprising:

[0048] The first module is used to monitor the UAV through sensors to obtain target collection data;

[0049] The second module is used to perform state estimation on the UAV through the target collection data based on the extended Kalman filter algorithm to obtain a state estimation result;

[0050] A third module is used to monitor the mode switching state of the UAV according to the state estimation result to obtain a state monitoring result;

[0051] The fourth module is used to execute the mode switching strategy according to the status monitoring result to realize the mode switching control of the UAV.

[0052] To achieve the above-mentioned purpose, another aspect of an embodiment of the present invention provides an electronic device, which includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, it implements the mode switching control method of the air-sea amphibious drone described above.

[0053] To achieve the above-mentioned purpose, another aspect of an embodiment of the present invention provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the mode switching control method of the air-sea amphibious drone described above.

[0054] To achieve the above-mentioned purpose, another aspect of an embodiment of the present invention provides a computer program product or a computer program, which includes a computer instruction stored in a computer-readable storage medium. A processor of a computer device can read the computer instruction from the computer-readable storage medium, and the processor executes the computer instruction, so that the computer device executes the aforementioned mode switching control method of an air-sea amphibious drone.

[0055] The embodiments of the present invention include at least the following beneficial effects: the present invention provides a mode switching control method and device for an air-sea amphibious UAV, which monitors the UAV through a sensor to obtain target acquisition data; based on an extended Kalman filter algorithm, the state of the UAV is estimated through the target acquisition data to obtain a state estimation result; according to the state estimation result, the mode switching state of the UAV is monitored to obtain a state monitoring result; according to the state monitoring result, a mode switching strategy is executed to realize mode switching control of the UAV, which can improve the stability and accuracy of the mode switching control of the air-sea amphibious UAV. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0057] Figure 1 It is a flow chart of a mode switching control method of an air-sea amphibious UAV provided by an embodiment of the present invention;

[0058] Figure 2 1 is a schematic diagram of the overall steps of the mode switching control method of the air-sea amphibious UAV provided by an embodiment of the present invention;

[0059] Figure 3 It is a schematic diagram of the hardware structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0060] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the embodiments of the present invention, and they are only examples of devices and methods consistent with some aspects of the embodiments of the present invention as detailed in the attached claims.

[0061] It should be noted that, although the functional modules are divided in the system schematic diagram and the logical order is shown in the flow chart, in some cases, the steps shown or described may be performed in a different order than the module division in the system or the flow chart. The terms "first / S100" and "second / S200" in the specification and claims and the above-mentioned drawings may be used to describe various concepts in this article, but unless otherwise specified, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another. For example, without departing from the scope of the embodiment of the present invention, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the words "if" and "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determination".

[0062] The terms "at least one", "multiple", "each", "any", etc. used in the present invention, at least one includes one, two or more, multiple includes two or more, each refers to each of the corresponding multiple, and any refers to any one of the multiple.

[0063] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which the present invention belongs. The terms used herein are only for the purpose of describing the embodiments of the present invention and are not intended to limit the present invention.

[0064] The force conditions of air-sea amphibious UAVs in different modes are complex. Mathematical models can be used to describe their behaviors in air flight, surface navigation, underwater diving and other modes, as well as switching between modes. However, since air-sea amphibious UAVs involve switching between multiple media during mode switching, such as from air to water, from water to air, etc., the accuracy of describing the force conditions in these different media and the dynamic changes during mode switching is not high. In addition, in different modes and during mode switching, dynamic adjustments need to be made based on real-time environmental information and self-state information to ensure the stability and control accuracy of the UAV. However, the current control algorithms used to adapt to environmental information and self-state changes are not very adaptable and robust, which affects the stability and accuracy of mode switching control of air-sea amphibious UAVs.

[0065] In view of this, if Figure 1 As shown, the embodiment of the present invention proposes a mode switching control method for an air-sea amphibious UAV, which may include but is not limited to steps S100 to S400:

[0066] Step S100, monitoring the drone through sensors to obtain target collection data;

[0067] Step S200, based on the extended Kalman filter algorithm, the state of the UAV is estimated by collecting data from the target to obtain a state estimation result;

[0068] Step S300, monitoring the mode switching state of the UAV according to the state estimation result to obtain a state monitoring result;

[0069] Step S400: executing a mode switching strategy according to the status monitoring result to implement mode switching control of the drone.

[0070] In some embodiments, step S100 may include but is not limited to steps S110 to S130:

[0071] Step S110, monitoring the drone through the plurality of sensors to obtain a plurality of first collected data;

[0072] Step S120, performing sliding average filtering on a plurality of the first collected data to obtain a plurality of second collected data;

[0073] Step S130, performing weighted fusion processing on a plurality of the second collected data to obtain the target collected data.

[0074] In step S110 of some embodiments, the drone is monitored by different sensors to obtain the collected data of multiple drones. Optionally, the air-sea amphibious drone is monitored by sensors such as air pressure sensors, inertial sensors (IMU), global positioning systems (GPS), sonars, etc., to obtain information such as the battery power, communication signal strength, position, attitude, speed, height or depth of the air-sea amphibious drone, and environmental perception data such as temperature, humidity, water depth, and water flow speed can also be obtained.

[0075] In some embodiments, in step S120 to step S130, the data collected by the sensor is fused. Optionally, the sensor data is processed by a sliding average filter algorithm to reduce the interference of random noise on subsequent fusion, and the collected data is fused using a weighted fusion method to obtain target collected data. By fusing data from different sensors, the accuracy and reliability of the data are improved, providing a more accurate decision-making basis for the central controller.

[0076] In some embodiments, step S120 may include but is not limited to steps S121 to S123:

[0077] Step S121, obtaining a data sequence of each of the sensors;

[0078] Step S122, obtaining a sliding window output according to the data sequence;

[0079] Step S123: performing boundary processing on the first collected data according to the sliding window output to obtain the second collected data.

[0080] In steps S121 to S122 of some embodiments, for each sensor j (such as an air pressure sensor, IMU, GPS, sonar, etc.), the data sequence The output of the sliding window is:

[0081]

[0082] in, represents the data of sensor j at sampling point t; Represents the data sequence of the sensor; j represents the sensor; i=tk represents the starting point of the summation operation; t is the index of the current time point, indicating the position where the smoothing result is being calculated in the sliding window processing; k is the half width of the sliding window, indicating that k data points are taken forward and backward from the current time point t; N is the size of the sliding window, indicating the number of data points contained in the sliding window in each smoothing calculation; M represents the total number of sampling points of the sensor data sequence.

[0083] In step S123 of some embodiments, boundary processing is performed on the first collected data collected by the air-sea amphibious drone according to the sliding window output:

[0084]

[0085] When the sliding window exceeds the starting boundary of the data, only the collected data from the data points 1 to t+k can be taken; when the sliding window exceeds the end of the data, only the collected data from the data points t+k to M can be taken.

[0086] In some embodiments, step S130 may include but is not limited to steps S131 to S133:

[0087] Step S131, obtaining the confidence level of each sensor;

[0088] Step S132, obtaining a sensor weight of each of the sensors according to the confidence level;

[0089] Step S133: performing weighted fusion processing on the second collected data according to the sensor weight to obtain the target collected data.

[0090] In steps S131 to S133 of some embodiments, the sensor data (i.e., the second collected data) after the sliding average filtering process is weighted fused to generate a unified environmental perception value to obtain the target collected data. Exemplarily, the weight of the sensor is set by the reliability of the sensor or the task requirement, and the sensor weight can be dynamically adjusted by the confidence method, optionally based on the variance estimation:

[0091]

[0092] In the formula, ω (j) Represents the sensor weight, which is used to represent the contribution of its data to the fusion result. The higher the weight, the more reliable the sensor data is. represents the variance of sensor j data, which is used to measure the noise level. The larger the variance, the more sensor noise there is, and the smaller the sensor weight is. S represents the total number of sensors. For example, if there are air pressure sensors, sonars, and IMUs, there are S = 3 sensors in total. is a summation term that normalizes the inverse of the noise of all sensors to ensure that the sum of all weights is 1.

[0093] The calculation formula for the variance of sensor j data is:

[0094]

[0095] In the formula, represents the average value of sensor j data.

[0096] According to the sensor weight ω (j) , weighted fusion processing is performed on the second collected data to obtain the fused perception data, that is, the target collected data:

[0097]

[0098] In the formula,

[0099] Among them, z t Represents the target acquisition data, which is the environmental perception result at the t-th sampling point, which is the weighted average result of all sensor filtering data.

[0100] In step S200 of some embodiments, the state estimation step based on the extended Kalman filter (EKF) includes predicting and updating the state vector and covariance matrix of the drone. By combining the prediction and observation data, the EKF can effectively reduce the uncertainty of the state estimation, improve the accuracy of the state estimation, and thus improve the navigation and control accuracy of the drone. In addition, the EKF can handle nonlinear systems, and through linearization processing, it is suitable for the dynamic model of the drone. Based on the relatively efficient calculation of the EKF, the state estimation can be quickly updated in real-time applications, which is suitable for the application of drones in dynamic environments.

[0101] In some embodiments, step S200 may include but is not limited to steps S210 to S280:

[0102] Step S210, obtaining a system dynamics model of the UAV;

[0103] Step S220, predicting the current state of the UAV according to the system dynamics model to obtain a predicted state vector;

[0104] Step S230, performing covariance prediction on the drone through the state transfer Jacobian matrix and process noise to obtain a predicted state covariance matrix of the attitude of the drone;

[0105] Step S240, constructing an observation function according to the target collection data;

[0106] Step S250, constructing a UAV observation model according to the observation function and the observation noise;

[0107] Step S260, obtaining the Kalman gain by observing the Jacobian matrix;

[0108] Step S270, updating the predicted state vector through the drone observation model and the Kalman gain to obtain a target state vector;

[0109] Step S280, updating the predicted state covariance matrix through the Kalman gain to obtain a target covariance matrix;

[0110] The state estimation result includes the target state vector and the target covariance matrix.

[0111] In step S210 of some embodiments, the state vector of the air-sea UAV includes key state variables, such as position, speed, attitude angle (roll, pitch, yaw) and its rate of change, etc., then the state vector x of the air-sea UAV is k It can be expressed as:

[0112]

[0113] Among them, x k ,y k 、z k Indicates the position of the drone in three-dimensional space; v x 、v y 、v z represents the speed of the UAV; φ, θ, ψ represent the attitude angles of the UAV, which are roll angle, pitch angle, and yaw angle, respectively; p, q, r represent the roll angular velocity, pitch angular velocity, and yaw angular velocity, respectively.

[0114] Predicting the attitude change of the UAV by extended Kalman filtering Assuming that the attitude and angular velocity of the air-sea UAV follow the following nonlinear dynamic equations, the following system dynamics model is obtained:

[0115] x k =f(x k-1 ,u k-1 )+w k-1

[0116] Among them, the nonlinear state transfer function f can be expressed as:

[0117]

[0118] In the formula, Δt represents the time interval; u k-1 represents the control input at time step k-1 (including the thrust, steering gear and other control variables of the drone); w k-1 represents the process noise at time step k-1, which follows a Gaussian distribution

[0119] In step S220 of some embodiments, the current state of the drone can be predicted based on the system dynamics model, and the predicted state vector is obtained as:

[0120]

[0121] in, represents the predicted state vector at time step k; Represents the predicted state vector at time step k-1. and control input u k-1 , and the state transfer function f to predict the current state The predicted state vector is the prior estimate at time step k, which provides prior information for the subsequent update steps. In the subsequent update steps, the observed data at the current moment will be used to correct this predicted state vector to obtain a more accurate state estimate.

[0122] In step S230 of some embodiments, the state transition Jacobian matrix Fk-1 and the process noise covariance matrix Q k-1 , the covariance prediction of the UAV is performed to update the uncertainty of the state estimation, and the predicted state covariance matrix of the UAV's attitude can be obtained as:

[0123]

[0124] in, represents the predicted state covariance matrix at time step k; P k-1 represents the predicted state covariance matrix at time step k-1; F k-1 represents the state transition Jacobian matrix at time step k-1; (·) T represents the transpose operation; Q k-1 represents the process noise covariance matrix at time step k-1.

[0125] In steps S240 to S250 of some embodiments, the air-sea drone observation model is represented by the relationship between the sensor measurement value (such as GPS, IMU, sonar) and the state vector, that is, the relationship between the target acquisition data and the state vector. Exemplarily, the observation function is usually linearized by the measurement function of the sensor, and the observation function represents the nonlinear relationship between the measurement value and the state variable. The following drone observation model is constructed through the observation function and observation noise:

[0126] z k =h(x k )+v k

[0127] Among them, z k represents the observation value vector at time step k; h(·) represents the observation function; v k represents observation noise, which follows a Gaussian distribution

[0128] In step S260 of some embodiments, the Kalman gain is calculated by observing the Jacobian matrix to adjust the state estimation, and the following expression is obtained:

[0129]

[0130] In the formula,

[0131] Among them, H k Represents the observation Jacobian matrix, which indicates that the observation function h is in the state vector The linearized form at R k represents the observation noise covariance matrix.

[0132] In step S270 of some embodiments, the predicted state vector is corrected and updated through the drone observation model and the Kalman gain, and the target state vector can be obtained as:

[0133]

[0134] in, represents the target state vector at time step k; represents the predicted state vector at time step k; Represents the measurement residual, which is the difference between the actual observed value and the predicted observed value.

[0135] In step S280 of some embodiments, the predicted state covariance matrix is ​​updated by the Kalman gain, thereby updating the uncertainty of the state estimation, and the target covariance matrix can be obtained as:

[0136]

[0137] Among them, P k represents the target covariance matrix at time step k; represents the predicted state covariance matrix at time step k; I represents the identity matrix.

[0138] In some embodiments, in the air-sea UAV autonomous switching system, mode switching state monitoring is to determine whether it is necessary to switch between different modes (such as air mode, transition mode and underwater mode) by evaluating key state parameters (such as speed, depth, attitude, environmental perception data, etc.) in real time. First, it is necessary to define and evaluate the key state parameters of the UAV, which can be obtained through the state estimation step. The position includes the position x of the air-sea UAV in three-dimensional space. k ,y k 、z k , where in the underwater mode z k is the depth of the air-sea drone, in air mode z k is the height of the air-sea UAV; the speed includes the speed v of the air-sea UAV in three-dimensional space x 、v y 、v z ; The attitude angles include the roll angle φ, pitch angle θ, and yaw angle ψ. The motion state of the drone can also be further evaluated based on the IMU data to obtain the drone's acceleration, angular velocity and other data.

[0139] In some embodiments, step S300 may include but is not limited to steps S310 to S340:

[0140] Step S310, obtaining the current position, current speed and current attitude angle of the UAV according to the state estimation result;

[0141] Step S320, when the height in the current position is higher than the first state threshold, the vertical speed in the current speed is greater than zero, and the current attitude angle is within the second state threshold range, the state monitoring result is the first switching condition that the UAV is in the air mode;

[0142] Step S330, when the depth in the current position is less than a third state threshold, the vertical speed in the current speed is less than zero, and the current attitude angle is within the second state threshold range, the state monitoring result is a second switching condition that the drone is in the underwater mode;

[0143] Step S340: When the current attitude angle is within a fourth state threshold range, the state monitoring result is that the UAV is in a transition mode.

[0144] In step S310 of some embodiments, in the mode switching monitoring, the key state parameters in the state estimation result are used to determine whether the working mode should be switched. The determination rule can be based on a state threshold or a model judgment, for example, based on position, speed, depth (or height), attitude angle, etc.

[0145] In step S320 of some embodiments, the switching condition of the air-sea UAV in the air mode is: the altitude of the UAV in the current position is higher than the first state threshold Threshold air , the vertical velocity is greater than zero v z >0, and the attitude angles φ, θ, ψ change slightly, that is, they remain within a certain threshold, satisfying |φ k ∣ <Threshold φ ,|θ k ∣ <Threshold θ , |ψ k ∣ <Threshold ψ .

[0146] In step S330 of some embodiments, the switching condition of the air-sea UAV in the underwater mode is: the depth of the current position of the UAV is less than the third state threshold, i.e., z k <Threshold sea , the vertical velocity is less than zero v z <0, and the posture is stable, that is, |φ k |<Threshold φ ,|θ k |<Threshold θ ,|ψ k |<Threshold ψ , the drone is considered to be in underwater mode.

[0147] In step S340 of some embodiments, if the posture angle changes dramatically, that is, |φ k |>Threshold φ ,|θ k |>Threshold θ ,|ψ k |>Threshold ψ , indicating that the drone may be entering the transition zone between the air and underwater modes. Combined with the change in attitude angle, the mode may switch frequently between the air and underwater modes. Multi-dimensional evaluation (such as speed, acceleration, depth, etc.) is required to avoid misjudgment. transition It is the speed threshold of the transition mode, which is used to determine whether the current state is close enough to the switching condition. When it is close enough to the switching condition, the switch from air to sea can be performed.

[0148] In some embodiments, step S400 may include but is not limited to determining whether a switching condition is triggered based on the status monitoring result; if the mode switching condition is triggered, adjusting the control input of the UAV and optimizing the dynamic response of the UAV; if the mode switching condition is not triggered, monitoring the UAV and executing a preset emergency strategy for the UAV in an abnormal state.

[0149] In step S400 of some embodiments, the mode switching of the air-sea UAV requires a smooth transition in different media (air and seawater), which requires adjusting the control input according to the current state to ensure the stability and safety of the UAV. Assuming the sea level is zero, when the altitude is greater than zero, it means that the UAV is in air mode, and when it is lower than zero, it means that the UAV enters underwater mode, wherein the thrust and attitude control torque smoothly transition according to the altitude change, and smoothly switches from the air mode (z>0) to the underwater mode (z<0) to the air-sea switching transition mode. Among them, the state monitoring module (state estimation based on Kalman filter prediction) is used to determine whether to trigger the switch:

[0150]

[0151] During the switching process, the control input of the drone is adjusted to ensure a smooth transition of attitude, speed, and position. Assume that the control input of the drone is u = [T, τ], where T is the total thrust and τ = [τ φ , τ θ , τ ψ ] is the control torque of the posture.

[0152] The thrust T can be adjusted dynamically according to the medium in which the air-sea drone is located:

[0153] 1) Air Mode

[0154] T=m(g+a z )

[0155] Where m represents the mass of the drone; g represents the acceleration due to gravity; a z Represents the vertical acceleration of the drone.

[0156] 2) Sea Mode

[0157] T=m(g+a z )-ρVg

[0158] Where ρ represents the density of water; V represents the displacement volume of the UAV.

[0159] The goal of adjusting the attitude angle is to maintain a smooth transition of the drone. An attitude controller (such as a PID controller or an LQR controller) can be used to adjust the attitude angle, and the following expression is available:

[0160]

[0161] Where θ = [φ, θ, ψ] T

[0162] Among them, θ represents the current posture angle; θ desired represents the desired attitude angle; K p , K d represents the corresponding control gain matrix; Indicates the current attitude angular velocity; Indicates the desired attitude angular velocity.

[0163] Use the acceleration and velocity controller to adjust the vertical velocity v z To meet the target switching speed, the following expression is obtained:

[0164] a z =K v (v z，desired -v z )+K a (a z，desired -a z )

[0165] Among them, v z Indicates the vertical speed of the target, which decreases gradually when switching in the air and increases gradually when switching underwater; v z，desired represents the expected vertical speed; a z，desired represents the expected vertical acceleration; K v , K a represents the corresponding control gain matrix.

[0166] Using different resistance models of the medium during the switching process, the state equation of the drone is dynamically updated:

[0167] 1) Aerial model:

[0168]

[0169] Among them, A air and B air They represent the state transfer matrix and control input matrix in the air mode respectively. The calculation formulas of thrust and attitude control torque are adapted to the dynamics of the air mode. Specifically, the thrust T in the air mode is air It can be determined by the following formula:

[0170] T air =m(g+a z )

[0171] Attitude control torque T air Can be designed according to dynamic requirements in air mode.

[0172] 2) Underwater model:

[0173]

[0174] Among them, A sea and B sea They represent the state transfer matrix and control input matrix in underwater mode respectively, and are defined according to the characteristics of medium resistance coefficient, buoyancy coefficient, etc.

[0175] 3) Transition mode: During the transition from the air mode to the underwater mode, the transition coefficient λ will smoothly transition between 0 and 1, affecting the state transfer matrix A mode and the control input matrix B mode . Therefore, the state-space model for the transition mode will be of the following form:

[0176]

[0177] Among them, A λ and B λ are the state transfer matrix and control input matrix in transition mode, which are the weighted averages of the air mode and the underwater mode, respectively. The expressions are as follows:

[0178] A λ =(1-λ)A air +λA sea

[0179] B λ =(1-λ)B air +λB sea

[0180] The transition coefficient λ can be dynamically adjusted according to the altitude of the drone to ensure a smooth transition, and the following expression is available:

[0181]

[0182] Where h represents the current altitude of the drone; z max Indicates the maximum diving depth of the drone.

[0183] When the drone is above the sea level, λ is close to 0, and when the drone is underwater, λ is close to 1. Therefore, the state transfer and control input matrix in the transition mode can smoothly transition from the air mode to the underwater mode.

[0184] In some embodiments, to avoid sudden changes in the switching point, an interpolation method can be used to smoothly switch:

[0185] u transition =(1-λ)u current +λu target

[0186] Where λ represents the transition coefficient, which increases linearly with time t, λ∈[0,1]; u transition represents the control input in the transition mode, indicating the control quantity actually applied during the mode switching process (such as thrust T, attitude control torque τ, etc.); u current Represents the control input in the current mode, indicating the control input of the drone in the current mode (air mode or underwater mode); u target Represents the control input in target mode, indicating the control input that the drone should use in target mode (air mode or underwater mode).

[0187] In some embodiments, if the mode switching condition is triggered, the steps of adjusting the control input of the drone and optimizing the dynamic response of the drone may include but are not limited to steps S401 to S408:

[0188] Step S401, constructing an optimization objective function according to the position error, velocity error, acceleration error, attitude angle error, attitude angular velocity error and weighting coefficient of the drone;

[0189] Step S402, minimizing the position error, velocity error, acceleration error, attitude angle error and attitude angular velocity error of the UAV in mode switching control by optimizing the objective function;

[0190] Step S403, obtaining a control gain matrix through a linear quadratic regulator;

[0191] Step S404, adjusting the control input of the UAV according to the control gain matrix;

[0192] Step S405, presetting transition parameters, acceleration feedback gain matrix and angular velocity feedback gain matrix;

[0193] Step S406, performing interpolation control on the UAV to smoothly transition from the current mode to the target mode according to the transition parameters;

[0194] Step S407, performing vibration suppression control on the UAV through the acceleration feedback gain matrix;

[0195] Step S408: adjusting the attitude torque of the UAV through the angular velocity feedback gain matrix.

[0196] In some embodiments, in steps S401 to S408, during the mode switching process of the air-sea UAV, optimizing the dynamic response of the UAV is the key, the goal is to reduce the impact and vibration during the switching, and ensure a smooth transition of the attitude, speed and position of the UAV. Specifically, by monitoring the state of the UAV in real time, it is determined whether the switching point has been reached:

[0197]

[0198] By optimizing the control input, the errors of position, velocity and acceleration, as well as the changes of attitude angle and angular velocity during the switching process are minimized.

[0199] In some embodiments, in step S401 to step S402, according to the position error p of the drone error , speed error v error , acceleration error a error 、Attitude angle error θ error 、Attitude angular velocity error And weighted coefficients w1, w2, w3, w4, w5, construct the optimization objective function. Through this optimization objective function, the position error, velocity error, acceleration error, attitude angle error and attitude angular velocity error of the UAV in mode switching control can be minimized.

[0200] There is the following expression for the optimization objective function J:

[0201]

[0202] In the formula, p error =p desiredr -p;

[0203] v error =v desired -v;

[0204] a error =a desired -a;

[0205] θ error =θ desired -θ;

[0206]

[0207] Where J represents the optimization objective function; w1, w2, w3, w4, w5 represent the weighting coefficients; p error represents the position error; p desiredr represents the expected position; p represents the current position; v error represents the velocity error; v desired represents the expected speed; v represents the current speed; a error represents the acceleration error; a desired represents the expected acceleration; a represents the current acceleration; θ error represents the attitude angle error; θ desired represents the desired attitude angle; θ represents the current attitude angle; Represents the attitude angular velocity error; Represents the desired attitude angular velocity; Represents the current attitude angular velocity; t start and t end Represents the start time and end time of the time interval; t end Represents the end time of integration, that is, the end time of the system control process; t start Represents the starting time of integration, that is, the time when the system starts to execute the control process.

[0208] In steps S403 to S404 of some embodiments, the optimal control gain matrix K is calculated by a linear quadratic regulator (LQR) or other optimization methods. According to the control gain matrix, the control input of the UAV can be adjusted, and the control input expression of the optimized air-sea UAV is:

[0209] u optimal =-Kx error

[0210] Among them, u optimal represents the optimized control input, including thrust and attitude torque; K represents the optimal control gain matrix; x error Represents the state error vector, including errors in position, velocity, acceleration, attitude angle, and angular velocity.

[0211] In steps S405 to S408 of some embodiments, during the switching process, the control input smoothly transitions from the current mode to the target mode, and interpolation control of the smooth transition is required, and the following expression is obtained:

[0212] u transition=(1-λ)u current +λu target

[0213] By introducing acceleration feedback to suppress vibration, the UAV can be subjected to vibration suppression control, and the following expression can be obtained:

[0214] u adjusted =u opt i mal -K a a error

[0215] Among them, K a Represents the acceleration feedback gain matrix.

[0216] By adjusting the attitude torque of the drone to reduce the angular velocity change caused by vibration, the following expression is obtained:

[0217]

[0218] Among them, τ adjusted represents the adjusted attitude torque; τ optimal Represents the optimal attitude control torque of the UAV under ideal conditions; K θ Represents the angular velocity feedback gain matrix.

[0219] In some embodiments, if there is no trigger mode switching condition, the drone is monitored and a preset emergency strategy is executed for the drone in an abnormal state. When the air and sea drone is performing a mission, real-time monitoring of the drone status and triggering emergency strategies based on abnormal conditions are the key to ensuring the safe operation of the drone. Real-time status monitoring specifically includes real-time monitoring of the drone status through multi-sensor fusion technology, including position, speed, attitude, acceleration, battery power, etc. Monitored state vector:

[0220] x=[x,y,z,v x , v y , v z ,φ,θ,ψ,a x , a y , a z , E b ]

[0221] Among them, x, y, z represent the position of the drone; v x , v y , v z represents the speed of the drone; φ, θ, ψ represent the attitude angles (roll, pitch, yaw) of the drone; a x , a y , a z Represents the acceleration of the drone; Eb Indicates the remaining battery power.

[0222] In some embodiments, state anomalies can be detected by setting thresholds or using statistical and model-based methods, while defining a safety range for each state variable. For example, the safety range is as follows:

[0223]

[0224] Among them, x min and x max Represent the minimum and maximum values ​​of the state variables respectively.

[0225] In some optional embodiments, the state prediction of the extended Kalman filter is combined to calculate the state residual r and perform residual abnormality detection, and the following expression is obtained:

[0226] r = x measured -x predicted

[0227] Among them, the abnormal conditions are:

[0228] ∥r∥>∈

[0229] In the formula, x measured Represents the sensor measurement status; x predicted represents the Kalman filter prediction state; ∈ represents the allowed residual threshold.

[0230] In some optional embodiments, the state abnormality is detected by the dynamic model, and the dynamic model abnormality detection is performed. For example, based on the current state x of the UAV, the control input u and the time t, the expected state change rate of the UAV is predicted by the dynamic model f(x, u, t); the actual measured state change rate is measured by a sensor (such as IMU, GPS, etc.); according to the expected state change rate and the actual measured state change rate, the dynamic model error is obtained, and the following expression is obtained:

[0231]

[0232] in, represents the expected rate of state change (i.e., the expected state derivative); represents the actual measured state change rate, reflecting the real motion state of the UAV; f(x, u, t) represents the UAV dynamics model; e dynamic represents the dynamic model error; δ represents the abnormal threshold, which is used to determine whether the error of the state change rate exceeds the tolerance range. If the error is greater than the threshold δ, it is considered that an abnormality has occurred, which may be caused by factors such as sensor failure, model failure or external disturbance.

[0233] In some embodiments, when an anomaly is detected, an emergency strategy is triggered, a corresponding emergency strategy is executed, and an emergency priority is assigned according to the anomaly type, wherein the priority includes:

[0234] 1) Low priority: slight deviation (adjusting posture, position);

[0235] 2) Medium priority: signal loss, low battery;

[0236] 3) High priority: major anomalies (such as attitude loss of control, system failure).

[0237] The emergency actions corresponding to the above priorities include:

[0238] 1) Return to the starting point: p target =p home , where p target represents the target position (return to the take-off point), p home Represents the take-off point position;

[0239] 2) Emergency landing and ascent strategy:

[0240] The goal of emergency landing is to safely land the drone from its current working state (such as in the air or under the sea) to the ground or water surface. To ensure safety, the control strategy must be adjusted according to the current environment of the drone (air mode, sea mode or underwater mode). The following expression is used for (drone landing on the ground or sea surface):

[0241] z target =z land

[0242] In the formula, z target represents the target height (ground); z land Represents the ground height.

[0243] Among them, when performing tasks in air mode, if an emergency occurs (such as failure, loss of control, etc.), the drone needs to respond quickly and switch modes. Emergency landing switches modes according to different environments (such as air mode → sea mode):

[0244] T sea =m(g-ρVg)

[0245] Where, T sea Represents the thrust in sea mode after switching.

[0246] In underwater mode, the drone may need to float up through airbags or other buoyancy devices in an emergency so that it can successfully reach the water surface and perform an emergency ascent. If the drone is currently in underwater mode (when performing a submarine mission) and encounters an emergency (such as system failure, mission interruption, etc.), it may need to float up through buoyancy devices such as airbags to ensure successful arrival at the sea surface from underwater. Airbags and underwater thrust can be used to make the drone float up. In underwater mode, the airbag device can be activated to increase the buoyancy of the drone by injecting gas, thereby overcoming the buoyancy of the water and pulling the drone to the sea surface. The key to the ascent process is to adjust the inflation volume of the airbag and accurately control the buoyancy to ensure a smooth ascent.

[0247] In underwater mode, the thrust calculation of the drone needs to take into account the density and buoyancy of water. If the thruster is required to provide power for buoyancy, the thrust T underwater The calculation formula is:

[0248] T underwater =m(g+a z )+ρVg

[0249] Among them, a z represents underwater acceleration; V represents the displacement volume of the drone; ρ represents the density of water.

[0250] 3) Power system stop:

[0251] T=0,τ=0

[0252] That is, stop the thrust T and attitude torque τ.

[0253] In summary, the processing flow of a mode switching control method for an air-sea amphibious UAV according to an embodiment of the present invention is as follows: Figure 2 As shown:

[0254] 1.1 Sensor data collection and fusion, real-time monitoring of drone battery power, communication signal strength, temperature, humidity and other information through multiple sensors;

[0255] 1.2 Based on the state estimation of the extended Kalman filter, the state of the UAV is estimated using the collected data;

[0256] 1.3 Mode switching status monitoring: judging whether the drone can safely switch to another mode based on the status estimation results;

[0257] 1.4 The air-sea UAV mode switching strategy based on the state monitoring results includes:

[0258] 1.4.1 Switching process control to ensure smooth transition of air-sea UAVs from air flight mode to underwater navigation mode;

[0259] 1.4.2 Switching stability strategy to optimize the stability and performance of the drone during mode switching;

[0260] 1.4.3 Security monitoring and emergency strategy, monitor the security status during mode switching and implement emergency measures when necessary.

[0261] The embodiment of the present invention further provides a mode switching control device for an air-sea amphibious UAV, which can implement a mode switching control method for an air-sea amphibious UAV, and the device includes:

[0262] The first module is used to monitor the UAV through sensors to obtain target collection data;

[0263] The second module is used to perform state estimation on the UAV through the target collection data based on the extended Kalman filter algorithm to obtain a state estimation result;

[0264] A third module is used to monitor the mode switching state of the UAV according to the state estimation result to obtain a state monitoring result;

[0265] The fourth module is used to execute the mode switching strategy according to the status monitoring result to realize the mode switching control of the UAV.

[0266] It can be understood that the contents of the above method embodiments are all applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0267] The embodiment of the present invention further provides an electronic device, which includes a processor and a memory, wherein the memory stores a computer program, and when the processor executes the computer program, the mode switching control method of the air-sea amphibious drone described above is implemented. The electronic device can be any intelligent terminal including a tablet computer, a vehicle-mounted computer, etc.

[0268] It can be understood that the contents of the above method embodiments are all applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0269] refer to Figure 3 , Figure 3 The hardware structure of an electronic device of another embodiment is illustrated, and the electronic device includes:

[0270] The processor 501 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present invention;

[0271] The memory 502 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 502 can store an operating system and other application programs. When the technical solution provided in the embodiments of this specification is implemented by software or firmware, the relevant program code is stored in the memory 502, and the processor 501 calls and executes a mode switching control method for an air-sea amphibious UAV according to an embodiment of the present invention;

[0272] Input / output interface 503, used to implement information input and output;

[0273] Communication interface 504, used to realize communication interaction between the device and other devices, which can be realized by wired mode (such as USB, network cable, etc.) or wireless mode (such as mobile network, WIFI, Bluetooth, etc.);

[0274] A bus 505 that transmits information between the various components of the device (e.g., the processor 501, the memory 502, the input / output interface 503, and the communication interface 504);

[0275] The processor 501 , the memory 502 , the input / output interface 503 and the communication interface 504 are connected to each other in communication within the device via the bus 505 .

[0276] An embodiment of the present invention further provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the above-mentioned mode switching control method of an air-sea amphibious unmanned aerial vehicle.

[0277] It can be understood that the contents of the above method embodiments are all applicable to the present storage medium embodiments, the functions specifically implemented by the present storage medium embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0278] The embodiment of the present invention also provides a computer program product or a computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device can read the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the aforementioned mode switching control method of an air-sea amphibious UAV.

[0279] In some selectable embodiments, the function / operation mentioned in the block diagram may not occur in the order mentioned in the operation diagram. For example, depending on the function / operation involved, the two boxes shown in succession can actually be executed substantially simultaneously or the boxes can sometimes be executed in reverse order. In addition, the embodiment presented and described in the flow chart of the present invention is provided by way of example, for the purpose of providing a more comprehensive understanding of technology. The disclosed method is not limited to the operation and logic flow presented herein. Selectable embodiments are expected, wherein the order of various operations is changed and the sub-operation of a part for which is described as a larger operation is performed independently.

[0280] In addition, although the present invention is described in the context of functional modules, it should be understood that, unless otherwise specified, one or more of the functions and / or features described may be integrated into a single physical device and / or software module, or one or more functions and / or features may be implemented in separate physical devices or software modules. It is also understood that a detailed discussion of the actual implementation of each module is unnecessary for understanding the present invention. More specifically, in view of the properties, functions, and internal relationships of the various functional modules in the device disclosed herein, the actual implementation of the module will be understood within the conventional skills of the engineer. Therefore, those skilled in the art can implement the present invention set forth in the claims without excessive experimentation using ordinary techniques. It is also understood that the specific concepts disclosed are merely illustrative and are not intended to limit the scope of the present invention, which is determined by the full scope of the appended claims and their equivalents.

[0281] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0282] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0283] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0284] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0285] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0286] Although the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the claims and their equivalents.

[0287] The above is a specific description of the preferred implementation of the present invention, but the present invention is not limited to the described embodiments. Those skilled in the art may make various equivalent modifications or substitutions without violating the spirit of the present invention. These equivalent modifications or substitutions are all included in the scope defined by the claims of the present invention.

Claims

1. A mode switching control method for an air-sea amphibious UAV, characterized in that: The following steps are involved: Monitor the drone through sensors to obtain target collection data; Based on the extended Kalman filter algorithm, the state of the UAV is estimated by collecting data from the target to obtain a state estimation result; According to the state estimation result, the mode switching state of the UAV is monitored to obtain a state monitoring result; According to the status monitoring result, a mode switching strategy is executed to realize the mode switching control of the UAV.

2. The mode switching control method of an air-sea amphibious UAV according to claim 1 is characterized in that: The method of monitoring the drone by using a sensor to obtain target collection data includes the following steps: The drone is monitored by a plurality of sensors to obtain a plurality of first collected data; Performing sliding average filtering on a plurality of the first collected data to obtain a plurality of second collected data; A weighted fusion process is performed on a plurality of the second collected data to obtain the target collected data.

3. The mode switching control method of an air-sea amphibious UAV according to claim 2 is characterized in that: The step of performing sliding average filtering on the plurality of first collected data to obtain a plurality of second collected data comprises the following steps: Acquire a data sequence of each of the sensors; According to the data sequence, obtaining a sliding window output; According to the sliding window output, the first collected data is subjected to boundary processing to obtain the second collected data.

4. The mode switching control method of an air-sea amphibious UAV according to claim 2 is characterized in that: The step of performing weighted fusion processing on a plurality of the second collected data to obtain the target collected data comprises the following steps: Obtaining the confidence level of each of the sensors; According to the confidence level, obtaining a sensor weight of each of the sensors; The second collected data is subjected to weighted fusion processing according to the sensor weight to obtain the target collected data.

5. The mode switching control method of an air-sea amphibious UAV according to claim 1 is characterized in that: The method of performing state estimation on the UAV by collecting data from the target based on the extended Kalman filter algorithm to obtain a state estimation result includes the following steps: Obtaining a system dynamics model of the UAV; Predicting the current state of the UAV according to the system dynamics model to obtain a predicted state vector; The covariance of the UAV is predicted by using the state transfer Jacobian matrix and the process noise to obtain a predicted state covariance matrix of the attitude of the UAV; Collect data according to the target and construct an observation function; Constructing a UAV observation model according to the observation function and the observation noise; By observing the Jacobian matrix, the Kalman gain is obtained; The predicted state vector is updated through the UAV observation model and the Kalman gain to obtain a target state vector; The predicted state covariance matrix is ​​updated by the Kalman gain to obtain a target covariance matrix; The state estimation result includes the target state vector and the target covariance matrix.

6. The mode switching control method of an air-sea amphibious UAV according to claim 1 is characterized in that: The method of monitoring the mode switching state of the UAV according to the state estimation result to obtain the state monitoring result includes the following steps: According to the state estimation result, obtaining the current position, current speed and current attitude angle of the UAV; When the height in the current position is higher than a first state threshold, the vertical speed in the current speed is greater than zero, and the current attitude angle is within a second state threshold, the state monitoring result is a first switching condition for the drone to be in an air mode; When the depth in the current position is less than a third state threshold, the vertical speed in the current speed is less than zero, and the current attitude angle is within the second state threshold range, the state monitoring result is a second switching condition that the drone is in the underwater mode; When the current attitude angle is within a fourth state threshold range, the state monitoring result is that the UAV is in a transition mode.

7. The mode switching control method of an air-sea amphibious UAV according to claim 1 is characterized in that: The method of executing a mode switching strategy according to the state monitoring result to realize the mode switching control of the drone includes the following steps: According to the status monitoring result, it is determined whether the switching condition is triggered. If the mode switching condition is triggered, the control input of the UAV is adjusted and the dynamic response of the UAV is optimized. If the mode switching condition is not triggered, the UAV is monitored and a preset emergency strategy is executed for the UAV in an abnormal state.

8. The mode switching control method of an air-sea amphibious UAV according to claim 7 is characterized in that: The step of adjusting the control input of the UAV and optimizing the dynamic response of the UAV comprises the following steps: Constructing an optimization objective function according to the position error, velocity error, acceleration error, attitude angle error, attitude angular velocity error and weighting coefficient of the UAV; By optimizing the objective function, the position error, velocity error, acceleration error, attitude angle error and attitude angular velocity error of the UAV in the mode switching control are minimized; Obtain a control gain matrix through a linear quadratic regulator; adjusting the control input of the UAV according to the control gain matrix; Presetting transition parameters, acceleration feedback gain matrix and angular velocity feedback gain matrix; According to the transition parameters, interpolation control is performed to smoothly transition the UAV from the current mode to the target mode; Performing vibration suppression control on the UAV through the acceleration feedback gain matrix; The attitude torque of the UAV is adjusted by the angular velocity feedback gain matrix.

9. A mode switching control device for an air-sea amphibious UAV, characterized in that: include: The first module is used to monitor the UAV through sensors to obtain target collection data; The second module is used to perform state estimation on the UAV through the target collection data based on the extended Kalman filter algorithm to obtain a state estimation result; A third module is used to monitor the mode switching state of the UAV according to the state estimation result to obtain a state monitoring result; The fourth module is used to execute the mode switching strategy according to the status monitoring result to realize the mode switching control of the UAV.

10. An electronic device, characterized in that: including a processor and a memory; The memory is used to store programs; The processor executes the program to implement the method according to any one of claims 1 to 8.

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