CN109683164A - A kind of unmanned plane based on flying quality falls Activity recognition method - Google Patents
A kind of unmanned plane based on flying quality falls Activity recognition method Download PDFInfo
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- CN109683164A CN109683164A CN201910072764.4A CN201910072764A CN109683164A CN 109683164 A CN109683164 A CN 109683164A CN 201910072764 A CN201910072764 A CN 201910072764A CN 109683164 A CN109683164 A CN 109683164A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S15/00—Systems using the reflection or reradiation of acoustic waves, e.g. sonar systems
- G01S15/86—Combinations of sonar systems with lidar systems; Combinations of sonar systems with systems not using wave reflection
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/005—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 with correlation of navigation data from several sources, e.g. map or contour matching
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S15/00—Systems using the reflection or reradiation of acoustic waves, e.g. sonar systems
- G01S15/02—Systems using the reflection or reradiation of acoustic waves, e.g. sonar systems using reflection of acoustic waves
- G01S15/50—Systems of measurement, based on relative movement of the target
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Abstract
It falls Activity recognition method the invention discloses a kind of unmanned plane based on flying quality; this method compares the unmanned plane during flying data threshold that the sensor loaded on existing unmanned plane acquires unmanned plane during flying data and normal flight; to in the unit time UAV position and orientation situation of change carry out unmanned plane fall behavior determine; in combination with position and the multi-faceted judgement of posture; judge whether unmanned plane falls behavior; once occurring to forcibly close propeller rotation and sound-light alarm, and unmanned plane fall protection system is combined to carry out omnibearing protection.Simultaneously because sensor used is common airborne sensor, without reequiping to unmanned plane, it is easy to accomplish, input cost substantially reduces.
Description
Technical field
The invention belongs to air vehicle technique fields, and in particular to a kind of unmanned plane based on flying quality falls Activity recognition
Method.
Background technique
Small and micro-satellite because its have the characteristics that it is small in size, light-weight and at low cost due to be widely applied the military and people
Use field.In military field, enemy-occupied area detecting can be realized in the area of more earth bulging using Small and micro-satellite, established
The tasks such as communication relay station, destination mapping and the efficient tracking of near-earth Area Objects.In civil field, unmanned plane is also extensive
For taking photo by plane, data collection, object damage degree detecting, the fields such as article carrying and agricultural plant protection.
Due to unmanned plane be by artificial remote control, there are no small operation blind area and operation difficulty, easily occur by
Unmanned plane is out of control caused by remote signal loses and collides electric wire etc. falls, in this case the high-speed rotating paddle of unmanned plane
Leaf easily causes hazard to person to ground staff.The prior art is mainly based upon the unmanned plane fall protection system of mechanical structure,
Be passive type protection, can not identify that unmanned plane falls behavior with warning in advance ground handling operator, and be only capable of passive protection nobody
Machine can not preferably avoid the injury to ground staff.
Summary of the invention
For above-mentioned deficiency in the prior art, the unmanned plane provided by the invention based on flying quality falls Activity recognition
Method solves and can not predict unmanned plane in existing unmanned plane during flying system in advance and fall behavior, can only passive protection nobody
Machine can not preferably avoid the problem that the injury of dough figurine over the ground.
In order to achieve the above object of the invention, a kind of the technical solution adopted by the present invention are as follows: unmanned plane based on flying quality
It falls Activity recognition method, comprising the following steps:
S1, the data that each sensor installed on unmanned plane acquires are uploaded to processor；
S2, it is pre-processed by data of the processor to upload；
S3, it tests to pretreated data；
S4, data fusion is carried out to the data for examining qualification, obtains and refers to flying quality；
S5, according to reference flying quality, judge its whether be more than setting safe flight data threshold；
If so, entering step S6；
If it is not, then entering step S7；
S6, it determines that unmanned plane falls behavior, realizes that unmanned plane falls Activity recognition, and execute unmanned plane falling guard
Measure terminates identification；
S7, determine that unmanned plane during flying is normal, and return step S1.
Further, each sensor installed on unmanned plane in the step S1 includes GPS positioning sensor, ultrasonic wave
Sensor, light stream sensor, gyroscope, accelerometer, barometer and magnetometer；
Wherein, the GPS positioning sensor and barometer cooperating, for obtaining height of the unmanned plane in high-altitude flight
Degree evidence；
The light stream sensor and ultrasonic sensor cooperating, for obtaining height of the unmanned plane in flight near the ground
Degree evidence；
The accelerometer and gyroscope cooperating, for obtaining flying speed, position and the flight attitude of unmanned plane
Data；
The magnetometer is used to obtain course heading data when unmanned plane during flying.
Further, the data of upload are pre-processed in the step S2 specifically: using low-pass filter to upper
The data of biography are filtered.
Further, the step S3 specifically:
S31, the Chi-square Test functional value β for determining the pretreated data that each sensor uploadskAre as follows:
βk=rk TS(k)-1rk
In formula, rkFor residual error；
S (k) is residual error rkCovariance equation；
Subscript T is transposition operator；
S32, the value for setting false-alarm probability α；
S33, according to the value of false-alarm probability α, inquire chi-square distribution table and obtain corresponding threshold value gk；
S34, the Chi-square Test functional value β for judging the pretreated data that each sensor uploadskWhether thresholding is greater than
Value gk；
If so, the data invalid that the sensor uploads, disqualified upon inspection, terminate to examine；
If it is not, the data that then sensor uploads are effective, it is qualified to examine, and enters step S4.
Further, data fusion is carried out to the data for examining qualification by Kalman filtering algorithm in the step S4,
The step S4 specifically:
S41, according to qualified data are examined, construct the state equation and measurement equation of Data Fusion of Sensor system；
Wherein, state equation are as follows:
xk=Axk-1+Buk+wk
In formula, xkFor the state vector at k moment；
ukFor the input quantity at k moment；
wkFor the process noise of Data Fusion of Sensor system；
State-transition matrix of the A between input quantity and state variable；
B is input gain matrix；
Measure equation are as follows:
zi,k=Hixk+εi,k
In formula, zi,kFor i-th of sensor the k moment measured value；
εi,kFor i-th of sensor the k moment measurement noise；
HiFor the carry-over factor between each sensor and measured value；
S42, according to state equation, determine Data Fusion of Sensor system to the predicted value at k moment；
Wherein, the predicted value at k moment are as follows:
In formula,For the status predication value according to the value at k-1 moment to the k moment；
S43, according to the predicted value and each sensor at k moment in the measurement value matrix at k moment, determine Data Fusion of Sensor
The optimal estimation value of system as refers to flying quality；
Wherein, optimal estimation value are as follows:
In formula,For the optimal estimation value of the Data Fusion of Sensor system at k moment；
KkFor kalman gain；
ZkThe measurement value matrix that the measured value obtained by measurement equation for each sensor at the k moment is constituted；
H is observing matrix；
Further, the height change threshold value when safe flight data threshold in the step S5 includes unmanned plane during flying
T1, the first attitudes vibration threshold value T2 and the second attitudes vibration threshold value T3；
The step S5 specifically:
S51, according to reference flying quality, judge whether height change when unmanned plane during flying is more than height change threshold value
T1；
If so, entering step S52；
If it is not, then entering step S7；
S52, according to reference flying quality, in the attitudes vibration of three degree of freedom when judging unmanned plane during flying, if deposit
More than the first attitudes vibration threshold value T2 or there is the attitudes vibration value of one degree of freedom there are two the attitudes vibration value of freedom degree
The case where more than the second attitudes vibration threshold value T3；
If so, entering step S6；
If it is not, then entering step S7.
Further, the height change threshold value T1 is 0.3 meter, and the first attitudes vibration threshold value T2 is 40 °, described the
Two attitudes vibration threshold value T3 are 60 °.
Further, in the step S6, the method for execution unmanned plane falling guard measure specifically:
The acoustic-optic alarm installed on starting unmanned plane, warns surface personnel, while closing unmanned plane immediately
Motor.
Further, the model M8N GPS module of the GPS positioning sensor；
The model KS109 of the ultrasonic sensor；
The model CUAV PX4FLOW 2.21 of the light stream sensor；
The model ICM-20602 of the gyroscope and acceierometer sensor；
The barometrical model MS5611；
The model IST8310 of the magnetometer；
The model STM32F427VIT6 of the main control chip of the processor.
The Activity recognition method the invention has the benefit that the unmanned plane provided by the invention based on flying quality falls,
By the unmanned plane during flying data threshold ratio of the sensor loaded on existing unmanned plane acquisition unmanned plane during flying data and normal flight
It is right, in the unit time UAV position and orientation situation of change carry out unmanned plane fall behavior determine, in combination with position and posture
Multi-faceted judgement judges whether unmanned plane falls behavior, once occur to forcibly close propeller rotation and sound-light alarm,
And unmanned plane fall protection system is combined to carry out omnibearing protection.Simultaneously because sensor used is common airborne sensor,
Without being reequiped to unmanned plane, it is easy to accomplish, input cost substantially reduces.
Detailed description of the invention
Fig. 1 is that the unmanned plane based on flying quality falls Activity recognition method flow diagram in the present invention.
Fig. 2 is in the present invention to the pretreated method flow diagram tested.
Fig. 3 is in the present invention to the method flow diagram for examining qualified data to be merged.
Fig. 4 is to judge whether the flying quality of unmanned plane is more than safe flight data threshold method flow diagram in the present invention.
Specific embodiment
A specific embodiment of the invention is described below, in order to facilitate understanding by those skilled in the art this hair
It is bright, it should be apparent that the present invention is not limited to the ranges of specific embodiment, for those skilled in the art,
As long as various change is in the spirit and scope of the present invention that the attached claims limit and determine, these variations are aobvious and easy
See, all are using the innovation and creation of present inventive concept in the column of protection.
A kind of Activity recognition method as shown in Figure 1, unmanned plane based on flying quality falls, comprising the following steps:
S1, the data that each sensor installed on unmanned plane acquires are uploaded to processor；
The each sensor installed on unmanned plane in step S1 includes GPS positioning sensor, ultrasonic sensor, light stream biography
Sensor, gyroscope, accelerometer, barometer and magnetometer；
Wherein, the GPS positioning sensor and barometer cooperating, for obtaining height of the unmanned plane in high-altitude flight
Degree evidence；High-altitude flight refers to that drone flying height is greater than 10 meters；
The light stream sensor and ultrasonic sensor cooperating, for obtaining height of the unmanned plane in flight near the ground
Degree evidence；Flight near the ground refers to drone flying height less than 10 meters；
The accelerometer and gyroscope cooperating, for obtaining flying speed, position and the flight attitude of unmanned plane
Data；
The magnetometer is used to obtain course heading data when unmanned plane during flying.
S2, it is pre-processed by data of the processor to upload；
Wherein, the data of upload are pre-processed specifically: be filtered using data of the low-pass filter to upload
Processing, while carrying out coordinate conversion and converting the data that sensor uploads into current unmanned plane during flying coordinate system.
S3, it tests to pretreated data；
S4, data fusion is carried out to the data for examining qualification, obtains and refers to flying quality；
S5, according to reference flying quality, judge its whether be more than setting safe flight data threshold；
If so, entering step S6；
If it is not, then entering step S7；
S6, it determines that unmanned plane falls behavior, realizes that unmanned plane falls Activity recognition, and execute unmanned plane falling guard
Measure；
Wherein, the method for unmanned plane falling guard measure is executed specifically:
The acoustic-optic alarm installed on starting unmanned plane, warns surface personnel, while closing unmanned plane immediately
Motor forcibly closes propeller operating, opens unmanned plane fall protection system.
S7, determine that unmanned plane during flying is normal, and return step S1.
As shown in Fig. 2, step S3 specifically:
S31, the Chi-square Test functional value β for determining the pretreated data that each sensor uploadskAre as follows:
βk=rk TS(k)-1rk
In formula, rkFor residual error；
S (k) is residual error rkCovariance equation；
Subscript T is transposition operator；
S32, the value for setting false-alarm probability α；
S33, according to the value of false-alarm probability α, inquire chi-square distribution table and obtain corresponding threshold value gk；
S34, the Chi-square Test functional value β for judging the pretreated data that each sensor uploadskWhether thresholding is greater than
Value gk；
If so, the data invalid that the sensor uploads, disqualified upon inspection；
If it is not, the data that then sensor uploads are effective, it is qualified to examine.
By above-mentioned data checking method, the poor sensing data of confidence level can be effectively rejected, and is automatically updated each
The weight of the above-mentioned data of sensor carries out accurate unmanned plane convenient for the subsequent data according to after inspection and falls the identification of behavior.
As shown in figure 3, by Kalman filtering algorithm to the data progress data fusion for examining qualification in step S4, it is described
Step S4 specifically:
S41, according to qualified data are examined, construct the state equation and measurement equation of Data Fusion of Sensor system；
Wherein, state equation are as follows:
xk=Axk-1+Buk+wk
In formula, xkFor the state vector at k moment；
ukFor the input quantity at k moment；
wkFor the process noise of Data Fusion of Sensor system；
State-transition matrix of the A between input quantity and state variable；
B is input gain matrix；
Measure equation are as follows:
zi,k=Hixk+εi,k
In formula, zi,kFor i-th of sensor the k moment measured value；
εi,kFor i-th of sensor the k moment measurement noise；
HiFor the carry-over factor between each sensor and measured value；
S42, according to state equation, determine Data Fusion of Sensor system to the predicted value at k moment；
Wherein, the predicted value at k moment are as follows:
In formula,For the status predication value according to the value at k-1 moment to the k moment；
S43, according to the predicted value and each sensor at k moment in the measurement value matrix at k moment, determine Data Fusion of Sensor
The optimal estimation value of system as refers to flying quality；
Wherein, optimal estimation value are as follows:
In formula,For the optimal estimation value of the Data Fusion of Sensor system at k moment；
KkFor kalman gain, and kalman gain are as follows:
In formula, KkFor the kalman gain of the sensor data fusion system at k moment；
RkFor observation noise covariance matrix；
ZkThe measurement value matrix that the measured value obtained by measurement equation for each sensor at the k moment is constituted；
H is observing matrix；
In above-mentioned steps S41-S45, constantly run down until sensor data to guarantee to pass Kalman filter
All data fusions in emerging system terminate, it is also necessary to update the covariance matrix at k moment, wherein the covariance square at k moment
Battle array are as follows:
Pk|k=(I-KkH)Pk|k-1
In formula, Pk|kFor the covariance matrix of the Data Fusion of Sensor system at k moment；
I is the matrix that value is 1；
Pk|k-1It is k-1 moment Data Fusion of Sensor system to the covariance matrix predicted value at k moment, the covariance matrix
Predicted value are as follows:
Pk|k-1=APk-1|k-1AT+Qk
Wherein, Pk|k-1It is the k-1 moment to the covariance matrix predicted value at k moment；
QkFor process noise covariance matrix.
As shown in figure 4, height change threshold value T1 when the safe flight data threshold in step S5 includes unmanned plane during flying,
First attitudes vibration threshold value T2 and the second attitudes vibration threshold value T3；Wherein, height change threshold value T1 is 0.3 meter, and the first posture becomes
Changing threshold value T2 is 40 °, and the second attitudes vibration threshold value T3 is 60 °
The step S5 specifically:
S51, according to reference flying quality, judge whether height change when unmanned plane during flying is more than height change threshold value
T1；
If so, entering step S52；
If it is not, then entering step S7；
S52, according to reference flying quality, in the attitudes vibration of three degree of freedom when judging unmanned plane during flying, if deposit
More than the first attitudes vibration threshold value T2 or there is the attitudes vibration value of one degree of freedom there are two the attitudes vibration value of freedom degree
The case where more than the second attitudes vibration threshold value T3；
If so, entering step S6；
If it is not, then entering step S7.
It should be noted that the model M8N GPS module of the GPS positioning sensor in the present invention；With traditional 6M,
7M GPS module is compared, and M8N GPS module signal is most strong, fastest, precision highest, which uses Switzerland u-blox company
The NEO series high accuracy positioning module of production, built-in electronic compass can support the mainstreams HA Global Positioning Satellite such as GPS, Beidou system
System；
The model KS109 of the ultrasonic sensor；KS109 is transceiver ultrasonic wave, farthest can reach 10 meters
Measurement range, precision can reach 3-5mm, and compared with other each model ultrasonic sensors, the series sensor field angle is minimum,
For 10-20 degree, influence of the barrier to unmanned plane Height Estimation below unmanned plane can be utmostly avoided；
The model CUAV PX4FLOW 2.21 of the light stream sensor；The sensor has MT9V034CMOS sensor
Unit and MB1043 sonar distance measuring unit, light stream arithmetic speed can reach 120Hz (interior) to 250Hz (outdoor), can be black
Steady operation under dark or half-light environment；
The model ICM-20602 of the gyroscope and acceierometer sensor；It is a kind of 6 axis motion tracers,
Three-axis gyroscope, three axis accelerometer are combined, volume and power consumption can be reduced, compared with MPU6050, its drift is missed
Difference and measurement noise are smaller；
The barometrical model MS5611, it is a SPI and I released by MEAS2The high-resolution of C bus interface
Rate baroceptor, resolution ratio can reach 10cm；
The model IST8310 of the magnetometer has excellent low magnetic hysteresis, low-noise performance and high-reliability, can be most
Big situation avoids the magnetic disturbance to it such as unmanned plane oneself motor and ambient enviroment；
The model STM32F427VIT6 of the main control chip of the processor；The microcontroller dominant frequency is up to 180MHz, tool
There is the up to processing capacity of 225DMIPS, furthermore there are the Peripheral Interfaces such as multiple SPI, meet actual demand.
Unmanned plane provided by the invention based on flying quality falls Activity recognition method, by what is loaded on existing unmanned plane
Sensor acquisition unmanned plane during flying data and the unmanned plane during flying data threshold of normal flight compare, to nobody in the unit time
Seat in the plane appearance situation of change carry out unmanned plane fall behavior judgement, in combination with position and the multi-faceted judgement of posture, judge unmanned plane
Whether fall behavior, once occurring to forcibly close propeller rotation and sound-light alarm, and combines unmanned plane falling guard
System carries out omnibearing protection.Simultaneously because sensor used is common airborne sensor, without being reequiped to unmanned plane,
It is easily achieved, input cost substantially reduces.
Claims (9)
- A kind of Activity recognition method 1. unmanned plane based on flying quality falls, which comprises the following steps:S1, the data that each sensor installed on unmanned plane acquires are uploaded to processor；S2, it is pre-processed by data of the processor to upload；S3, it tests to pretreated data；S4, data fusion is carried out to the data for examining qualification, obtains and refers to flying quality；S5, according to reference flying quality, judge its whether be more than setting safe flight data threshold；If so, entering step S6；If it is not, then entering step S7；S6, it determines that unmanned plane falls behavior, realizes that unmanned plane falls Activity recognition, and execute unmanned plane falling guard and arrange It applies, terminates identification；S7, determine that unmanned plane during flying is normal, and return step S1.
- The Activity recognition method 2. the unmanned plane according to claim 1 based on flying quality falls, which is characterized in that described The each sensor installed on unmanned plane in step S1 includes GPS positioning sensor, ultrasonic sensor, light stream sensor, top Spiral shell instrument, accelerometer, barometer and magnetometer；Wherein, the GPS positioning sensor and barometer cooperating, for obtaining high degree of the unmanned plane in high-altitude flight According to；The light stream sensor and ultrasonic sensor cooperating, for obtaining high degree of the unmanned plane in flight near the ground According to；The accelerometer and gyroscope cooperating, for obtaining flying speed, position and the flight attitude data of unmanned plane；The magnetometer is used to obtain course heading data when unmanned plane during flying.
- The Activity recognition method 3. the unmanned plane according to claim 1 based on flying quality falls, which is characterized in that described The data of upload are pre-processed in step S2 specifically: be filtered using data of the low-pass filter to upload.
- The Activity recognition method 4. the unmanned plane according to claim 1 based on flying quality falls, which is characterized in that described Step S3 specifically:S31, the Chi-square Test functional value β for determining the pretreated data that each sensor uploadskAre as follows:βk=rk TS(k)-1rkIn formula, rkFor residual error；S (k) is residual error rkCovariance equation；Subscript T is transposition operator；S32, the value for setting false-alarm probability α；S33, according to the value of false-alarm probability α, inquire chi-square distribution table and obtain corresponding threshold value gk；S34, the Chi-square Test functional value β for judging the pretreated data that each sensor uploadskWhether threshold value g is greater thank；If so, the data invalid that the sensor uploads, disqualified upon inspection, terminate to examine；If it is not, the data that then sensor uploads are effective, it is qualified to examine, and enters step S4.
- The Activity recognition method 5. the unmanned plane according to claim 4 based on flying quality falls, which is characterized in that described Data fusion, the step S4 are carried out to the data for examining qualification by Kalman filtering algorithm in step S4 specifically:S41, according to qualified data are examined, construct the state equation and measurement equation of Data Fusion of Sensor system；Wherein, state equation are as follows:xk=Axk-1+Buk+wkIn formula, xkFor the state vector at k moment；ukFor the input quantity at k moment；wkFor the process noise of Data Fusion of Sensor system；State-transition matrix of the A between input quantity and state variable；B is input gain matrix；Measure equation are as follows:zi,k=Hixk+εi,kIn formula, zi,kFor i-th of sensor the k moment measured value；εi,kFor i-th of sensor the k moment measurement noise；HiFor the carry-over factor between each sensor and measured value；S42, according to state equation, determine Data Fusion of Sensor system to the predicted value at k moment；Wherein, the predicted value at k moment are as follows:In formula,For the status predication value according to the value at k-1 moment to the k moment；S43, according to the predicted value and each sensor at k moment in the measurement value matrix at k moment, determine Data Fusion of Sensor system Optimal estimation value, as refer to flying quality；Wherein, optimal estimation value are as follows:In formula,For the optimal estimation value of the Data Fusion of Sensor system at k moment；KkFor kalman gain；ZkThe measurement value matrix that the measured value obtained by measurement equation for each sensor at the k moment is constituted；H is observing matrix.
- The Activity recognition method 6. the unmanned plane according to claim 1 based on flying quality falls, which is characterized in that described Height change threshold value T1, the first attitudes vibration threshold value T2 when safe flight data threshold in step S5 includes unmanned plane during flying With the second attitudes vibration threshold value T3；The step S5 specifically:S51, according to reference flying quality, judge whether height change when unmanned plane during flying is more than height change threshold value T1；If so, entering step S52；If it is not, then entering step S7；S52, according to reference flying quality, in the attitudes vibration of three degree of freedom when judging unmanned plane during flying, if there are The attitudes vibration value of two freedom degrees is more than more than the first attitudes vibration threshold value T2 or the attitudes vibration value for having one degree of freedom The case where second attitudes vibration threshold value T3；If so, entering step S6；If it is not, then entering step S7.
- The Activity recognition method 7. the unmanned plane according to claim 6 based on flying quality falls, which is characterized in that described Height change threshold value T1 is 0.3 meter, and the first attitudes vibration threshold value T2 is 40 °, and the second attitudes vibration threshold value T3 is 60°。
- The behavior safeguard measure 8. the unmanned plane according to claim 1 based on flying quality falls, which is characterized in that described In step S6, the method for execution unmanned plane falling guard measure specifically:The acoustic-optic alarm installed on starting unmanned plane, warns surface personnel, while closing the motor of unmanned plane immediately.
- The Activity recognition method 9. the unmanned plane according to claim 2 based on flying quality falls, which is characterized in that described The model M8N GPS module of GPS positioning sensor；The model KS109 of the ultrasonic sensor；The model CUAV PX4FLOW 2.21 of the light stream sensor；The model ICM-20602 of the gyroscope and acceierometer sensor；The barometrical model MS5611；The model IST8310 of the magnetometer；The model STM32F427VIT6 of the main control chip of the processor.
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| WO2020216072A1 (en) * | 2019-04-26 | 2020-10-29 | 拓攻（南京）机器人有限公司 | Method and apparatus for detecting abnormal falling of unmanned aerial vehicle, device, and storage medium |
| CN112346471A (en) * | 2020-11-18 | 2021-02-09 | 苏州臻迪智能科技有限公司 | Unmanned aerial vehicle height fixing method and device, unmanned aerial vehicle and storage medium |
| CN112672306A (en) * | 2021-01-13 | 2021-04-16 | 四川九通智路科技有限公司 | Structural object posture detection method |
| CN112666961A (en) * | 2020-12-14 | 2021-04-16 | 广东电网有限责任公司佛山供电局 | Unmanned aerial vehicle crash detection method, system and device, unmanned aerial vehicle and storage medium |
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