Method for estimating attitude of ornithopter flying towards surface of Mars

Abstract

The invention discloses a Mars surface flight-oriented ornithopter attitude estimation method, which comprises the following steps: sensor data acquisition: acquiring original data of a Mars ornithopter through a sensor; sensor data filtering processing: carrying out data filtering processing on the original data acquired by the sensor; preprocessing sensor data, performing complementary filtering processing on the filtered data, and taking an obtained attitude quaternion as an estimation reference of an attitude estimation algorithm; attitude estimation: fusing the preprocessed sensor data by adopting an improved self-adaptive gradient descent algorithm to obtain a final attitude estimation result; and attitude description and updating: updating the attitude of the Mars flapping-wing aircraft by adopting a quaternion method, and converting the attitude into attitude angle information as attitude description. Through data preprocessing and an improved gradient descent algorithm, rapid and accurate convergence of attitude estimation is ensured, and the real-time performance of the system is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of Martian aircraft research and development, and in particular to a method for estimating the attitude of a flapping-wing aircraft flying toward the surface of Mars. Background Art

[0002] Collecting samples from the Martian surface is crucial for conducting in-situ scientific exploration of Mars and returning them to Earth for in-depth scientific analysis. However, current Mars exploration relies primarily on rovers, whose complex and varied topography significantly limits their detection range. To achieve comprehensive and in-depth exploration of Mars, research into other deep space exploration options and technologies is necessary.

[0003] The thin atmosphere on Mars' surface offers a certain degree of potential for aircraft exploration missions. Flapping-wing aircraft boast high aerodynamic efficiency, along with numerous advantages such as compact size, light weight, and the ability to hover, making them suitable for flight exploration missions under various operating conditions. However, unlike conventional aircraft, the high-frequency flapping of their wings during flight can cause the aircraft and onboard sensors to experience severe oscillations, posing significant challenges to attitude estimation.

[0004] Attitude estimation algorithms are a key technology in Mars flapping-wing vehicle control research. Due to the limited size and payload capacity of Mars flapping-wing vehicles, they typically utilize a low-cost, lightweight MEMS-based attitude reference system for body attitude estimation. Due to the limited accuracy of sensors, multi-sensor data fusion is required to improve attitude estimation accuracy. Furthermore, the high-frequency flapping of the flexible wings during Mars flapping-wing vehicle motion introduces difficult-to-filter measurement noise into the sensors, further complicating attitude estimation.

[0005] Therefore, how to provide a method for estimating the attitude of a flapping-wing aircraft flying towards the surface of Mars has become a technical problem that technicians in this field urgently need to solve. Summary of the Invention

[0006] In view of this, the purpose of the present invention is to provide a method for attitude estimation of a flapping-wing aircraft flying on the surface of Mars. Through data preprocessing and an improved gradient descent algorithm, the rapid and accurate convergence of attitude estimation is ensured, thereby ensuring the real-time performance of the system.

[0007] The present invention solves the technical problem by adopting the following technical solutions:

[0008] A method for estimating the attitude of a flapping-wing aircraft flying toward the surface of Mars comprises the following steps:

[0009] S1, sensor data acquisition, obtains the raw data of the Mars flapping-wing aircraft through sensors;

[0010] S2, sensor data filtering processing, performing data filtering processing on the raw data obtained by the sensor;

[0011] S3, sensor data preprocessing, complementary filtering is performed on the filtered data, and the obtained attitude quaternion is used as the estimation reference of the attitude estimation algorithm;

[0012] S4, attitude estimation, uses an improved adaptive gradient descent algorithm to fuse the preprocessed sensor data to obtain the final attitude estimation result;

[0013] S5, attitude description and update, uses the quaternion method to update the attitude of the Mars flapping-wing aircraft and converts it into attitude angle information as attitude description.

[0014] Furthermore, in step S1, the attitude information of the Mars flapping-wing aircraft is collected by a gyroscope and an accelerometer, wherein the gyroscope measures the angular velocity information of the Mars flapping-wing aircraft, and the accelerometer measures the acceleration information of the Mars flapping-wing aircraft.

[0015] Furthermore, in step S2, the raw data acquired by the sensor is filtered using a cascade filter consisting of a Butterworth low-pass filter and a sliding window mean filter.

[0016] Furthermore, the sliding window mean filter calculates the average value within a certain number of sample windows and uses this average value as the output of the current point. When the window moves along the signal, the new average value of the data in the window is calculated to obtain a smoothed signal, which can effectively reduce random noise. Its output is expressed as:

[0017]

[0018] Where y[n] is the output of the sliding window mean filter; x is the raw data from the sensor; n is the time index, indicating the data point currently being processed; N is the window size, which is the length of the sensor data used to calculate the mean; and k is the summation index used to traverse the data within the window.

[0019] Furthermore, the accelerometer and gyroscope use a third-order low-pass Butterworth filter with different cutoff frequencies, and the formula is:

[0020]

[0021]

[0022] Sort it out,

[0023]

[0024] Among them, H a(s) is the filter transfer function in the analog domain; H(z) is the filter transfer function in the discrete domain; s is the complex frequency variable in the Laplace transform, λ c is the cutoff frequency of the analog filter, T s is the sensor signal sampling period; z is the complex variable in the Z transform; Ω c Normalized cutoff frequency for digital filters, usually defined as

[0025] Furthermore, in step S3, complementary filtering is performed on the filtered data of the gyroscope and the accelerometer through a PI controller.

[0026] Furthermore, in step S4, the improved adaptive gradient step size is:

[0027] β=α(ω′(t)*T+▽f g +f g )

[0028] Among them, α is the adaptive rate, ω'(t) is the preprocessed gyroscope data at time t, T is the sampling time, ▽f g is the error gradient, f g is the error size.

[0029] Furthermore, in step S5, the specific method of posture description and update is as follows:

[0030] First, define the coordinate system as follows: the body coordinate system uses the center of gravity of the Mars flapping-wing aircraft as the coordinate origin, the z-axis coincides with the longitudinal axis of the body, and the direction pointing to the head of the body is positive. The x-axis is located in the longitudinal symmetry plane of the body and is perpendicular to the z-axis. The y-axis is determined according to the right-hand screw rule.

[0031] The attitude angles are defined as follows: the yaw angle φ, the pitch angle θ, and the roll angle ψ are collectively referred to as the attitude angles of the Mars flapping-wing aircraft;

[0032] Based on the coordinate system definition, attitude angle definition and their specific transformation process, the Mars flapping-wing aircraft attitude matrix expressed in quaternions is obtained, as shown in the following formula:

[0033]

[0034] Where q = [q0 q1 q2 q3] T is the attitude quaternion of the body coordinate system b relative to the ground coordinate system g.

[0035] The first-order Runge-Kutta method is used to update the attitude quaternion of the Mars flapping-wing aircraft:

[0036]

[0037] in, Represents quaternion multiplication operations; is the attitude quaternion of the body coordinate system b relative to the ground coordinate system g at time t+T; is the attitude quaternion of the body coordinate system b relative to the ground coordinate system g at time t; is the derivative of the attitude quaternion of the body coordinate system b relative to the ground coordinate system g at time t; T is the update period of the attitude quaternion; is the expression of the angular velocity of the body coordinate system b relative to the ground coordinate system g at time t in the body coordinate system b.

[0038] The attitude angle information of the Mars flapping-wing aircraft is calculated by the following formula:

[0039]

[0040] The present invention discloses a method for estimating the attitude of a flapping-wing aircraft flying toward the surface of Mars, which has the following beneficial effects:

[0041] 1) The attitude estimation method for a flapping-wing aircraft flying on the surface of Mars disclosed in the present invention achieves high-precision and high-stability attitude estimation output of a flapping-wing aircraft on Mars through filter design, data preprocessing, and an improved gradient descent algorithm, and can adapt to the strong vibration environment of the flapping-wing aircraft on Mars.

[0042] 2) The attitude estimation method for flapping-wing aircraft flying on the surface of Mars disclosed in the present invention is based on a low-cost MEMS attitude reference system, has small computational complexity, and requires low hardware computing power, which is suitable for the low load requirements of flapping-wing aircraft on Mars.

[0043] 3) The method for attitude estimation of a flapping-wing aircraft flying on the surface of Mars disclosed in the present invention ensures rapid and accurate convergence of attitude estimation through data preprocessing and an improved gradient descent algorithm, thereby ensuring the real-time performance of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 This is a flow chart of a method for estimating the attitude of a flapping-wing aircraft flying toward the surface of Mars according to the present invention;

[0045] Figure 2 Schematic diagram of the coordinate system of the present invention;

[0046] Figure 3 Schematic diagram of the definition and transformation of Euler angles in the present invention;

[0047] Figure 4 Schematic diagram of the posture estimation method of the present invention. DETAILED DESCRIPTION

[0048] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0049] refer to Figure 1 The present invention discloses a method for estimating the attitude of a flapping-wing aircraft flying toward the surface of Mars, comprising the following steps:

[0050] S1, sensor data acquisition, obtains the original data of the Mars flapping-wing aircraft through the sensor; collects the attitude information of the Mars flapping-wing aircraft through the gyroscope and accelerometer, among which the gyroscope measures the angular velocity information of the Mars flapping-wing aircraft and the accelerometer measures the acceleration information of the Mars flapping-wing aircraft.

[0051] S2, sensor data filtering processing, performs data filtering processing on the raw data obtained by the sensor through a cascade filter composed of a Butterworth low-pass filter and a sliding window mean filter.

[0052] The sliding window mean filter calculates the average value within a certain number of sample windows and uses this average value as the output of the current point. When the window moves along the signal, the new average value of the data in the window is calculated to obtain a smoothed signal, which can effectively reduce random noise. Its output is expressed as:

[0053]

[0054] Where y[n] is the output of the sliding window mean filter; x is the raw data from the sensor; n is the time index, indicating the data point currently being processed; N is the window size, which is the length of the sensor data used to calculate the mean; and k is the summation index used to traverse the data within the window.

[0055] The accelerometer and gyroscope use a third-order low-pass Butterworth filter with different cutoff frequencies. The formula is:

[0056]

[0057] Sort it out,

[0058]

[0059] Among them, H a (s) is the filter transfer function in the analog domain; H(z) is the filter transfer function in the discrete domain; s is the complex frequency variable in the Laplace transform, λ c is the cutoff frequency of the analog filter, Ts is the sensor signal sampling period; z is the complex variable in the Z transform; Ω c Normalized cutoff frequency for digital filters, usually defined as

[0060] S3, sensor data preprocessing, uses a PI controller to perform complementary filtering on the filtered data of the gyroscope and accelerometer, and the obtained attitude quaternion is used as an estimation reference for the attitude estimation algorithm.

[0061] S4, attitude estimation, uses an improved adaptive gradient descent algorithm to fuse the sensor data of the accelerometer and the pre-processed and corrected gyroscope to obtain the final attitude estimation result;

[0062] The improved adaptive gradient step size is:

[0063] β=α(ω′(t)*T+▽f g +f g )

[0064] Among them, α is the adaptive rate, ω'(t) is the pre-processed gyroscope data at time t, T is the sampling time, ▽f g is the error gradient, f g is the size of the error.

[0065] S5, attitude description and update, uses the quaternion method to update the attitude of the Mars flapping-wing aircraft and converts it into attitude angle information as attitude description.

[0066] The specific methods for posture description and update are as follows:

[0067] First, define the coordinate system as Figure 2 The specific definitions are as follows: the body coordinate system takes the center of gravity of the Mars flapping-wing aircraft as the coordinate origin, the z-axis coincides with the longitudinal axis of the body, and the direction pointing to the head of the body is positive, the x-axis is located in the longitudinal symmetry plane of the body and is perpendicular to the z-axis, and the y-axis is determined according to the right-hand screw rule;

[0068] The attitude angle is defined as follows and the specific transformation process is as follows Figure 3 (a) Figure 3 (b) with Figure 3 As shown in (c), the yaw angle φ, pitch angle θ and roll angle ψ are collectively referred to as the attitude angle of the Mars flapping-wing aircraft;

[0069] Based on the coordinate system definition, attitude angle definition and their specific transformation process, the Mars flapping-wing aircraft attitude matrix expressed in quaternions is obtained, as shown in the following formula:

[0070]

[0071] Where q = [q0 q1 q2 q3] T is the attitude quaternion of the body coordinate system b relative to the ground coordinate system g.

[0072] The first-order Runge-Kutta method is used to update the attitude quaternion of the Mars flapping-wing aircraft:

[0073] in, Represents quaternion multiplication operations; is the attitude quaternion of the body coordinate system b relative to the ground coordinate system g at time t+T; is the attitude quaternion of the body coordinate system b relative to the ground coordinate system g at time t; is the derivative of the attitude quaternion of the body coordinate system b relative to the ground coordinate system g at time t; T is the update period of the attitude quaternion; is the expression of the angular velocity of the body coordinate system b relative to the ground coordinate system g at time t in the body coordinate system b.

[0074] The attitude angle information of the Mars flapping-wing aircraft is calculated by the following formula:

[0075]

[0076] The method for estimating the attitude of a flapping-wing aircraft flying on the surface of Mars disclosed by the present invention realizes a high-precision and high-stability attitude estimation output of a flapping-wing aircraft flying on the surface of Mars through filter design, data preprocessing and an improved gradient descent algorithm, and can adapt to the strong vibration environment of the flapping-wing aircraft on Mars. The method for estimating the attitude of a flapping-wing aircraft flying on the surface of Mars disclosed by the present invention is based on a low-cost MEMS attitude reference system, with small calculation amount and low hardware computing power requirement, which is adapted to the low load requirement of the flapping-wing aircraft on Mars. The method for estimating the attitude of a flapping-wing aircraft flying on the surface of Mars disclosed by the present invention realizes a high-precision and high-stability attitude estimation output of a flapping-wing aircraft flying on the surface of Mars through filter design, data preprocessing and an improved gradient descent algorithm, and can adapt to the strong vibration environment of the flapping-wing aircraft on Mars.

[0077] Processing and improved gradient descent algorithm ensure fast and accurate convergence of posture estimation and guarantee the real-time performance of the system.

[0078] Example:

[0079] like Figure 4 As shown, the process of the posture estimation method of the present invention is:

[0080] (1) The filtered sensor data and the current posture matrix of the body are known at the current moment

[0081] (2) Set the gravity reference component g = [00g] T Convert to the body coordinate system:

[0082]

[0083] (3) Define the multivariate error function as the acceleration normalized data and The cross product of .

[0084]

[0085] Among them, q is the original attitude quaternion, a is the accelerometer data, is the estimated attitude quaternion, is the estimated acceleration vector.

[0086] (4) Update the accelerometer error integral:

[0087] e a (t+T)=e a (t)+e a *T

[0088] (5) Preprocessing of gyroscope data by PI controller:

[0089] ω′=ω+K p *e a +K I *e a (t+T)

[0090] Among them, K P is the proportional control parameter, K I is the integral control parameter.

[0091] (6) Construct a multivariate error function:

[0092]

[0093] (7) Derivative the multivariate error function and find its Jacobian matrix:

[0094]

[0095] (8) According to the gradient calculation formula:

[0096]

[0097] The gradient expression is as follows:

[0098]

[0099] (9) Calculate the adaptive dynamic step size β, which is positively correlated with the motion angular velocity, sampling time, error, and error change rate.

[0100] β=α(ω′(t)*T+▽f g +f g )

[0101] Among them, α is the adaptive rate, ω'(t) is the pre-processed gyroscope data at time t, T is the sampling time, ▽f g is the error gradient, f g is the size of the error.

[0102] (10) Finally, the attitude quaternion is updated using the gradient descent method based on the preprocessed sensor data and the calculated adaptive dynamic step size:

[0103]

[0104] in, The quaternion of the body coordinate system b relative to the ground coordinate system g estimated at the current moment; The attitude quaternion of the body coordinate system b relative to the ground coordinate system g estimated at the last moment; is the estimated quaternion derivative of the body coordinate system b relative to the ground coordinate system g at the current moment; is the attitude quaternion derivative of the current body coordinate system b relative to the ground coordinate system g obtained based on the preprocessed gyroscope data.

[0105] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for estimating the attitude of a flapping-wing aircraft flying toward the surface of Mars, characterized in that: The process includes the following steps: S1, sensor data acquisition, obtains the raw data of the Mars flapping-wing aircraft through sensors; S2, sensor data filtering processing, performing data filtering processing on the raw data obtained by the sensor; S3, sensor data preprocessing, complementary filtering is performed on the filtered data, and the obtained attitude quaternion is used as the estimation reference of the attitude estimation algorithm; S4, attitude estimation, uses an improved adaptive gradient descent algorithm to fuse the preprocessed sensor data to obtain the final attitude estimation result; S5, attitude description and update, uses the quaternion method to update the attitude of the Mars flapping-wing aircraft and converts it into attitude angle information as attitude description.

2. The method for estimating the attitude of a flapping-wing aircraft flying toward the surface of Mars according to claim 1, characterized in that: In step S1, the attitude information of the Mars flapping-wing aircraft is collected by a gyroscope and an accelerometer, wherein the gyroscope measures the angular velocity information of the Mars flapping-wing aircraft, and the accelerometer measures the acceleration information of the Mars flapping-wing aircraft.

3. The method for estimating the attitude of a flapping-wing aircraft flying toward the surface of Mars according to claim 2, characterized in that: In step S2, the raw data acquired by the sensor is filtered using a cascade filter consisting of a Butterworth low-pass filter and a sliding window mean filter.

4. The method for estimating the attitude of a flapping-wing aircraft flying toward the surface of Mars according to claim 3, wherein: The sliding window mean filter calculates the average value within a certain number of sample windows and uses this average value as the output of the current point. When the window moves along the signal, the new average value of the data in the window is calculated to obtain a smoothed signal, which can effectively reduce random noise. Its output is expressed as: Where y[n] is the output signal of the sliding window mean filter; x is the raw data of the sensor; n is the time index, which indicates the data point currently being processed; N is the window size, that is, the length of the sensor data used to calculate the mean; and k is the summation index used to traverse the data within the window.

5. The method for estimating the attitude of a flapping-wing aircraft flying toward the surface of Mars according to claim 4, characterized in that: The accelerometer and gyroscope use a third-order low-pass Butterworth filter with different cutoff frequencies. The formula is: Arranged: Among them, H a (s) is the filter transfer function in the analog domain; H(z) is the filter transfer function in the discrete domain; s is the complex frequency variable in the Laplace transform, λ c is the cutoff frequency of the analog filter, T s is the sensor signal sampling period; z is the complex variable in the Z transform; Ω c Normalized cutoff frequency for digital filters, usually defined as 6. The method for estimating the attitude of a flapping-wing aircraft flying toward the surface of Mars according to claim 5, characterized in that: In step S3, complementary filtering is performed on the filtered data of the gyroscope and the accelerometer through a PI controller.

7. The method for estimating the attitude of a flapping-wing aircraft flying toward the surface of Mars according to claim 6, characterized in that: In step S4, the improved adaptive gradient step size is: β=α(ω′(t)*T+▽f g +f g ) Among them, α is the adaptive rate, ω'(t) is the pre-processed gyroscope data at time t, T is the sampling time, ▽f g is the error gradient, f g is the size of the error.

8. The method for estimating the attitude of a flapping-wing aircraft flying toward the surface of Mars according to claim 7, characterized in that: In step S5, the specific method of posture description and update is as follows: First, define the coordinate system as follows: the body coordinate system uses the center of gravity of the Mars flapping-wing aircraft as the coordinate origin, the z-axis coincides with the longitudinal axis of the body, and the direction pointing to the head of the body is positive. The x-axis is located in the longitudinal symmetry plane of the body and is perpendicular to the z-axis. The y-axis is determined according to the right-hand screw rule. The attitude angles are defined as follows: the yaw angle φ, the pitch angle θ, and the roll angle ψ are collectively referred to as the attitude angles of the Mars flapping-wing aircraft; Based on the coordinate system definition, attitude angle definition and their specific transformation process, the Mars flapping-wing aircraft attitude matrix expressed in quaternions is obtained, as shown in the following formula: Where q = [q0 q1 q2 q3] T is the attitude quaternion of the body coordinate system b relative to the ground coordinate system g. The first-order Runge-Kutta method is used to update the attitude quaternion of the Mars flapping-wing aircraft: in, Represents quaternion multiplication operations; is the attitude quaternion of the body coordinate system b relative to the ground coordinate system g at time t+T; is the attitude quaternion of the body coordinate system b relative to the ground coordinate system g at time t; is the derivative of the attitude quaternion of the body coordinate system b relative to the ground coordinate system g at time t; T is the update period of the attitude quaternion; is the expression of the angular velocity of the body coordinate system b relative to the ground coordinate system g at time t in the body coordinate system b. The attitude angle information of the Mars flapping-wing aircraft is calculated by the following formula:

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