UAV multi-sensor triple-redundant flight control system, method and storage medium

Abstract

The present invention belongs to the field of drone control technology and discloses a drone multi-sensor three-redundant flight control system, method and storage medium. The data acquisition module uses three groups of redundant sensors to collect aircraft data; the preprocessing module performs preliminary calculations on the sensor raw data; the redundancy management module votes on the data collected by the sensors; and the flight control module fuses the sensor data to estimate and control the drone's motion state. The present invention uses a redundancy management algorithm to implement an effective voting mechanism for the three groups of IMUs and magnetometers to select the optimal sensor group and estimate and control the drone's motion state. The present invention uses a complementary filtering algorithm to preprocess the raw data of the three groups of sensors, solving the problem of difficulty in effectively comparing the raw data of multiple groups of sensors and the inability to detect sensor soft faults, thereby improving the stability and reliability of the drone's flight control system.

Description

Technical Field

[0001] The present invention belongs to the technical field of unmanned aerial vehicle (UAV) control, and in particular relates to a UAV multi-sensor triple-redundant flight control system, method and storage medium. Background Art

[0002] At present, drones are unmanned aircraft that are controlled by radio remote control equipment and self-contained program control devices, among which the flight control system is the most core part of the drone.

[0003] The UAV flight control system collects information from various sensors to filter and estimate the aircraft's motion state information such as position, speed and attitude, and uses this information to control the UAV, thereby realizing manual and automatic flight of the aircraft and completing various preset tasks.

[0004] Among them, the Inertial Measurement Unit (IMU) and magnetometer are very important sensor devices in the UAV flight control system. The IMU contains a three-axis accelerometer and a three-axis gyroscope. The accelerometer is used to detect the three-axis acceleration of the UAV in the carrier coordinate system, and the gyroscope is used to detect the angular velocity information of the UAV relative to the navigation coordinate system. The magnetometer is used to detect the direction of the geomagnetic field near the aircraft to obtain the aircraft's heading information. By fusing and processing the information from the IMU and magnetometer, the UAV flight control can calculate the aircraft's attitude information, which is crucial for aircraft flight control. If any of these sensors fails, the UAV will lose control and will not function properly.

[0005] In order to avoid loss of control due to sensor failure, existing drones usually have redundant backups of IMU and magnetometers. Once a sensor failure occurs, they switch to the backup to improve system stability.

[0006] Typical multi-redundant systems typically compare raw data from sensors of the same type and then vote to select the fault-free device for subsequent calculations. However, the primary and backup sensors used for redundancy often use different models. These sensors sometimes have different data output frequencies. They are often also located on the same bus or lack data synchronization mechanisms. This makes it difficult for the voting module to effectively compare the raw sensor data. It can only select the optimal sensor based on a few simple fault indicators and detect "hard faults" such as data interruptions. However, these faults are rare, and more common are "soft faults" such as signal aliasing, clipping, and drift, which are difficult to diagnose by comparing raw data.

[0007] Through the above analysis, the problems and defects of the existing technology are: it is difficult to effectively compare the original data of the sensor, and the redundancy sensor voting algorithm has a low ability to detect sensor soft faults, resulting in unreliable sensor units in the UAV flight control system.

[0008] The difficulty of solving the above problems and defects is:

[0009] (1) If the existing small UAV control system uses highly reliable sensors such as mechanical gyroscopes and fiber optic gyroscopes, the cost is high and it affects the weight and volume of the UAV.

[0010] (2) The primary and backup sensors used in existing redundant systems usually use different models of devices, which sometimes have different data output frequencies. The data between them cannot be synchronized, making it difficult to directly and effectively compare the raw sensor data.

[0011] (3) In existing multi-sensor redundant systems, when a sensor has a "soft fault" such as signal aliasing, clipping, and drift, it is usually difficult to detect it by directly comparing the original data, which will lead to abnormal estimation of the aircraft attitude.

[0012] The significance of solving the above problems and defects is:

[0013] With the added weight and cost of the aircraft negligible, a redundant system with three sensor switching and its control method are proposed. A preprocessing module is added to preprocess the three sensor groups using the AHRS algorithm to obtain attitude solution results. By comparing the preprocessing results, potential faults in the three sensor groups can be identified. This effectively detects "soft faults" such as aliasing, clipping, and drift in the sensor signals, thereby improving the reliability of UAV flight. Summary of the Invention

[0014] In response to the problems existing in the prior art, the present invention provides a multi-sensor triple-redundant flight control system, method and storage medium for an unmanned aerial vehicle.

[0015] The present invention is implemented as follows: a multi-sensor triple-redundant flight control system for an unmanned aerial vehicle, the multi-sensor triple-redundant flight control system for an unmanned aerial vehicle comprises: three groups of redundant sensor units, three pre-processing modules, a redundancy management module and a flight state estimation module;

[0016] Three redundant sensor units, each consisting of an IMU consisting of a three-axis accelerometer and a three-axis gyroscope, and a magnetometer. The three-axis accelerometer detects the drone's acceleration along three axes in the carrier coordinate system, while the three-axis gyroscope measures the drone's angular velocity relative to the navigation coordinate system. The magnetometer detects the direction of the Earth's magnetic field near the aircraft, thereby obtaining the aircraft's heading information.

[0017] The pre-processing module is used to perform preliminary calculations on the raw sensor data. This module uses the AHRS algorithm to fuse the data from the accelerometer, gyroscope, and magnetometer to calculate the aircraft's current attitude information, such as pitch, roll, and heading.

[0018] The redundancy management module is used to monitor and judge the attitude information output by the preprocessing module, compare the fault status of the three sensor groups using fault judgment logic, and select the optimal fault-free sensor group for subsequent flight status estimation.

[0019] A data acquisition module for collecting aircraft data using three sets of redundant sensor units;

[0020] The flight control module is used to fuse sensor data to estimate and control the UAV's motion state.

[0021] Furthermore, the three groups of redundant sensor units also include a temperature detection subunit for performing temperature detection and control.

[0022] Furthermore, the UAV multi-sensor triple-redundant flight control system also includes:

[0023] The constant temperature control module includes multiple heating modules evenly distributed at the four corners of the redundant sensor unit; it is used to provide heat for the data acquisition module.

[0024] Another object of the present invention is to provide a UAV multi-sensor triple-redundancy flight control method applied to the UAV multi-sensor triple-redundancy flight control system, the UAV multi-sensor triple-redundancy flight control method comprising:

[0025] Step 1: using three sets of redundant sensors to collect data simultaneously; pre-processing the data collected by the three sets of redundant sensors by complementary filtering method;

[0026] Step 2: Monitor the preprocessing results through a redundancy management algorithm and adjust the sensor group based on the monitored preprocessing results;

[0027] Step 3: Select the optimal sensor raw data according to the redundancy management algorithm and use it as the input information of the Kalman filter in the flight control system to estimate the UAV motion state;

[0028] Step 4: Determine the availability of the main Kalman filter's estimation results and monitor the estimated state of the drone. Step 5: Compare the preprocessed results with the main Kalman filter's attitude estimation. If it exceeds a certain threshold, it is determined that the main Kalman filter may have an abnormal estimate of the drone's motion state due to other measurement information. The availability of the main Kalman filter's estimation results and the estimated state of the monitoring system are determined.

[0029] Furthermore, in step 1, preprocessing the data collected by the three groups of sensors by using the complementary filtering method includes:

[0030] The data collected by the three-axis accelerometer, three-axis gyroscope and three-axis magnetometer are obtained, and the collected data are fused using a complementary filter to calculate the pitch, roll and yaw angles of the drone to obtain accurate three-axis attitude information.

[0031] In step 2, the AHRS algorithm uses the Madgwick method to calculate the pitch, roll and yaw angles of the drone using the data from the three-axis accelerometer, three-axis gyroscope and three-axis magnetometer. The essence of the Madgwick algorithm is to weighted integrate the attitude calculated by the gyroscope at time t. Attitude calculated together with the accelerometer and magnetic field meter So as to obtain the final posture The weighted formula is as follows.

[0032]

[0033] α1+α2=1 0≤α1≤1,0≤α2≤1

[0034] Here, α1 and α2 are weighting coefficients, determined by the contribution of their respective errors to the overall error. The smaller the error contribution, the larger the weighting coefficient. Let the sampling interval be Δt. The gyroscope's error per unit time, β, can be found in the gyroscope manual and is generally a very small value, so the gyroscope's error is βΔt. The attitude error calculated by the accelerometer and magnetometer is determined by the calculation method, which includes gradient descent, Gauss-Newton iteration, Newton's method, and conjugate gradient method.

[0035] Furthermore, in step 2, adjusting the sensor group based on the monitored preprocessing result includes:

[0036] When the data of the main sensor group does not output the three-axis attitude information for N consecutive detection moments, or outputs the same attitude data for M consecutive detection moments, the sensor raw data used by the main Kalman filter is set to the sensor group with the second priority; if the data of the second priority sensor group does not output the three-axis attitude information for N consecutive detection moments, or outputs the same attitude data for M consecutive detection moments, the sensor raw data used by the main Kalman filter is set to the sensor group with the third priority;

[0037] When the difference between any of the three-axis attitude information in the preprocessing results of the main sensor group and the other two groups exceeds the threshold X, the sensor raw data used by the main Kalman filter is switched to the sensor group with the second priority;

[0038] When the difference between any axis data in the three-axis posture information of a certain sensor group and the other two groups exceeds the threshold Y, the number of failures kn of the sensor group will be increased by one; when the number of sensor failures kn of a certain group exceeds the threshold Z, the priority of the sensor group will be adjusted to the lowest.

[0039] Furthermore, in step 4, determining the availability of the estimation result of the main Kalman filter and monitoring the estimated state of the drone include:

[0040] The result of complementary filter preprocessing is compared with the attitude estimation of the main Kalman filter. When it exceeds a certain threshold, the estimation of the UAV motion state is judged to be abnormal.

[0041] Another object of the present invention is to provide a drone, which implements the drone multi-sensor triple-redundancy flight control method described in any one of claims 1 to 4.

[0042] Another object of the present invention is to provide a computer device, which includes a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the unmanned aerial vehicle multi-sensor three-degree-of-redundancy flight control method.

[0043] Another object of the present invention is to provide a computer-readable storage medium storing a computer program, which, when executed by a processor, enables the processor to execute the unmanned aerial vehicle multi-sensor three-degree-of-redundancy flight control method.

[0044] Combining all the above technical solutions, the advantages and positive effects of the present invention are as follows: the present invention uses a redundancy management algorithm to perform an effective voting mechanism on the three groups of IMUs and magnetometers to select the optimal sensor group and estimate and control the motion state of the drone.

[0045] The present invention preprocesses the raw data of three groups of sensors through a complementary filtering algorithm, solving the problem of difficulty in effectively comparing the raw data of multiple groups of sensors and inability to detect sensor soft faults, thereby improving the stability and reliability of the UAV flight control system.

[0046] This invention uses complementary filtering to preprocess each set of IMU and magnetometer sensor data. A sensor redundancy management algorithm then votes to select the optimal sensor group for integration into the flight control system. This information is then used to control the drone, improving the safety and stability of the flight control system. This preprocessing algorithm overcomes the difficulty of effectively comparing raw sensor data and enhances the redundant sensor voting algorithm's ability to detect soft sensor faults.

[0047] The present invention uses a complementary filtering method to pre-process the raw sensor data and performs redundancy management based on the posture information calculated from each set of sensor data. This solves the problem of the inability to directly compare raw data caused by data asynchrony between sensors of different models, and can effectively detect soft faults such as sensor data drift and clipping. The control strategy and switching are completed by software, which is simple to implement and highly reliable. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments of the present application. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0049] Figure 1 This is a schematic diagram of the structure of a multi-sensor triple-redundant flight control system for a UAV provided by an embodiment of the present invention;

[0050] In the figure: 1. Data acquisition module; 2. Preprocessing module; 3. Redundancy management module; 4. Flight control module; 5. Constant temperature control module; 6. Three sets of redundant sensor units.

[0051] Figure 2 It is a structural schematic diagram of a constant temperature control module provided by an embodiment of the present invention.

[0052] Figure 3 This is a schematic diagram of the principle of the multi-sensor triple-redundant flight control method for a UAV provided by an embodiment of the present invention.

[0053] Figure 4 This is a flow chart of the multi-sensor triple-redundant flight control method for a UAV provided by an embodiment of the present invention.

[0054] Figure 5 This is a flow chart of a constant temperature control module provided by an embodiment of the present invention.

[0055] Figure 6 This is a comparison diagram of sensor temperatures with and without a heating module provided by an embodiment of the present invention.

[0056] Figure 7 This is a comparison diagram of shedding data drift when a heating module is provided and when a heating module is not provided in an embodiment of the present invention.

[0057] Figure 8 This is a data preprocessing flow chart of the complementary filtering method provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0058] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the 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.

[0059] In response to the problems existing in the prior art, the present invention provides a multi-sensor triple-redundant flight control system and method for an unmanned aerial vehicle (UAV). The present invention is described in detail below with reference to the accompanying drawings.

[0060] like Figure 1-Figure 2 As shown, the UAV multi-sensor triple-redundant flight control system provided by the embodiment of the present invention includes:

[0061] Data acquisition module 1, for collecting aircraft data using three sets of redundant sensor units;

[0062] Preprocessing module 2, used for preliminary solution of sensor raw data;

[0063] Redundancy management module 3, used to vote on the data collected by the sensor;

[0064] Flight control module 4, used to fuse sensor data to estimate and control the UAV motion state;

[0065] The constant temperature control module 5 includes four heating modules evenly distributed at the four corners of the redundant sensor unit, and is used to provide heat for the data acquisition module.

[0066] Three redundant sensor units 6, each consisting of an IMU consisting of a three-axis accelerometer and a three-axis gyroscope, and a magnetometer. The three-axis accelerometer is used to detect the three-axis acceleration of the drone in the carrier coordinate system, and the three-axis gyroscope is used to detect the angular velocity of the drone relative to the navigation coordinate system. The magnetometer is used to detect the direction of the geomagnetic field near the aircraft, thereby obtaining the aircraft's heading information.

[0067] The three groups of redundant sensor units also include a temperature detection subunit for performing temperature detection and control.

[0068] Among them, the pre-processing module 2 uses the AHRS algorithm to fuse the data of the accelerometer, gyroscope and magnetometer to calculate the aircraft's current attitude information such as pitch angle, roll angle and heading angle;

[0069] In an embodiment of the present invention, the redundancy management module 3 is also used to monitor and judge the attitude information output by the preprocessing module, compare the fault states of the three sensor groups using fault judgment logic, and select the optimal fault-free sensor group for subsequent flight state estimation.

[0070] like Figure 3-Figure 4As shown, the UAV multi-sensor triple-redundant flight control method provided by the embodiment of the present invention includes the following steps:

[0071] S101, using three sets of redundant sensors to simultaneously collect data; preprocessing the data collected by the three sets of redundant sensors using a complementary filtering method;

[0072] S102, monitoring the preprocessing result through a redundancy management algorithm, and adjusting the sensor group based on the monitored preprocessing result;

[0073] S103, selecting the optimal sensor raw data according to the redundancy management algorithm as input information for the Kalman filter in the flight control system to estimate the motion state of the UAV;

[0074] S104, determining the availability of the estimation result of the main Kalman filter and monitoring the estimation state of the UAV.

[0075] In step S101, the preprocessing of data collected by the three groups of sensors using the complementary filtering method provided by the embodiment of the present invention includes:

[0076] The data collected by the three-axis accelerometer, three-axis gyroscope and three-axis magnetometer are obtained, and the collected data are fused using a complementary filter to calculate the pitch, roll and yaw angles of the drone to obtain accurate three-axis attitude information.

[0077] In step S102, the redundancy management algorithm AHRS algorithm uses the Madgwick method to calculate the pitch, roll and yaw angles of the drone using the data of the three-axis accelerometer, three-axis gyroscope and three-axis magnetometer. The essence of the Madgwick algorithm is to weighted integrate the attitude calculated by the gyroscope at time t. Attitude calculated together with the accelerometer and magnetic field meter So as to obtain the final posture The weighted formula is as follows.

[0078]

[0079] α1+α2=1 0≤α1≤1,0≤α2≤1

[0080] Here, α1 and α2 are weighting coefficients, determined by the contribution of their respective errors to the overall error. The smaller the error contribution, the larger the weighting coefficient. Let the sampling interval be Δt. The gyroscope's error per unit time, β, can be found in the gyroscope manual and is generally a very small value, so the gyroscope's error is βΔt. The attitude error calculated by the accelerometer and magnetometer is determined by the calculation method, which includes gradient descent, Gauss-Newton iteration, Newton's method, and conjugate gradient method.

[0081] In step S102, adjusting the sensor group based on the monitored pre-processing result includes:

[0082] When the data of the main sensor group does not output the three-axis attitude information for N consecutive detection moments, or outputs the same attitude data for M consecutive detection moments, the sensor raw data used by the main Kalman filter is set to the sensor group with the second priority; if the data of the second priority sensor group does not output the three-axis attitude information for N consecutive detection moments, or outputs the same attitude data for M consecutive detection moments, the sensor raw data used by the main Kalman filter is set to the sensor group with the third priority;

[0083] When the difference between any of the three-axis attitude information in the preprocessing results of the main sensor group and the other two groups exceeds the threshold X, the sensor raw data used by the main Kalman filter is switched to the sensor group with the second priority;

[0084] When the difference between any axis data in the three-axis posture information of a certain sensor group and the other two groups exceeds the threshold Y, the number of failures kn of the sensor group will be increased by one; when the number of sensor failures kn of a certain group exceeds the threshold Z, the priority of the sensor group will be adjusted to the lowest.

[0085] In step S104, the method of determining the availability of the estimation result of the main Kalman filter and monitoring the estimated state of the drone provided by the embodiment of the present invention includes:

[0086] The result of complementary filter preprocessing is compared with the attitude estimation of the main Kalman filter. When it exceeds a certain threshold, the estimation of the UAV motion state is judged to be abnormal.

[0087] The technical effects of the present invention will be further described below with reference to specific embodiments.

[0088] Example:

[0089] The present invention provides a multi-sensor triple-redundancy system for unmanned aerial vehicles (UAVs), which mainly includes a flight control module for fusing sensor data to estimate and control the UAV's motion state; three groups of redundant sensor units for collecting aircraft data, each group of sensor units including an inertial measurement unit (IMU) consisting of a three-axis accelerometer and a three-axis gyroscope, and a magnetometer; three preprocessing modules for performing preliminary calculations on raw sensor data; and a redundancy management module for voting on the three groups of sensor data.

[0090] The UAV multi-sensor triple redundancy system provided by the present invention is designed with a constant temperature system to improve the reliability of data in low temperature environment. The hardware structure is as follows: Figure 1As shown, four heating modules provide heat to the entire IMU system unit. The four heating modules are evenly distributed at the four corners of the module, ensuring uniform heating of the three redundant IMU units. The redundant IMU unit contains three IMU + magnetometer modules. The temperature detection unit integrated in the IMU module is used to detect and control the temperature of the entire system. This ensures the accuracy of the IMU and magnetometer data in low-temperature environments.

[0091] The present invention provides a multi-sensor triple-redundant flight control method for an unmanned aerial vehicle, which includes the following specific steps:

[0092] Step 1: After the drone is powered on, the three sensor groups collect data simultaneously. The flight control software first uses the sensor group with the highest default priority as the primary sensor group.

[0093] Step 2: Preprocess the three sensor units using complementary filtering. This algorithm uses data from a triaxial accelerometer, triaxial gyroscope, and triaxial magnetometer to calculate the drone's pitch, roll, and yaw angles. While gyroscopes offer excellent dynamic response, they generate cumulative errors when calculating attitude. Magnetometers and accelerometers measure attitude without cumulative errors, but their dynamic response is poor. Their characteristics complement each other in the frequency domain. Using a complementary filter to fuse the data from these three sensors yields accurate triaxial attitude information. Complementary filter calculation.

[0094] The present invention provides a multi-sensor triple-redundant flight control method for an unmanned aerial vehicle, which includes the following specific steps:

[0095] Step 1: After the drone is powered on, the three sensor groups collect data simultaneously. The flight control software first uses the sensor group with the highest default priority as the primary sensor group.

[0096] Step 2: Preprocess the three sensor units using the complementary filtering method. The complementary filtering method uses data from a triaxial accelerometer, triaxial gyroscope, and triaxial magnetometer to calculate the drone's pitch, roll, and yaw angles. While the gyroscope has good dynamic response characteristics, it generates cumulative errors when calculating attitude. Magnetometers and accelerometers measure attitude without cumulative errors, but their dynamic response is poor. Their characteristics complement each other in the frequency domain. Using a complementary filter to fuse the data from these three sensors yields accurate triaxial attitude information. The block diagram for the complementary filter is shown below.

[0097] The above description is only a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications, equivalent substitutions and improvements made by any technician familiar with this technical field within the technical scope disclosed by the present invention and within the spirit and principles of the present invention should be covered by the scope of protection of the present invention.

Claims

1. A multi-sensor triple-redundant flight control method for an unmanned aerial vehicle, characterized in that: The UAV multi-sensor triple-redundancy flight control method includes: Using three sets of redundant sensors to collect data; pre-processing the collected data by complementary filtering method; Monitoring the preprocessing result by a redundancy management algorithm, and adjusting the sensor group based on the monitored preprocessing result; The optimal sensor raw data is selected based on the redundancy management algorithm and used as the input information of the Kalman filter in the flight control system to estimate the UAV motion state; Determining the availability of the Kalman filter's estimation results and monitoring the estimated state of the drone; comparing the complementary filter preprocessing results with the main Kalman filter's attitude estimation, and determining that the estimated motion state of the drone is abnormal when a certain threshold is exceeded; The redundancy management algorithm adopts the Madgwick method, which uses the data of the three-axis accelerometer, three-axis gyroscope and three-axis magnetometer to calculate the pitch, roll and yaw angles of the drone; the Madgwick method includes: weighted integration of the attitude calculated by the gyroscope at time t Attitude calculated together with the accelerometer and magnetic field meter , and get the final posture ; The weighted formula is as follows: ； in, and is the weighting coefficient, which is determined by the proportion of each error to the total error. The smaller the proportion of the error, the larger the weighting coefficient. Suppose the sampling time interval is ; Gyroscope error per unit time By checking the manual of the gyroscope, the error of the gyroscope is ; The error of the attitude calculated by the accelerometer and the magnetometer is determined by the calculation method, which includes gradient descent method, Gauss-Newton iteration method, Newton method, and conjugate gradient method; The sensor group adjustment based on the monitoring preprocessing result includes: When the data of the main sensor group does not output the three-axis attitude information for N consecutive detection moments, or outputs the same attitude data for M consecutive detection moments, the sensor raw data used by the main Kalman filter is set to the sensor group with the second priority; if the data of the second priority sensor group does not output the three-axis attitude information for N consecutive detection moments, or outputs the same attitude data for M consecutive detection moments, the sensor raw data used by the main Kalman filter is set to the sensor group with the third priority; When the difference between any of the three-axis attitude information in the preprocessing results of the main sensor group and the other two groups exceeds the threshold X, the sensor raw data used by the main Kalman filter is switched to the sensor group with the second priority; When the difference between any axis data in the three-axis posture information of a certain sensor group and the other two groups exceeds the threshold Y, the number of failures kn of the sensor group will be increased by one; when the number of sensor failures kn of a certain group exceeds the threshold Z, the priority of the sensor group will be adjusted to the lowest.

2. The UAV multi-sensor triple-redundancy flight control method according to claim 1, characterized in that: The preprocessing of the data collected by the three groups of sensors by the complementary filtering method includes: The data collected by the three-axis accelerometer, three-axis gyroscope and three-axis magnetometer are obtained, and the collected data are fused using a complementary filter to calculate the pitch, roll and yaw angles of the drone to obtain accurate three-axis attitude information.

3. A UAV multi-sensor triple-redundant flight control system, characterized in that: The UAV multi-sensor triple-redundancy flight control system includes: Three sets of redundant sensor units, each including an IMU consisting of a three-axis accelerometer and a three-axis gyroscope, and a magnetometer. The three-axis accelerometer is used to detect the three-axis acceleration of the drone in the carrier coordinate system, and the three-axis gyroscope is used to detect the angular velocity information of the drone relative to the navigation coordinate system. The magnetometer is used to detect the direction of the geomagnetic field near the aircraft and obtain the aircraft's heading information. The pre-processing module is used to perform preliminary calculations on the raw sensor data. It uses the AHRS algorithm to fuse the data from the accelerometer, gyroscope, and magnetometer to calculate the aircraft's current pitch, roll, and heading attitude information. The redundancy management module is used to monitor and judge the attitude information output by the pre-processing module, compare the fault status of the three sensor groups using fault judgment logic, and select the optimal fault-free sensor group for subsequent flight state estimation; A data acquisition module, used to collect aircraft data using three sets of redundant sensors; The flight control module is used to fuse sensor data to estimate and control the UAV's motion state.

4. The UAV multi-sensor triple-redundant flight control system according to claim 3, characterized in that: The redundant sensor unit further includes a temperature detection subunit for performing temperature detection and control.

5. The UAV multi-sensor triple-redundancy flight control system according to claim 3, characterized in that: The UAV multi-sensor triple-redundancy flight control system also includes: The constant temperature control module includes multiple heating modules evenly distributed at the four corners of the redundant sensor unit; it is used to provide heat for the data acquisition module.

6. A drone, characterized in that: The UAV implements the UAV multi-sensor triple-redundancy flight control method described in any one of claims 1 to 2.

7. A computer device, characterized in that: The computer device includes a memory and a processor, the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the unmanned aerial vehicle multi-sensor triple-redundant flight control method according to any one of claims 1 to 2.

8. A computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor executes the unmanned aerial vehicle multi-sensor triple-redundant flight control method according to any one of claims 1 to 2.

Images