Unmanned aerial vehicle accurate attitude control method integrated with multiple sensors and flight controller of unmanned aerial vehicle accurate attitude control method

Abstract

The invention relates to the technical field of unmanned aerial vehicle control, in particular to an unmanned aerial vehicle accurate attitude control method fusing multiple sensors and a flight controller thereof. The method comprises the following steps: acquiring the position and attribute characteristic data of an unmanned aerial vehicle through multiple sensors; determining the offset degree of the statistical period and the offset direction of the unmanned aerial vehicle according to the position difference between the unmanned aerial vehicle position and the planned route in the previous statistical period, the unmanned aerial vehicle direction and the planned route angle; attitude angles are obtained through attitude calculation, the attitude angles form a fluctuation curve, the fluctuation degree is analyzed, and the attitude stability degree of the unmanned aerial vehicle is obtained; the deviation of the unmanned aerial vehicle relative to the initial position is obtained according to the fluctuation curve slope of the yaw angle; comparing the direction with the offset direction to determine a correction angle; and calculating a three-axis error signal through the correction angle, and inputting the three-axis error signal into a PID (Proportion Integration Differentiation) controller for control. A more accurate attitude angle is obtained, so that the control precision is improved.

Description

Technical Field

[0001] This application relates to the field of unmanned aerial vehicle (UAV) control technology, specifically to a method for precise attitude control of UAVs that integrates multiple sensors and its flight controller. Background Technology

[0002] Drones have been widely used in numerous fields such as aerial photography, agricultural plant protection, material delivery, and environmental monitoring. During drone flight, the accuracy of its attitude control directly affects flight stability and mission performance. Traditional drone attitude control largely relies on a single sensor, such as a gyroscope or accelerometer. However, single sensors have significant limitations. Gyroscopes develop drift errors over extended periods, leading to decreased accuracy in attitude data. Accelerometers are susceptible to external acceleration, and measurements can deviate significantly during rapid drone maneuvers or vibrations, making precise attitude control difficult. While some technologies attempt to use multiple sensors, these often involve simple data aggregation, failing to fully explore the potential of multi-sensor data fusion and thus unable to meet the high-precision attitude control requirements of drones in complex environments.

[0003] Currently, when fusing multi-sensor data for precise UAV attitude control, data fusion algorithms are typically used to filter and fuse the multi-sensor data. This filtered and fused data is then used as the system input for the controller, employing a cascaded PID control algorithm to control and stabilize the UAV's attitude. However, during flight, factors such as vibration, rapid maneuvers, and weather conditions can cause UAVs to deviate from their planned flight paths. Therefore, existing methods for UAV attitude control based on multi-sensor data cannot achieve a balance between keeping the UAV on its planned flight path and precise attitude control. Summary of the Invention

[0004] To address the technical problem of conflict between control accuracy and planned flight path operation caused by UAV deviation from its heading, this application provides a precise attitude control method for UAVs integrating multiple sensors and its flight controller. The specific technical solution adopted is as follows: This application proposes a method for precise attitude control of a UAV that integrates multiple sensors and its flight controller. The method includes the following steps: The drone's location and attribute data are collected using multiple sensors; The system presets a statistical period. The distance between the drone's position and the planned flight path in the previous statistical period at the current moment is used as the deviation degree from the corresponding position on the planned flight path. The system also obtains the offset degree between the drone and the planned flight path based on the difference between the angle and the right angle between the drone and the planned flight path, as well as the degree of deviation. Finally, the system weights the offset degree by considering the distance between the drone's position and the planned flight path at all times within the previous statistical period; the closer the distance, the lower the weight. The system compares the drone's position at the last moment of the statistical period with the planned flight path to obtain the drone's offset direction. After filtering all attribute feature data, attitude calculation is performed to obtain attitude angles, including pitch angle, yaw angle, and roll angle; a fluctuation curve is obtained for each attitude angle in the previous cycle at the current moment; the fluctuation degree and slope of the fluctuation curve are obtained based on the mean difference between adjacent data in the fluctuation curve and the slope of the data points; the attitude stability of the UAV is obtained based on the fluctuation degree of the three attitude angles and the difference between the maximum fluctuation degree and the minimum fluctuation degree. The deviation of the UAV relative to its initial position is obtained by the slope of the yaw angle fluctuation curve; the deviation is combined with the UAV's offset direction and the degree of offset and attitude stability to determine the correction angle. The three-axis error signals are calculated using the corrected yaw angle, pitch angle, and roll angle, and then input into the PID controller for control.

[0005] In the aforementioned scheme, this application proposes a method for precise attitude control of a UAV integrating multiple sensors and its flight controller. By designing a flight controller for precise attitude control of the UAV, the degree and direction of the UAV's deviation from the planned flight path are obtained by analyzing the positional relationship between the UAV and the planned flight path in the previous statistical period. Then, the fluctuation curves of the three attitude angles in the previous statistical period are calculated to determine the fluctuation degree of each attitude angle, thereby obtaining the stability of the UAV's attitude during flight in the previous statistical period. Finally, based on the degree of change in the yaw angle and the direction of deviation, the degree of correction for the UAV's yaw angle in the next statistical period is determined. This results in a more accurate attitude angle in the next statistical period, ensuring that the UAV achieves precise attitude control while operating on the planned flight path.

[0006] In one embodiment, the attribute feature data includes the UAV's triaxial linear acceleration, triaxial angular velocity, triaxial magnetic field strength, atmospheric pressure, and the difference between dynamic and static pressure.

[0007] In one embodiment, the method of using the distance between the UAV's position and the planned flight path in the previous statistical period at the current moment as the degree of deviation of the UAV from the corresponding position on the planned flight path is as follows: For a given location of a drone, the shortest Euclidean distance from that location to the planned flight path is taken as the degree of deviation between that location and the corresponding location on the planned flight path.

[0008] In one embodiment, the method for obtaining the degree of deviation between the UAV and the planned flight path based on the difference between the angle and the right angle between the UAV and the planned flight path, and the degree of deviation, is as follows: The angle between the drone's flight direction and the planned flight path is recorded as the positive angle. The degree of offset is positively correlated with the degree of deviation, the difference in the return angle, and the difference in the right angle.

[0009] In one embodiment, the method for obtaining the offset degree of the statistical period by weighting the distance between the drone's location and its position at all times in the previous statistical period as a weight is as follows: Calculate the Euclidean distance between each location and the current location of the drone, and sort all locations in descending order of Euclidean distance to obtain the location sequence; The expression for the degree of offset is: , This represents the u-th index in the position sequence. This indicates the degree of offset at the position corresponding to the u-th index. Indicates the number of elements in the position sequence. This indicates the degree of deviation in the statistical period.

[0010] In one embodiment, the degree of fluctuation of the fluctuation curve is the absolute value of the average difference between adjacent data points; the slope of the fluctuation curve is the average of the slopes of all data points therein.

[0011] In one embodiment, the degree of attitude stability is negatively correlated with the difference between the degree of attitude angle fluctuation, the maximum degree of fluctuation, and the minimum degree of fluctuation.

[0012] In one embodiment, the method for obtaining the UAV's yaw relative to its initial position based on the slope of the yaw angle fluctuation curve is as follows: When the slope of the yaw angle fluctuation curve is positive, the UAV's overall flight direction in the previous statistical period is to the left relative to the initial position; otherwise, it is to the right relative to the initial position.

[0013] In one embodiment, the method for determining the correction angle by combining the bias with the offset direction of the UAV and the degree of offset and attitude stability is as follows: When the drone's deviation from the planned flight path is the same as the drone's deviation from its initial position in the previous statistical period, a correction is required. The correction method is as follows: , Indicates the degree of deviation in the statistical period. Y represents the attitude stability of the drone, and Y represents the preset correction angle. This indicates the correction angle for the yaw angle in the next statistical period; Conversely, no correction is needed.

[0014] Secondly, embodiments of this application also provide a drone flight controller that integrates multiple sensors, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any of the above-described drone precise attitude control methods that integrate multiple sensors.

[0015] The beneficial effects of this application are as follows: This application designs a flight controller for precise attitude control of a UAV. In actual control, it analyzes the positional relationship between the UAV and the planned flight path in the previous statistical period to obtain the degree and direction of the UAV's deviation from the planned path. Then, by calculating the fluctuation curves of three attitude angles in the previous statistical period, it determines the fluctuation degree of each attitude angle, thereby obtaining the stability of the UAV's attitude during flight in the previous statistical period. Finally, based on the degree of change in the yaw angle and the direction of deviation, it determines the degree of correction for the UAV's yaw angle in the next statistical period. This results in more accurate attitude angles in the next statistical period, ensuring precise attitude control of the UAV while it operates on the planned flight path. Attached Figure Description

[0016] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a flowchart of a method for precise attitude control of a UAV that integrates multiple sensors, provided in one embodiment of this application. Figure 2 A diagram illustrating the deviation of the drone from its planned course. Detailed Implementation

[0018] To further illustrate the technical means and effects adopted by this application to achieve the intended inventive objective, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of the multi-sensor-integrated UAV precise attitude control method and its flight controller proposed in this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0020] A method for precise attitude control of UAVs integrating multiple sensors and an example of its flight controller: The following description, in conjunction with the accompanying drawings, details the specific scheme of the multi-sensor fusion UAV precise attitude control method provided in this application.

[0021] Please see Figure 1 The diagram illustrates a flowchart of a multi-sensor fusion method for precise attitude control of a UAV, according to an embodiment of this application. The method includes the following steps: Step S001: Collect the location and attribute feature data of the UAV through multiple sensors.

[0022] The drone collects various data and its location information using multiple sensors.

[0023] In this embodiment, the three-axis linear acceleration, three-axis angular velocity, three-axis magnetic field strength, atmospheric pressure, and dynamic-to-static pressure difference of the UAV are collected using an accelerometer, gyroscope, magnetic compass, barometer, and airspeed meter, respectively. The UAV's position information during flight is acquired using a GNSS receiver. The UAV's three-axis linear acceleration, three-axis angular velocity, three-axis magnetic field strength, atmospheric pressure, and dynamic-to-static pressure difference are used as the UAV's attribute characteristic data. In this embodiment, the data acquisition frequency is 50 Hz.

[0024] At this point, data and location information of the drone were obtained through different sensors.

[0025] Step S002: Based on the positional difference between the UAV and the planned flight path in the previous statistical period, the UAV's direction and the angle between the planned flight path determine the degree of deviation in the statistical period and the direction of UAV deviation.

[0026] Drones typically have a pre-planned flight path before takeoff. When the drone flies along the planned path, only precise attitude control needs to be considered. When the drone deviates significantly from the planned path, its attitude needs to be adjusted to return to the planned path, which requires a higher cost in terms of precise attitude control. Therefore, it is essential to first determine whether the drone is on the correct flight path.

[0027] A preset statistical period is used to obtain the degree of deviation of the drone from the planned flight path within the previous statistical period at the current moment; specifically: For the i-th position in the previous statistical period First, calculate the deviation between the i-th position and the corresponding position on the planned route. Where the i-th position is at the corresponding position on the planned route, as shown below... Figure 2 As shown.

[0028] For a given location of the UAV, the perpendicular line drawn from that location to the planned flight path intersects with the planned flight path at a point. This intersection point is the location corresponding to the planned flight path. The Euclidean distance between the intersection point and the UAV's current location is calculated as the degree of deviation between the UAV's current location and the corresponding location on the planned flight path.

[0029] Since a large deviation exists between the drone's current position and the planned flight path, the closer the drone's heading is to the planned flight path, the more it indicates that the drone is moving in that direction. Therefore, the corresponding i-th position... The smaller the deviation from the planned flight path, the better. The flight control system obtains the current flight direction of the UAV and records the angle between the UAV's flight direction and the planned flight path as the normalization angle. If the normalization angle is 90 degrees, it means that the UAV is currently flying straight along the planned flight path. In other words, the stronger the UAV's initiative in correcting the positional deviation of the flight path, the smaller the deviation of the i-th position from the planned flight path.

[0030] The degree of deviation between the UAV and the planned flight path is obtained based on the degree of deviation and the difference between the return angle and the right angle.

[0031] The degree of offset is positively correlated with the degree of deviation, the difference in the return angle, and the difference in the right angle.

[0032] It should be noted that positive correlation means that when one variable increases, the other variable also increases, and the two variables change in the same direction. When one variable changes from large to small or from small to large, the other variable also changes from large to small or from small to large. The specific relationship is determined by the actual application, and this application does not impose any special restrictions.

[0033] Preferably, in this embodiment, the expression for the degree of offset is: , This indicates the degree of deviation at the i-th position of the drone. This represents the positive angle at the i-th position of the drone. Represents the normalization function. This indicates the degree of offset of the i-th position of the drone.

[0034] For the current moment, calculate the offset of the location at each moment in the previous statistical period. Use the distance of each location from the current moment as a weight to calculate the offset of the statistical period. Then, within the statistical period, calculate the Euclidean distance between each location and the current position of the UAV. Sort all locations in descending order of Euclidean distance to obtain a position sequence. The smaller the sequence number, the farther the location is from the current moment.

[0035] Preferably, the expression for the degree of shift in the statistical period is: , This indicates the degree of offset at the position corresponding to the u-th index. This represents the u-th index in the position sequence. Indicates the number of elements in the position sequence. This indicates the degree of shift in the statistical period. The closer it is to the current position, the greater its weight, and thus the weighted average is used to obtain the degree of shift from the previous statistical period.

[0036] When the deviation of the statistical period exceeds the deviation threshold, it indicates that the drone was not on the correct flight path in the previous statistical period. In this embodiment, the deviation threshold is 0.5.

[0037] If the drone is not on the correct flight path, then during subsequent precise attitude control of the drone, it is necessary to consider all the controls required for the drone to return to the planned flight path. Conversely, if the drone is on the correct flight path, then it is not necessary to consider all the controls required for the drone to return to the planned flight path during subsequent precise attitude control. Simultaneously, the horizontal coordinate of the last position coordinate is used to determine whether it is to the left or right compared to the planned flight path. This yields the drone's offset direction.

[0038] At this point, the offset degree of the drone in the previous cycle was obtained, and the offset direction of the drone was obtained accordingly.

[0039] Step S003: Attitude calculation to obtain attitude angles, and use the attitude angles to form a fluctuation curve to analyze the degree of fluctuation and obtain the attitude stability of the UAV.

[0040] Due to manufacturing processes and the excitation of the corresponding engine and aerodynamic forces during flight, the real-time data acquired from multiple sensors will contain noise data.

[0041] Therefore, the data acquired from multiple sensors is filtered to avoid the influence of noise data on the subsequent UAV attitude calculation results. The filter used in this embodiment is an IIR filter. Filtering data is a well-known technique and will not be described in detail here.

[0042] Attitude calculation refers to the process of obtaining the three attitude angles (pitch, yaw, and roll) of an aircraft through mathematical calculations using attitude sensor data such as gyroscopes, accelerometers, and magnetic compasses. Due to the limited measurement accuracy of sensors, it is impossible to accurately measure the three attitude angles of an aircraft using only one sensor. To fully utilize the measurement data from each sensor to obtain accurate measurements, a complementary filtering method is first used to calculate the three attitude angles of the UAV. At each moment, the three attitude angles are obtained through input data, with the input to the algorithm being the collected attribute feature data. The complementary filtering algorithm is a well-known algorithm and will not be elaborated upon in this application.

[0043] Among them, pitch angle indicates the degree of tilt of the drone's nose up or down, yaw angle indicates the change in heading when the drone turns left or right, and roll angle indicates the degree of tilt of the drone's fuselage to the left or right.

[0044] Then, based on the calculation results and the degree of deviation of the UAV from the planned route, corresponding PID commands are generated to precisely control the attitude of the UAV.

[0045] For the previous statistical period at the current moment, the angle fluctuation curve of each attitude angle during the statistical period is obtained; since the pitch angle controls the drone's head-up or head-down movement, the more stable the pitch angle change, the more stable the drone's flight process, and the less likely it is to experience vertical vibration; similarly, the more stable the yaw and roll angle changes, the more stable the drone's flight.

[0046] Therefore, for each angular fluctuation curve, the difference between adjacent data points is calculated, and the absolute value of the average of all differences is taken as the fluctuation degree of that angular fluctuation curve. Then, for that angular fluctuation curve, the slope of each data point is obtained by differentiation, and the average of all slopes is taken as the slope of the angular fluctuation curve. This yields the fluctuation degree and slope of each angular fluctuation curve.

[0047] The attitude stability of the UAV is obtained by measuring the degree of fluctuation of the three attitude angles and the difference between the maximum and minimum fluctuation.

[0048] The degree of attitude stability is negatively correlated with the degree of fluctuation of attitude angle, the difference between the maximum fluctuation and the minimum fluctuation.

[0049] It should be noted that negative correlation means that when one variable increases, the other variable decreases accordingly, and the two variables change in opposite directions. When one variable changes from large to small or from small to large, the other variable also changes from small to large or from large to small. The specific relationship is determined by practical application, and this application does not impose any special restrictions.

[0050] Preferably, in this embodiment, the expression for the degree of attitude stability is: , This indicates the degree of fluctuation of the j-th attitude angle. This represents the maximum fluctuation of the three attitude angles. This represents the minimum fluctuation of the three attitude angles. Represents the normalization function. This indicates the degree of attitude stability of the drone.

[0051] in The smaller the value, the smaller the fluctuation of the overall attitude angle during the drone's flight. A smaller value indicates a smaller difference in the degree of fluctuation among the three attitude angles. This corresponds to a greater degree of attitude stability of the UAV during flight in the previous statistical period.

[0052] At this point, the attitude stability of the UAV in the previous statistical period at the current moment has been obtained.

[0053] Step S004: Obtain the yaw angle relative to the initial position of the UAV based on the slope of the yaw angle fluctuation curve; compare it with the offset direction to determine the correction angle.

[0054] To determine the UAV's positional characteristics during flight in the previous statistical period, specifically, a positive slope on the pitch angle fluctuation curve indicates that the UAV's overall flight altitude was increasing compared to its initial position in the previous statistical period, while a negative slope indicates a decrease. Similarly, a positive slope on the yaw angle fluctuation curve indicates that the UAV's overall flight direction was shifted to the left relative to its initial position in the previous statistical period, while a negative slope indicates a shift to the right. Finally, a positive slope on the roll angle fluctuation curve indicates that the UAV's fuselage was shifted to the left relative to its initial position in the previous statistical period, while a negative slope indicates a shift to the right.

[0055] When the drone's deviation from the planned flight path is in the same direction as the drone's deviation from its initial position in the previous statistical period, it indicates that the drone is moving further away from the prescribed course. This means that a yaw angle correction is needed. The corresponding degree of deviation at this point is... The larger the value, the better. The larger the value, the higher the stability of the drone's attitude during flight, and the greater the deviation from the planned route. In this case, the correction for the yaw angle can be increased. Anyway, if... A smaller value indicates that the drone's flight stability was poor in the previous statistical period. Therefore, in order to improve stability during flight, the degree of yaw angle correction should be relatively smaller.

[0056] Conversely, when the drone's deviation from the planned flight path differs from the drone's deviation from its initial position in the previous statistical period, it indicates that the drone is getting closer to the prescribed heading. Maintaining the current yaw angle, the drone will gradually approach the planned flight path. Therefore, no yaw angle correction is needed at this time.

[0057] Therefore, the angle is corrected based on the degree of drone offset and attitude stability in the previous statistical period.

[0058] The correction method is as follows: , Indicates the degree of deviation in the statistical period. Y represents the attitude stability of the drone, and Y represents the preset correction angle. This represents the correction angle for the yaw angle in the next statistical period. The value of Y is the maximum correction angle of 5, meaning the maximum correction is 5 degrees.

[0059] At this point, the corrected yaw angle for the next statistical period has been obtained.

[0060] Step S005: Calculate the triaxial error signal by correcting the angle, and input it into the PID controller for control.

[0061] In the next statistical period, a correction angle is added to the existing yaw angle to obtain the yaw angle for the next period's UAV. The pitch and roll angles are determined based on the controller's observation and experience to determine if corrections are needed. Then, the three-axis error signals for the three attitude angles are calculated (using existing, known attitude error calculation methods). Each attitude error signal is then input into its own PID controller to obtain the output control signal (e.g., the pitch error signal, after PID calculation, outputs as an elevator deflection signal or a motor differential speed command). This allows for precise attitude control of the UAV through PID control.

[0062] One embodiment of this application also provides a multi-sensor fusion drone flight controller, the hardware of which includes: High-performance processor: Employs an STM32H7 processor, providing powerful computing capabilities.

[0063] Multi-sensor fusion: Integrates sensors such as accelerometers, gyroscopes, magnetic compasses, barometers, and airspeed meters to achieve precise attitude control.

[0064] High-reliability interface: The J30J aerospace-grade connector is used, ensuring high interface reliability.

[0065] Integrated design: The built-in wide-voltage power supply module, GNSS receiver (providing high-precision positioning information), and airspeed meter module simplify the composition of the UAV electronic control system and improve the system's reliability.

[0066] Dual-antenna system: Built-in UM982 dual-antenna RTK module, eliminating magnetic compass interference problem, while supporting RTK to achieve centimeter-level control accuracy.

[0067] In addition to the flight controller, the flight control system also includes the ZK-4Q navigation and flight control system. The ZK-4Q is a highly integrated, high-reliability next-generation navigation and flight control system designed for industrial-grade UAVs. Tested according to the People's Republic of China's national military standards (National Military Standard), the ZK-4Q has passed rigorous environmental adaptability and screening tests, supporting various types of aircraft including compound wings, fixed wings, multi-rotors, and helicopters. The ZK-4Q uses industrial-grade processors and sensors, employs an aviation-grade aluminum alloy shielded shell and metal rectangular connectors, and boasts extremely high reliability and stability; its rich array of serial ports meets the needs of different mission payloads.

[0068] It should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

[0069] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A method for precise attitude control of a UAV that integrates multiple sensors, characterized in that, The method includes the following steps: The drone's location and attribute data are collected using multiple sensors; The system presets a statistical period. The distance between the drone's position and the planned flight path in the previous statistical period at the current moment is used as the deviation degree from the corresponding position on the planned flight path. The system also obtains the offset degree between the drone and the planned flight path based on the difference between the angle and the right angle between the drone and the planned flight path, as well as the degree of deviation. Finally, the system weights the offset degree by considering the distance between the drone's position and the planned flight path at all times within the previous statistical period; the closer the distance, the lower the weight. The system compares the drone's position at the last moment of the statistical period with the planned flight path to obtain the drone's offset direction. After filtering all attribute feature data, attitude calculation is performed to obtain attitude angles, including pitch angle, yaw angle, and roll angle; a fluctuation curve is obtained for each attitude angle in the previous cycle at the current moment; the fluctuation degree and slope of the fluctuation curve are obtained based on the mean difference between adjacent data in the fluctuation curve and the slope of the data points; the attitude stability of the UAV is obtained based on the fluctuation degree of the three attitude angles and the difference between the maximum fluctuation degree and the minimum fluctuation degree. The deviation of the UAV relative to its initial position is obtained by the slope of the yaw angle fluctuation curve; the deviation is combined with the UAV's offset direction and the degree of offset and attitude stability to determine the correction angle. The three-axis error signals are calculated using the corrected yaw angle, pitch angle, and roll angle, and then input into the PID controller for control.

2. The method for precise attitude control of a UAV by fusing multiple sensors as described in claim 1, characterized in that, The attribute feature data includes the UAV's three-axis linear acceleration, three-axis angular velocity, three-axis magnetic field strength, atmospheric pressure, and the difference between dynamic and static pressure.

3. The method for precise attitude control of a UAV by fusing multiple sensors as described in claim 1, characterized in that, The method of using the distance between the UAV's position and the planned flight path in the previous statistical period at the current moment as the deviation of the UAV from the corresponding position on the planned flight path is as follows: For a given location of a drone, the shortest Euclidean distance from that location to the planned flight path is taken as the degree of deviation between that location and the corresponding location on the planned flight path.

4. The method for precise attitude control of a UAV by fusing multiple sensors as described in claim 1, characterized in that, The method for obtaining the degree of deviation between the UAV and the planned flight path based on the difference between the angle and the right angle between the UAV and the planned flight path, and the degree of deviation, is as follows: The angle between the drone's flight direction and the planned flight path is recorded as the positive angle. The degree of offset is positively correlated with the degree of deviation, the difference in the return angle, and the difference in the right angle.

5. The method for precise attitude control of a UAV by fusing multiple sensors as described in claim 1, characterized in that, The method for obtaining the offset degree of the statistical period by weighting the distance between the drone's location and its position at all times within the previous statistical period is as follows: Calculate the Euclidean distance between each location and the current location of the drone, and sort all locations in descending order of Euclidean distance to obtain the location sequence; The expression for the degree of offset is: u represents the u-th index of the position sequence, α u α represents the offset degree of the position corresponding to the u-th index, U represents the number of elements in the position sequence, and α represents the offset degree of the statistical period.

6. The method for precise attitude control of a UAV by fusing multiple sensors as described in claim 1, characterized in that, The degree of fluctuation of the fluctuation curve is the absolute value of the average difference between adjacent data points; the slope of the fluctuation curve is the average of the slopes of all data points within it.

7. The method for precise attitude control of a UAV by fusing multiple sensors as described in claim 1, characterized in that, The degree of attitude stability is negatively correlated with the degree of fluctuation of attitude angle, the difference between the maximum fluctuation and the minimum fluctuation.

8. The method for precise attitude control of a UAV by fusing multiple sensors as described in claim 1, characterized in that, The method for obtaining the UAV's yaw angle relative to its initial position based on the slope of the yaw angle fluctuation curve is as follows: When the slope of the yaw angle fluctuation curve is positive, the UAV's overall flight direction in the previous statistical period is to the left relative to the initial position; otherwise, it is to the right relative to the initial position.

9. The method for precise attitude control of a UAV by fusing multiple sensors as described in claim 1, characterized in that, The method for determining the correction angle by combining the bias with the UAV's offset direction and the degree of offset and attitude stability is as follows: When the drone's deviation from the planned flight path is the same as the drone's deviation from its initial position in the previous statistical period, a correction is required. The correction method is as follows: γ = α × β × Y, where α represents the offset of the statistical period, β represents the attitude stability of the UAV, Y represents the preset correction angle, and γ represents the correction angle of the yaw angle in the next statistical period. Conversely, no correction is needed.

10. A multi-sensor fusion drone flight controller, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the UAV precision attitude control method that integrates multiple sensors as described in any one of claims 1-9.

Images