CN113104233A - Unmanned aerial vehicle quality estimation method and device, electronic equipment and storage medium - Google Patents

Unmanned aerial vehicle quality estimation method and device, electronic equipment and storage medium Download PDF

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CN113104233A
CN113104233A CN202110545266.4A CN202110545266A CN113104233A CN 113104233 A CN113104233 A CN 113104233A CN 202110545266 A CN202110545266 A CN 202110545266A CN 113104233 A CN113104233 A CN 113104233A
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unmanned aerial
aerial vehicle
motor
preset
drone
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CN113104233B (en
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杨政
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Zhejiang Huafei Intelligent Technology Co ltd
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Zhejiang Huafei Intelligent Technology Co ltd
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B64—AIRCRAFT; AVIATION; COSMONAUTICS
    • B64F—GROUND OR AIRCRAFT-CARRIER-DECK INSTALLATIONS SPECIALLY ADAPTED FOR USE IN CONNECTION WITH AIRCRAFT; DESIGNING, MANUFACTURING, ASSEMBLING, CLEANING, MAINTAINING OR REPAIRING AIRCRAFT, NOT OTHERWISE PROVIDED FOR; HANDLING, TRANSPORTING, TESTING OR INSPECTING AIRCRAFT COMPONENTS, NOT OTHERWISE PROVIDED FOR
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Abstract

The invention discloses a method and a device for estimating the quality of an unmanned aerial vehicle, electronic equipment and a storage medium, wherein the method comprises the following steps: acquiring a pwm value of each motor in the unmanned aerial vehicle; for each motor, when the pwm value of the motor is greater than a preset first threshold value and the pwm value of a diagonal motor of the motor is less than a preset second threshold value, determining that the blade corresponding to the motor is an abnormal blade; otherwise, determining the blade corresponding to the motor as a normal blade; wherein the preset first threshold is greater than the preset second threshold; and determining the pulling force of the unmanned aerial vehicle according to the rotating speed of the motor corresponding to each normal blade, and estimating the mass of the unmanned aerial vehicle according to the pulling force and the gravity acceleration of the unmanned aerial vehicle. According to the embodiment of the invention, the tension of the unmanned aerial vehicle is determined according to the rotating speed of the motor corresponding to each normal blade. The influence of the rotating speed of the motor corresponding to the abnormal paddle on the pulling force of the unmanned aerial vehicle is avoided, so that the quality of the unmanned aerial vehicle is estimated more accurately.

Description

Unmanned aerial vehicle quality estimation method and device, electronic equipment and storage medium
Technical Field
The invention relates to the technical field of unmanned aerial vehicles, in particular to a method and a device for estimating the quality of an unmanned aerial vehicle, electronic equipment and a storage medium.
Background
Unmanned aerial vehicle wide application is in fields such as public security fire control, electric power inspection, relief of disaster rescue. The mass of the unmanned aerial vehicle influences the control strategy of the control system. Accurate estimation of the mass of the unmanned aerial vehicle is crucial to estimation of the service life, flight time and flight distance of the unmanned aerial vehicle.
In the prior art, when the mass of the unmanned aerial vehicle is estimated, the tension of the unmanned aerial vehicle is calculated according to the rotating speed of a motor of each blade of the unmanned aerial vehicle, and the mass of the unmanned aerial vehicle is estimated according to the tension and the gravitational acceleration of the unmanned aerial vehicle. The problem that prior art exists is that, unmanned aerial vehicle the condition that the paddle drops probably appears in flight in-process, if the paddle drops, the motor of this paddle has the rotational speed, but does not have tensile force output in fact, consequently can lead to the unmanned aerial vehicle pulling force of calculation inaccurate, and then makes the unmanned aerial vehicle quality of estimation inaccurate.
Disclosure of Invention
The embodiment of the invention provides a method and a device for estimating the quality of an unmanned aerial vehicle, electronic equipment and a storage medium, which are used for solving the problem that the quality of the unmanned aerial vehicle is estimated inaccurately in the prior art.
The embodiment of the invention provides an unmanned aerial vehicle quality estimation method, which comprises the following steps:
acquiring a pulse width modulation pwm value of each motor in the unmanned aerial vehicle;
for each motor, when the pwm value of the motor is greater than a preset first threshold value and the pwm value of a diagonal motor of the motor is less than a preset second threshold value, determining that the blade corresponding to the motor is an abnormal blade; otherwise, determining the blade corresponding to the motor as a normal blade; wherein the preset first threshold is greater than the preset second threshold;
and determining the pulling force of the unmanned aerial vehicle according to the rotating speed of the motor corresponding to each normal blade, and estimating the mass of the unmanned aerial vehicle according to the pulling force and the gravity acceleration of the unmanned aerial vehicle.
Further, for each motor, when the pwm value of the motor is greater than a preset first threshold and the pwm value of a diagonal motor of the motor is less than a preset second threshold, determining that the blade corresponding to the motor is an abnormal blade; otherwise, determining that the blade corresponding to the motor is a normal blade comprises the following steps:
for each motor, determining that the pwm value of the motor is greater than a preset first threshold within a preset detection time or within a preset detection duration, and determining that the blade corresponding to the motor is an abnormal blade when the pwm value of the diagonal motor of the motor is less than a preset second threshold; otherwise, determining the blade corresponding to the motor as a normal blade.
Further, the determining the tension of the unmanned aerial vehicle according to the rotating speed of the motor corresponding to each normal blade comprises:
the pitch angle and the roll angle of the unmanned aerial vehicle are obtained, and the pitch angle and the roll angle of the unmanned aerial vehicle are determined according to the rotating speed of a motor corresponding to each normal blade.
Further, the estimating the mass of the drone according to the pulling force and the gravitational acceleration of the drone comprises:
acquiring the vertical speed of the unmanned aerial vehicle, and determining the resistance of the unmanned aerial vehicle according to the vertical speed of the unmanned aerial vehicle and the determined resistance coefficient;
and estimating the mass of the unmanned aerial vehicle according to the tension and the gravity acceleration of the unmanned aerial vehicle and the resistance of the unmanned aerial vehicle.
Further, estimating the mass of the drone according to the tension, the acceleration of gravity, and the resistance of the drone comprises:
and acquiring the vertical acceleration of the unmanned aerial vehicle, and estimating the mass of the unmanned aerial vehicle according to the pulling force and the gravity acceleration of the unmanned aerial vehicle, the resistance of the unmanned aerial vehicle and the vertical acceleration of the unmanned aerial vehicle.
Further, estimating the mass of the drone according to the pulling force of the drone, the acceleration of gravity, the resistance of the drone, and the vertical acceleration of the drone comprises:
and estimating the mass of the unmanned aerial vehicle based on an iterative least square method according to the tension and the gravity acceleration of the unmanned aerial vehicle, the resistance of the unmanned aerial vehicle and the vertical acceleration of the unmanned aerial vehicle.
Further, estimating the mass of the unmanned aerial vehicle based on an iterative least square method according to the pulling force of the unmanned aerial vehicle, the acceleration due to gravity, the resistance of the unmanned aerial vehicle, and the vertical acceleration of the unmanned aerial vehicle includes:
according to
Figure BDA0003073381920000031
Estimating the mass of the drone, wherein V > 0 represents upward movement of the drone, and V < 0 tableShowing the unmanned aerial vehicle to move downwards; v is the vertical speed of the unmanned aerial vehicle, g is the acceleration of gravity, azIs the vertical acceleration, kfThe coefficient of resistance is m, the mass of the unmanned aerial vehicle is F, the tension of the unmanned aerial vehicle is theta, the pitch angle of the unmanned aerial vehicle is theta, and phi is the roll angle of the unmanned aerial vehicle;
definition of
Figure BDA0003073381920000032
b＝F·cosθ·cosφ；
Calculating h as a.h + c.ATA, wherein a is a preset memory coefficient, and c is a preset weighting coefficient; calculating covariance matrix P ═ h-1(ii) a Calculating gain matrix K ═ P · AT；
According to x ═ xlast+K·(b-bpredict) Estimating the mass of the drone, wherein bpredict＝Ax。
Further, the definition
Figure BDA0003073381920000033
After b is F · cos θ · cos Φ, h is calculated as a · h + c · aTBefore A, the method further comprises:
calculating h ═ h'last+ATA, judging whether h' calculated in a preset first time length is reversible or not, and if so, carrying out subsequent calculation of h as a.h + c.ATAnd A.
Further, the calculation of h '═ h'last+ATAfter a, the method further comprises:
calculating covariance matrix P '═ h'-1(ii) a Calculating the information matrix S ═ Slast+ATb; an initial mass estimation value x 'P' S is calculated.
Further, said according to x ═ xlast+K·(b-bpredict) After estimating the quality of the drone, the method further comprises:
judging whether the difference value of any two qualities of the unmanned aerial vehicle obtained by calculation in a preset second time length is smaller than a preset threshold value, and if so, determining that the quality of the unmanned aerial vehicle is effective; if not, determining that the quality of the unmanned aerial vehicle is invalid, adjusting the preset memory coefficient, or the preset weighting coefficient, or the preset first duration, and re-estimating the quality of the unmanned aerial vehicle.
In another aspect, an embodiment of the present invention provides an apparatus for estimating quality of an unmanned aerial vehicle, where the apparatus includes:
the acquisition module is used for acquiring a pulse width modulation pwm value of each motor in the unmanned aerial vehicle;
the determining module is used for determining that the blade corresponding to the motor is an abnormal blade when the pwm value of the motor is greater than a preset first threshold and the pwm value of the diagonal motor of the motor is less than a preset second threshold for each motor; otherwise, determining the blade corresponding to the motor as a normal blade; wherein the preset first threshold is greater than the preset second threshold;
and the estimation module is used for determining the tension of the unmanned aerial vehicle according to the rotating speed of the motor corresponding to each normal blade, and estimating the mass of the unmanned aerial vehicle according to the tension and the gravity acceleration of the unmanned aerial vehicle.
Further, the determining module is specifically configured to determine, for each motor, that a pwm value of the motor is greater than a preset first threshold within a preset detection time or within a preset detection duration, and when the pwm value of a diagonal motor of the motor is less than a preset second threshold, determine that a blade corresponding to the motor is an abnormal blade; otherwise, determining the blade corresponding to the motor as a normal blade.
Further, the estimation module is specifically used for obtaining the pitch angle and the roll angle of the unmanned aerial vehicle, and the tension of the unmanned aerial vehicle is determined according to the rotating speed of the motor corresponding to each normal blade and the pitch angle and the roll angle of the unmanned aerial vehicle.
Further, the estimation module is specifically configured to acquire a vertical speed of the unmanned aerial vehicle, and determine the resistance of the unmanned aerial vehicle according to the vertical speed of the unmanned aerial vehicle and the determined resistance coefficient; and estimating the mass of the unmanned aerial vehicle according to the tension and the gravity acceleration of the unmanned aerial vehicle and the resistance of the unmanned aerial vehicle.
Further, the estimation module is specifically used for acquiring the vertical acceleration of the unmanned aerial vehicle, and the mass of the unmanned aerial vehicle is estimated according to the tension and the gravity acceleration of the unmanned aerial vehicle, the resistance of the unmanned aerial vehicle and the vertical acceleration of the unmanned aerial vehicle.
Further, the estimation module is specifically configured to estimate the mass of the unmanned aerial vehicle based on an iterative least square method according to the tension of the unmanned aerial vehicle, the gravitational acceleration, the resistance of the unmanned aerial vehicle, and the vertical acceleration of the unmanned aerial vehicle.
Further, the estimation module is specifically configured to operate in accordance with
Figure BDA0003073381920000051
Estimating a mass of the drone, wherein V > 0 represents upward movement of the drone and V < 0 represents downward movement of the drone; v is the vertical speed of the unmanned aerial vehicle, g is the acceleration of gravity, azIs the vertical acceleration, kfThe coefficient of resistance is m, the mass of the unmanned aerial vehicle is F, the tension of the unmanned aerial vehicle is theta, the pitch angle of the unmanned aerial vehicle is theta, and phi is the roll angle of the unmanned aerial vehicle;
definition of
Figure BDA0003073381920000052
b＝F·cosθ·cosφ；
Calculating h as a.h + c.ATA, wherein a is a preset memory coefficient, and c is a preset weighting coefficient; calculating covariance matrix P ═ h-1(ii) a Calculating gain matrix K ═ P · AT；
According to x ═ xlast+K·(b-bpredict) Estimating the mass of the drone, wherein bpredict＝Ax。
Further, the apparatus further comprises:
a first judging module for calculating h '═ h'last+ATA, judging whether h' calculated in a preset first time length is reversible or not, if so, triggering the estimation module to perform subsequent calculation on h ═ a · h + c · ATAnd A.
Further, the apparatus further comprises:
a calculation module for calculating a covariance matrix P '═ h'-1(ii) a Calculating the information matrix S ═ Slast+ATb; an initial mass estimation value x 'P' S is calculated.
Further, the apparatus further comprises:
the second judgment module is used for judging whether the difference value of any two qualities of the unmanned aerial vehicle calculated in a preset second time length is smaller than a preset threshold value or not, and if so, determining that the quality of the unmanned aerial vehicle is effective; if not, determining that the quality of the unmanned aerial vehicle is invalid, adjusting the preset memory coefficient, or the preset weighting coefficient, or the preset first duration, and re-estimating the quality of the unmanned aerial vehicle.
On the other hand, the embodiment of the invention provides electronic equipment, which comprises a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory complete mutual communication through the communication bus;
a memory for storing a computer program;
a processor for implementing any of the above method steps when executing a program stored in the memory.
In another aspect, an embodiment of the present invention provides a computer-readable storage medium, in which a computer program is stored, and the computer program, when executed by a processor, implements the method steps of any one of the above.
The embodiment of the invention provides a method and a device for estimating the quality of an unmanned aerial vehicle, electronic equipment and a storage medium, wherein the method comprises the following steps: acquiring a pulse width modulation pwm value of each motor in the unmanned aerial vehicle; for each motor, when the pwm value of the motor is greater than a preset first threshold value and the pwm value of a diagonal motor of the motor is less than a preset second threshold value, determining that the blade corresponding to the motor is an abnormal blade; otherwise, determining the blade corresponding to the motor as a normal blade; wherein the preset first threshold is greater than the preset second threshold; and determining the pulling force of the unmanned aerial vehicle according to the rotating speed of the motor corresponding to each normal blade, and estimating the mass of the unmanned aerial vehicle according to the pulling force and the gravity acceleration of the unmanned aerial vehicle.
The technical scheme has the following advantages or beneficial effects:
in the embodiment of the invention, the normal blades and the abnormal blades in the unmanned aerial vehicle are determined according to the pwm value of each motor in the unmanned aerial vehicle. Then confirm unmanned aerial vehicle's pulling force according to the rotational speed of the motor that every normal paddle corresponds, according to unmanned aerial vehicle's pulling force and acceleration of gravity, estimate unmanned aerial vehicle's quality. The influence of the motor rotating speed corresponding to the abnormal paddle on the tension of the unmanned aerial vehicle is avoided, so that the tension of the unmanned aerial vehicle is determined more accurately, and the quality of the unmanned aerial vehicle is estimated more accurately.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic diagram of a process of estimating the quality of an unmanned aerial vehicle according to embodiment 1 of the present invention;
fig. 2 is a schematic view of a wing of a six-rotor drone provided in embodiment 1 of the present invention;
fig. 3 is a schematic view of force analysis if the unmanned aerial vehicle moves upward according to embodiment 3 of the present invention;
fig. 4 is a schematic view of force analysis if the unmanned aerial vehicle moves downward according to embodiment 3 of the present invention;
fig. 5 is a schematic view of force analysis of the unmanned aerial vehicle according to embodiment 3 of the present invention if the unmanned aerial vehicle moves at a certain inclination angle;
fig. 6 is a flow chart of unmanned aerial vehicle quality estimation provided in embodiment 5 of the present invention;
fig. 7 is a schematic structural diagram of an unmanned aerial vehicle quality estimation apparatus provided in embodiment 6 of the present invention;
fig. 8 is a schematic structural diagram of an electronic device according to embodiment 7 of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the attached drawings, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1:
fig. 1 is a schematic diagram of a quality estimation process of an unmanned aerial vehicle according to an embodiment of the present invention, where the process includes the following steps:
s101: and acquiring a pulse width modulation pwm value of each motor in the unmanned aerial vehicle.
S102: for each motor, when the pwm value of the motor is greater than a preset first threshold value and the pwm value of a diagonal motor of the motor is less than a preset second threshold value, determining that the blade corresponding to the motor is an abnormal blade; otherwise, determining the blade corresponding to the motor as a normal blade; wherein the preset first threshold is greater than the preset second threshold.
S103: and determining the pulling force of the unmanned aerial vehicle according to the rotating speed of the motor corresponding to each normal blade, and estimating the mass of the unmanned aerial vehicle according to the pulling force and the gravity acceleration of the unmanned aerial vehicle.
The unmanned aerial vehicle quality estimation method provided by the embodiment of the invention is applied to electronic equipment, and the electronic equipment can be a flight controller of an unmanned aerial vehicle, and can also be other equipment with unmanned aerial vehicle control function, such as a PC (personal computer), a tablet personal computer and the like. The unmanned aerial vehicle in the embodiment of the invention comprises a multi-rotor unmanned aerial vehicle.
When the unmanned aerial vehicle flies, the control system of the unmanned aerial vehicle outputs a signal to the power system, and the blades of the power system rotate to generate lift force, so that the unmanned aerial vehicle is controlled to move. The multi-rotor unmanned aerial vehicle is an unmanned aerial vehicle of an under-actuated system, and the pulse width modulation pwm value output to each motor by a control system realizes the six-degree-of-freedom decoupling control of the unmanned aerial vehicle. When a certain blade of the unmanned aerial vehicle loses power, the pwm value of the abnormal blade is larger and approximately close to the maximum value in a certain range, and the pwm value of the diagonal blade is close to the minimum value in the certain range. In the embodiment of the invention, the normal blade and the abnormal blade are determined based on the analysis.
When the unmanned aerial vehicle flies, the electronic equipment acquires a pulse width modulation pwm (pwm) value of each motor in the unmanned aerial vehicle, and then determines that a blade corresponding to each motor is an abnormal blade when the pwm value of the motor is greater than a preset first threshold and the pwm value of a diagonal motor of the motor is less than a preset second threshold for each motor; otherwise, determining the blade corresponding to the motor as a normal blade; the preset first threshold is larger than the preset second threshold.
As shown in fig. 2, taking a six-rotor drone as an example for explanation, the pwm value control amount of each motor is normally 1200-1900. The preset first threshold value may be set to 1850 and the preset second threshold value may be set to 1250. If the pwm value of the motor corresponding to the blade No. 1 is greater than 1850, and the pwm value of the motor corresponding to the blade No. 2 at the opposite angle of the motor corresponding to the blade No. 1 is less than 1250, determining that the blade No. 1 is an abnormal blade, otherwise, determining that the blade No. 1 is a normal blade. In addition, when the pwm value of the motor corresponding to the blade No. 1 is greater than the preset first threshold, it may be determined whether the difference between the pwm value of the motor corresponding to the blade No. 1 and the pwm value of the motor corresponding to the blade No. 2 is greater than a preset third threshold, if so, the blade No. 1 is determined to be an abnormal blade, otherwise, the blade No. 1 is determined to be a normal blade. Wherein the preset third threshold may be 550, 580, etc. In order to make the determination of the normal blade and the abnormal blade more accurate, the number of times of detection or the detection time period may be set in advance in the embodiment of the present invention. If the number 1 paddle detected by the preset detection times is abnormal, determining that the number 1 paddle is abnormal; or if the No. 1 paddle detected within the preset detection time is abnormal, determining that the No. 1 paddle is abnormal. Otherwise, determining the No. 1 blade as a normal blade. The rest arms are analogized in turn and are not listed one by one.
After the electronic equipment determines each normal paddle, the tension of the unmanned aerial vehicle is determined according to the rotating speed of the motor corresponding to each normal paddle. In particular, the determination of what each normal blade corresponds toThe average value omega of the rotating speeds of the motors is obtained according to the number n of the motors corresponding to the normal blades, the average value omega of the rotating speeds of the motors corresponding to each normal blade and a preset tension coefficient C of the unmanned aerial vehicleTAnd determining the tension F of the unmanned aerial vehicle. Wherein F is n.CT·Ω2。
And estimating the mass of the unmanned aerial vehicle according to the pulling force and the gravity acceleration of the unmanned aerial vehicle. Unmanned aerial vehicle's gravity equals unmanned aerial vehicle's gravity mass times acceleration of gravity, and unmanned aerial vehicle is when the suspended state, and pulling force and gravity are the same, therefore unmanned aerial vehicle's pulling force is confirmed when the suspended state to unmanned aerial vehicle, and then estimates unmanned aerial vehicle's quality.
Wherein, unmanned aerial vehicle coefficient of tension is related to motor model, atmospheric pressure and paddle shape, and the paddle shape has been fixed unchangeable when the design, and the motor model can the prerecording, and atmospheric pressure can be measured by the barometer of installing on unmanned aerial vehicle and obtain, and then can be obtained by driving system off-line test platform to single driving system test fitting, and different motor models, atmospheric pressure, paddle shape correspond different unmanned aerial vehicle coefficient of tension. Then when calculating the unmanned aerial vehicle pulling force, determine current unmanned aerial vehicle coefficient of tension according to current unmanned aerial vehicle's paddle shape, motor model and current atmospheric pressure.
In the embodiment of the invention, the normal blades and the abnormal blades in the unmanned aerial vehicle are determined according to the pwm value of each motor in the unmanned aerial vehicle. Then confirm unmanned aerial vehicle's pulling force according to the rotational speed of the motor that every normal paddle corresponds, according to unmanned aerial vehicle's pulling force and acceleration of gravity, estimate unmanned aerial vehicle's quality. The influence of the motor rotating speed corresponding to the abnormal paddle on the tension of the unmanned aerial vehicle is avoided, so that the tension of the unmanned aerial vehicle is determined more accurately, and the quality of the unmanned aerial vehicle is estimated more accurately.
In the embodiment of the present invention, in order to determine an abnormal blade more accurately, for each motor, when a pwm value of the motor is greater than a preset first threshold and a pwm value of a diagonal motor of the motor is less than a preset second threshold, the blade corresponding to the motor is determined to be an abnormal blade; otherwise, determining that the blade corresponding to the motor is a normal blade comprises the following steps:
for each motor, determining that the pwm value of the motor is greater than a preset first threshold within a preset detection time or within a preset detection duration, and determining that the blade corresponding to the motor is an abnormal blade when the pwm value of the diagonal motor of the motor is less than a preset second threshold; otherwise, determining the blade corresponding to the motor as a normal blade.
The preset number of times of detection is stored in the electronic device, for example, the preset number of times of detection is 5 times, 10 times, and the like. If the pwm value of the motor is determined to be larger than a preset first threshold value in the preset detection times, and the pwm value of the diagonal motor of the motor is determined to be smaller than a preset second threshold value, determining that the blade corresponding to the motor is an abnormal blade; otherwise, determining the blade corresponding to the motor as a normal blade. Or the preset detection time period is saved in the electronic device, for example, the preset detection time period is 1 minute, 2 minutes, and the like. If the pwm value of the motor is determined to be larger than a preset first threshold value within a preset detection duration, and the pwm value of a diagonal motor of the motor is determined to be smaller than a preset second threshold value, determining that the blade corresponding to the motor is an abnormal blade; otherwise, determining the blade corresponding to the motor as a normal blade.
The abnormal paddle is determined by setting the preset detection times or the preset detection duration, so that the problem of misjudgment caused by accidental interference is avoided, and the abnormal paddle is determined more accurately.
Example 2:
in general, the unmanned aerial vehicle not only moves in the vertical direction, but also moves in the horizontal direction, and in order to make the determination of the tension of the unmanned aerial vehicle more accurate, on the basis of the above embodiment, in an embodiment of the present invention, the determining of the tension of the unmanned aerial vehicle according to the rotation speed of the motor corresponding to each normal blade includes:
the pitch angle and the roll angle of the unmanned aerial vehicle are obtained, and the pitch angle and the roll angle of the unmanned aerial vehicle are determined according to the rotating speed of a motor corresponding to each normal blade.
In the flight process of the unmanned aerial vehicle, a control system in the unmanned aerial vehicle can acquire the pitch angle and the roll angle of the unmanned aerial vehicle in real time, and when the quality of the unmanned aerial vehicle is estimated by the electronic equipment, the current pitch angle and the current roll angle of the unmanned aerial vehicle can be acquired through the control system. And then determining the tension of the unmanned aerial vehicle according to the rotating speed of the motor corresponding to each normal blade, the pitch angle and the roll angle of the unmanned aerial vehicle.
Specifically, an average value omega of the rotating speeds of the motors corresponding to each normal blade is determined, and according to the number n of the motors corresponding to the normal blades, the average value omega of the rotating speeds of the motors corresponding to each normal blade and a preset tension coefficient CT of the unmanned aerial vehicle, F is calculated to be n.CT·Ω2. And then taking F · cos theta · cos phi as the finally determined tension of the unmanned aerial vehicle. Wherein theta is the pitch angle of the unmanned aerial vehicle, and phi is the roll angle of the unmanned aerial vehicle.
According to the embodiment of the invention, the pitch angle and the roll angle of the unmanned aerial vehicle are obtained, and the tension of the unmanned aerial vehicle is determined according to the rotating speed of the motor corresponding to each normal blade and the pitch angle and the roll angle of the unmanned aerial vehicle. Can guarantee no matter how unmanned aerial vehicle flies, can both determine vertical direction's pulling force, and then make the unmanned aerial vehicle quality of confirming more accurate.
Example 3:
unmanned aerial vehicle can receive the influence of resistance in the flight process, and the resistance further can influence the estimation of unmanned aerial vehicle quality. Therefore, in order to make the estimation of the mass of the unmanned aerial vehicle more accurate, on the basis of the above embodiments, in an embodiment of the present invention, the estimating the mass of the unmanned aerial vehicle according to the pulling force and the gravitational acceleration of the unmanned aerial vehicle includes:
acquiring the vertical speed of the unmanned aerial vehicle, and determining the resistance of the unmanned aerial vehicle according to the vertical speed of the unmanned aerial vehicle and the determined resistance coefficient;
and estimating the mass of the unmanned aerial vehicle according to the tension and the gravity acceleration of the unmanned aerial vehicle and the resistance of the unmanned aerial vehicle.
Unmanned aerial vehicle is at the flight in-process, and control system among the unmanned aerial vehicle can acquire unmanned aerial vehicle's vertical speed in real time, and electronic equipment can acquire unmanned aerial vehicle's vertical speed through control system when estimating unmanned aerial vehicle quality. The resistance of the drone is proportional to the square of the vertical velocity, and the electronic device may determine a resistance coefficient, where the resistance coefficient may beA resistance coefficient set in advance empirically. The resistance of the unmanned aerial vehicle is the product of the square of the vertical speed and the resistance coefficient, namely, f ═ kf·V2Wherein V is the vertical speed of the unmanned aerial vehicle, kfIs a resistance coefficient, and f is the resistance of the unmanned aerial vehicle.
Electronic equipment is according to unmanned aerial vehicle's pulling force, acceleration of gravity and unmanned aerial vehicle's resistance, when estimating unmanned aerial vehicle's quality, need consider that unmanned aerial vehicle is upward movement or downward motion. If the drone moves upwards, the resistance it is subjected to is downwards. Fig. 3 shows a schematic diagram of the force analysis of the drone, and the mass of the drone is estimated according to the formula F-F ═ mg. Wherein, F is unmanned aerial vehicle's pulling force, and F is unmanned aerial vehicle's resistance, and g is acceleration of gravity, and m is unmanned aerial vehicle's quality. If the drone moves downwards, it is subjected to resistance upwards. Fig. 4 shows a schematic diagram of the force analysis of the drone, and the mass of the drone is estimated according to the formula F + F ═ mg. Wherein, whether unmanned aerial vehicle upward movement or downward movement can obtain through the inertial measurement unit IMU of installation on the unmanned aerial vehicle.
It should be noted that, when the unmanned aerial vehicle moves at a certain inclination, the unmanned aerial vehicle stress analysis schematic diagram is shown in fig. 5, and at this moment, when the mass of the unmanned aerial vehicle is estimated, calculation should be performed according to the tension component of the unmanned aerial vehicle. Wherein, according to the number n of the motors corresponding to the normal blades, the average value omega of the rotating speeds of the motors corresponding to each normal blade and the preset tension coefficient C of the unmanned aerial vehicleTCalculating F ═ n · CT·Ω2. F · cos θ · cos phi unmanned plane tension component.
In the embodiment of the invention, the electronic equipment acquires the vertical speed of the unmanned aerial vehicle, and determines the resistance of the unmanned aerial vehicle according to the vertical speed of the unmanned aerial vehicle and the determined resistance coefficient; and estimating the mass of the unmanned aerial vehicle according to the tension and the gravity acceleration of the unmanned aerial vehicle and the resistance of the unmanned aerial vehicle. The influence of the resistance of the unmanned aerial vehicle on the unmanned aerial vehicle mass estimation is considered, so that the unmanned aerial vehicle mass obtained through estimation is more accurate.
Example 4:
on the basis of the above embodiments, in the embodiment of the present invention, in order to make estimation of the mass of the unmanned aerial vehicle more accurate, the estimating of the mass of the unmanned aerial vehicle according to the pulling force, the gravitational acceleration, and the resistance of the unmanned aerial vehicle includes:
and acquiring the vertical acceleration of the unmanned aerial vehicle, and estimating the mass of the unmanned aerial vehicle according to the pulling force and the gravity acceleration of the unmanned aerial vehicle, the resistance of the unmanned aerial vehicle and the vertical acceleration of the unmanned aerial vehicle.
In the embodiment of the invention, the vertical acceleration of the unmanned aerial vehicle can be obtained through the inertial measurement unit IMU installed on the unmanned aerial vehicle, and then whether the unmanned aerial vehicle moves upwards or downwards needs to be considered when the mass of the unmanned aerial vehicle is estimated according to the tension, the gravity acceleration, the resistance of the unmanned aerial vehicle and the vertical acceleration of the unmanned aerial vehicle. If the drone moves upwards, the resistance it is subjected to is downwards. In this case, according to the formula F-F-mg-mazAnd estimating the quality of the unmanned aerial vehicle. Wherein, F is unmanned aerial vehicle's pulling force, and F is unmanned aerial vehicle's resistance, and g is acceleration of gravity, and m is unmanned aerial vehicle's quality, anzIs the vertical acceleration of the unmanned aerial vehicle. If the drone moves downwards, it is subjected to resistance upwards. Fig. 4 shows a schematic diagram of the force analysis of the drone, which is obtained according to the formula F + F-mg ═ mazAnd estimating the quality of the unmanned aerial vehicle.
In the embodiment of the invention, the mass of the unmanned aerial vehicle is estimated according to the tension, the gravity acceleration, the resistance of the unmanned aerial vehicle and the vertical acceleration of the unmanned aerial vehicle. Therefore, the estimated quality of the unmanned aerial vehicle is more accurate.
Example 5:
in order to make the estimation of the mass of the unmanned aerial vehicle more accurate, on the basis of the above embodiments, in an embodiment of the present invention, the estimating the mass of the unmanned aerial vehicle according to the tension of the unmanned aerial vehicle, the gravitational acceleration, the resistance of the unmanned aerial vehicle, and the vertical acceleration of the unmanned aerial vehicle includes:
and estimating the mass of the unmanned aerial vehicle based on an iterative least square method according to the tension and the gravity acceleration of the unmanned aerial vehicle, the resistance of the unmanned aerial vehicle and the vertical acceleration of the unmanned aerial vehicle.
Specifically, according to the pulling force of unmanned aerial vehicle, acceleration of gravity, unmanned aerial vehicle's resistance and unmanned aerial vehicle's vertical acceleration, based on the iterative least square method, estimate unmanned aerial vehicle's quality includes:
according to
Figure BDA0003073381920000131
Estimating a mass of the drone, wherein V > 0 represents upward movement of the drone and V < 0 represents downward movement of the drone; v is the vertical speed of the unmanned aerial vehicle, g is the acceleration of gravity, azIs the vertical acceleration, kfThe coefficient of resistance is m, the mass of the unmanned aerial vehicle is F, the tension of the unmanned aerial vehicle is theta, the pitch angle of the unmanned aerial vehicle is theta, and phi is the roll angle of the unmanned aerial vehicle;
definition of
Figure BDA0003073381920000132
b＝F·cosθ·cosφ；
Calculating h as a.h + c.ATA, wherein a is a preset memory coefficient, and c is a preset weighting coefficient; calculating covariance matrix P ═ h-1(ii) a Calculating gain matrix K ═ P · AT；
According to x ═ xlast+K·(b-bpredict) Estimating the mass of the drone, wherein bpredict＝Ax。
Before this, can judge earlier whether reach steady state, if reach steady state and carry out subsequent according to unmanned aerial vehicle's pulling force, acceleration of gravity, unmanned aerial vehicle's resistance and unmanned aerial vehicle's vertical acceleration, based on the iterative least square method, estimate the process of unmanned aerial vehicle's quality. The specific process of determining whether the steady state is reached is defined as
Figure BDA0003073381920000133
After b is F · cos θ · cos Φ, before h is calculated as a · h + c · ATA, the method further comprises:
calculating h ═ h'last+ATA, judging whether h' calculated in a preset first time length is reversible or not, and if so, carrying out subsequent calculation of h as a.h + c.ATAnd A.
Also, in the embodiment of the present invention, the calculation h '═ h'last+ATAfter a, the method further comprises:
calculating covariance matrix P '═ h'-1(ii) a Calculating the information matrix S ═ Slast+ATb; an initial mass estimation value x 'P' S is calculated.
Said according to x ═ xlast+K·(b-bpredict) After estimating the quality of the drone, the method further comprises:
judging whether the difference value of any two qualities of the unmanned aerial vehicle obtained by calculation in a preset second time length is smaller than a preset threshold value, and if so, determining that the quality of the unmanned aerial vehicle is effective; if not, determining that the quality of the unmanned aerial vehicle is invalid, adjusting the preset memory coefficient, or the preset weighting coefficient, or the preset first duration, and re-estimating the quality of the unmanned aerial vehicle.
The process of estimating the mass of the unmanned aerial vehicle based on the iterative least square method according to the pulling force of the unmanned aerial vehicle, the gravitational acceleration, the resistance of the unmanned aerial vehicle, and the vertical acceleration of the unmanned aerial vehicle will be described in detail below.
The iterative least square method can estimate the quality in real time on line, and data at all times do not need to be stored in a flight control system, so that the data storage capacity and the calculation workload can be greatly reduced.
The vertical kinematics model of the unmanned aerial vehicle shows that the tension F, the gravity acceleration g, the speed V, the pitch angle theta, the roll angle phi and the speed azAre all known quantities, mass m, drag coefficient kfFor unknown quantities, where the mass m is the quantity to be estimated, V > 0 is defined when the drone is moving upwards, and V < 0 is defined when the drone is moving downwards. The unmanned aerial vehicle kinematics equation is rewritten into a form of separating a known quantity from an unknown quantity, specifically
Figure BDA0003073381920000141
For convenience of writing hereinafter, the above formula is noted
Ax＝b
Wherein,
Figure BDA0003073381920000151
b is F · cos θ · cos Φ, A, b is the known quantity, and x is the quantity to be estimated.
Based on the iterative least squares method, estimating the mass of the drone comprises two phases: an initialization phase and an iterative estimation phase.
And after the unmanned aerial vehicle normally takes off, entering an initialization stage. The initialization phase time is short and does not need very accurate estimation values, so the idea of state estimation batch calculation is adopted, that is, data in a period of time is accumulated until the initialization condition is met, and the specific method is as follows:
first, h ' ═ h ' is calculated 'last+ATA, defining h 'as the inverse of the covariance matrix, h' as A at each timeTA, thus, covariance matrix P '═ h'-1When the matrix h' is reversible, the second step is carried out;
second, calculating the information matrix S ═ Slast+ATb, S at each time ATb is the accumulated sum;
and thirdly, calculating an estimated initial value x, specifically x ═ P' · S.
When the matrix h is reversible and lasts for a preset first duration, for example, 5 seconds, the quality estimation initialization is considered to be completed, and an iterative estimation stage is entered. The method comprises the following specific steps:
first, a covariance matrix is calculated. In the iterative estimation state, the covariance P still needs to be calculated, but the iterative optimization process may last for a long time, and if the data at all time instants are accumulated all the time, the effect of newly added data will be reduced, which will cause the estimator to fail. Therefore, the problem of data saturation is solved by adopting an evanescent memory method, and the specific method comprises the following steps: calculating h as a.h + b.ATA, where a is a memory coefficient and b is a weightIn this embodiment, a is 0.99 and b is 1. Then the covariance matrix P ═ h is calculated-1When the matrix h is reversible, entering a second step;
second, the gain matrix K is calculated as P · aT。
And thirdly, performing iterative updating. Updating the equation to x ═ xlast+K·(b-bpredict) (ii) a Wherein, bpredictAx is the predicted value of b.
Fourthly, evaluating results, and when the quality estimation result is smooth and stable, considering that the quality estimation result is accurate and effective; if the estimation result has deviation or does not converge, the parameters are adjusted until the estimation result is stable and has no deviation.
By the above process, the online estimation of the mass m of the unmanned aerial vehicle is completed. The iterative optimization mode is adopted, so that the calculated amount can be reduced; the memory coefficient and the weighting coefficient can accelerate the convergence speed and accuracy of the quality estimation.
Fig. 6 is a flow chart of unmanned aerial vehicle quality estimation provided by the embodiment of the present invention, and as shown in fig. 6, power system abnormality detection is first performed to determine whether there is an abnormal paddle, if not, tension of the unmanned aerial vehicle is determined according to the rotation speeds of all motors, if so, the abnormal paddle is removed, and the rotation speed of the motor corresponding to the normal paddle is selected to determine the tension of the unmanned aerial vehicle. And establishing an unmanned aerial vehicle kinematic model, and estimating the mass of the unmanned aerial vehicle based on an iterative least square method according to the tension and the gravity acceleration of the unmanned aerial vehicle, the resistance of the unmanned aerial vehicle and the vertical acceleration of the unmanned aerial vehicle. Evaluating the result, and when the quality estimation result is smooth and stable, considering that the quality estimation result is accurate and effective; if the estimation result has deviation or does not converge, the parameters are adjusted until the estimation result is stable and has no deviation.
Example 6:
fig. 7 is a schematic structural diagram of an unmanned aerial vehicle quality estimation apparatus provided in an embodiment of the present invention, where the apparatus includes:
the acquisition module 71 is configured to acquire a pwm value of each motor in the unmanned aerial vehicle;
the determining module 72 is configured to determine, for each motor, that a blade corresponding to the motor is an abnormal blade when a pwm value of the motor is greater than a preset first threshold and a pwm value of a diagonal motor of the motor is less than a preset second threshold; otherwise, determining the blade corresponding to the motor as a normal blade; wherein the preset first threshold is greater than the preset second threshold;
and the estimation module 73 is used for determining the tension of the unmanned aerial vehicle according to the rotating speed of the motor corresponding to each normal blade, and estimating the mass of the unmanned aerial vehicle according to the tension and the gravity acceleration of the unmanned aerial vehicle.
The determining module 72 is specifically configured to determine, for each motor, that a pwm value of the motor is greater than a preset first threshold within a preset detection time or a preset detection duration, and when a pwm value of a diagonal motor of the motor is smaller than a preset second threshold, determine that a blade corresponding to the motor is an abnormal blade; otherwise, determining the blade corresponding to the motor as a normal blade.
The estimation module 73 is specifically used for acquiring the pitch angle and the roll angle of the unmanned aerial vehicle, and the tension of the unmanned aerial vehicle is determined according to the rotating speed of the motor corresponding to each normal blade and the pitch angle and the roll angle of the unmanned aerial vehicle.
The estimation module 73 is specifically configured to acquire a vertical speed of the unmanned aerial vehicle, and determine a resistance of the unmanned aerial vehicle according to the vertical speed of the unmanned aerial vehicle and the determined resistance coefficient; and estimating the mass of the unmanned aerial vehicle according to the tension and the gravity acceleration of the unmanned aerial vehicle and the resistance of the unmanned aerial vehicle.
The estimation module 73 is specifically configured to acquire a vertical acceleration of the unmanned aerial vehicle, and estimate the mass of the unmanned aerial vehicle according to the tension of the unmanned aerial vehicle, the acceleration of gravity, the resistance of the unmanned aerial vehicle, and the vertical acceleration of the unmanned aerial vehicle.
The estimation module 73 is specifically configured to estimate the mass of the unmanned aerial vehicle based on an iterative least square method according to the tension of the unmanned aerial vehicle, the gravitational acceleration, the resistance of the unmanned aerial vehicle, and the vertical acceleration of the unmanned aerial vehicle.
The estimation module 73 is specifically configured to be based on
Figure BDA0003073381920000171
Estimating a mass of the drone, wherein V > 0 represents upward movement of the drone and V < 0 represents downward movement of the drone; v is the vertical speed of the unmanned aerial vehicle, g is the acceleration of gravity, azIs the vertical acceleration, kfThe coefficient of resistance is m, the mass of the unmanned aerial vehicle is F, the tension of the unmanned aerial vehicle is theta, the pitch angle of the unmanned aerial vehicle is theta, and phi is the roll angle of the unmanned aerial vehicle;
definition of
Figure BDA0003073381920000172
b＝F·cosθ·cosφ；
Calculating h as a.h + c.ATA, wherein a is a preset memory coefficient, and c is a preset weighting coefficient; calculating covariance matrix P ═ h-1(ii) a Calculating gain matrix K ═ P · AT；
According to x ═ xlast+K·(b-bpredict) Estimating the mass of the drone, wherein bpredict＝Ax。
The device further comprises:
a first determining module 74 for calculating h '═ h'last+ATA, judging whether h' calculated within a preset first time length is reversible, if so, triggering the estimation module 73 to perform subsequent calculation of h ═ a · h + c · aTAnd A.
The device further comprises:
a calculation module 75 for calculating the covariance matrix P '═ h'-1(ii) a Calculating the information matrix S ═ Slast+ATb; an initial mass estimation value x 'P' S is calculated.
The device further comprises:
a second judging module 76, configured to judge whether a difference between any two qualities of the unmanned aerial vehicle calculated within a preset second duration is smaller than a preset threshold, and if so, determine that the quality of the unmanned aerial vehicle is valid; if not, determining that the quality of the unmanned aerial vehicle is invalid, adjusting the preset memory coefficient, or the preset weighting coefficient, or the preset first duration, and re-estimating the quality of the unmanned aerial vehicle.
Example 7:
on the basis of the foregoing embodiments, an embodiment of the present invention further provides an electronic device, as shown in fig. 8, including: the system comprises a processor 301, a communication interface 302, a memory 303 and a communication bus 304, wherein the processor 301, the communication interface 302 and the memory 303 complete mutual communication through the communication bus 304;
the memory 303 has stored therein a computer program which, when executed by the processor 301, causes the processor 301 to perform the steps of:
acquiring a pulse width modulation pwm value of each motor in the unmanned aerial vehicle;
for each motor, when the pwm value of the motor is greater than a preset first threshold value and the pwm value of a diagonal motor of the motor is less than a preset second threshold value, determining that the blade corresponding to the motor is an abnormal blade; otherwise, determining the blade corresponding to the motor as a normal blade; wherein the preset first threshold is greater than the preset second threshold;
and determining the pulling force of the unmanned aerial vehicle according to the rotating speed of the motor corresponding to each normal blade, and estimating the mass of the unmanned aerial vehicle according to the pulling force and the gravity acceleration of the unmanned aerial vehicle.
Based on the same inventive concept, the embodiment of the invention also provides the electronic equipment, and as the principle of solving the problems of the electronic equipment is similar to the unmanned aerial vehicle quality estimation method, the implementation of the electronic equipment can refer to the implementation of the method, and repeated parts are not repeated.
The electronic device provided by the embodiment of the invention can be a desktop computer, a portable computer, a smart phone, a tablet computer, a Personal Digital Assistant (PDA), a network side device and the like.
The communication bus mentioned in the electronic device may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The communication bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown, but this does not mean that there is only one bus or one type of bus.
The communication interface 302 is used for communication between the above-described electronic apparatus and other apparatuses.
The Memory may include a Random Access Memory (RAM) or a Non-Volatile Memory (NVM), such as at least one disk Memory. Alternatively, the memory may be at least one memory device located remotely from the processor.
The Processor may be a general-purpose Processor, including a central processing unit, a Network Processor (NP), and the like; but may also be a Digital Signal Processor (DSP), an application specific integrated circuit, a field programmable gate array or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or the like.
When the processor executes the program stored in the memory in the embodiment of the invention, the pulse width modulation pwm value of each motor in the unmanned aerial vehicle is obtained; for each motor, when the pwm value of the motor is greater than a preset first threshold value and the pwm value of a diagonal motor of the motor is less than a preset second threshold value, determining that the blade corresponding to the motor is an abnormal blade; otherwise, determining the blade corresponding to the motor as a normal blade; wherein the preset first threshold is greater than the preset second threshold; and determining the pulling force of the unmanned aerial vehicle according to the rotating speed of the motor corresponding to each normal blade, and estimating the mass of the unmanned aerial vehicle according to the pulling force and the gravity acceleration of the unmanned aerial vehicle. In the embodiment of the invention, the normal blades and the abnormal blades in the unmanned aerial vehicle are determined according to the pwm value of each motor in the unmanned aerial vehicle. Then confirm unmanned aerial vehicle's pulling force according to the rotational speed of the motor that every normal paddle corresponds, according to unmanned aerial vehicle's pulling force and acceleration of gravity, estimate unmanned aerial vehicle's quality. The influence of the motor rotating speed corresponding to the abnormal paddle on the tension of the unmanned aerial vehicle is avoided, so that the tension of the unmanned aerial vehicle is determined more accurately, and the quality of the unmanned aerial vehicle is estimated more accurately.
Example 8:
on the basis of the foregoing embodiments, an embodiment of the present invention further provides a computer storage readable storage medium, in which a computer program executable by an electronic device is stored, and when the program is run on the electronic device, the electronic device is caused to execute the following steps:
acquiring a pulse width modulation pwm value of each motor in the unmanned aerial vehicle;
for each motor, when the pwm value of the motor is greater than a preset first threshold value and the pwm value of a diagonal motor of the motor is less than a preset second threshold value, determining that the blade corresponding to the motor is an abnormal blade; otherwise, determining the blade corresponding to the motor as a normal blade; wherein the preset first threshold is greater than the preset second threshold;
and determining the pulling force of the unmanned aerial vehicle according to the rotating speed of the motor corresponding to each normal blade, and estimating the mass of the unmanned aerial vehicle according to the pulling force and the gravity acceleration of the unmanned aerial vehicle.
Based on the same inventive concept, embodiments of the present invention further provide a computer-readable storage medium, and since a principle of solving a problem when a processor executes a computer program stored in the computer-readable storage medium is similar to that of the method for estimating the quality of the unmanned aerial vehicle, implementation of the computer program stored in the computer-readable storage medium by the processor may refer to implementation of the method, and repeated details are not repeated.
The computer readable storage medium may be any available medium or data storage device that can be accessed by a processor in an electronic device, including but not limited to magnetic memory such as floppy disks, hard disks, magnetic tape, magneto-optical disks (MOs), etc., optical memory such as CDs, DVDs, BDs, HVDs, etc., and semiconductor memory such as ROMs, EPROMs, EEPROMs, non-volatile memory (NAND FLASH), Solid State Disks (SSDs), etc.
The computer program is stored in a computer readable storage medium provided in the embodiment of the present invention, and when executed by a processor, the computer program realizes obtaining a pwm value of each motor in the drone; for each motor, when the pwm value of the motor is greater than a preset first threshold value and the pwm value of a diagonal motor of the motor is less than a preset second threshold value, determining that the blade corresponding to the motor is an abnormal blade; otherwise, determining the blade corresponding to the motor as a normal blade; wherein the preset first threshold is greater than the preset second threshold; and determining the pulling force of the unmanned aerial vehicle according to the rotating speed of the motor corresponding to each normal blade, and estimating the mass of the unmanned aerial vehicle according to the pulling force and the gravity acceleration of the unmanned aerial vehicle. In the embodiment of the invention, the normal blades and the abnormal blades in the unmanned aerial vehicle are determined according to the pwm value of each motor in the unmanned aerial vehicle. Then confirm unmanned aerial vehicle's pulling force according to the rotational speed of the motor that every normal paddle corresponds, according to unmanned aerial vehicle's pulling force and acceleration of gravity, estimate unmanned aerial vehicle's quality. The influence of the motor rotating speed corresponding to the abnormal paddle on the tension of the unmanned aerial vehicle is avoided, so that the tension of the unmanned aerial vehicle is determined more accurately, and the quality of the unmanned aerial vehicle is estimated more accurately.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.

Claims (22)

1. A method for unmanned aerial vehicle quality estimation, the method comprising:
acquiring a pulse width modulation pwm value of each motor in the unmanned aerial vehicle;
for each motor, when the pwm value of the motor is greater than a preset first threshold value and the pwm value of a diagonal motor of the motor is less than a preset second threshold value, determining that the blade corresponding to the motor is an abnormal blade; otherwise, determining the blade corresponding to the motor as a normal blade; wherein the preset first threshold is greater than the preset second threshold;
and determining the pulling force of the unmanned aerial vehicle according to the rotating speed of the motor corresponding to each normal blade, and estimating the mass of the unmanned aerial vehicle according to the pulling force and the gravity acceleration of the unmanned aerial vehicle.
2. The method according to claim 1, wherein for each motor, when the pwm value of the motor is greater than a preset first threshold value and the pwm value of the diagonal motor of the motor is less than a preset second threshold value, determining that the blade corresponding to the motor is an abnormal blade; otherwise, determining that the blade corresponding to the motor is a normal blade comprises the following steps:
for each motor, determining that the pwm value of the motor is greater than a preset first threshold within a preset detection time or within a preset detection duration, and determining that the blade corresponding to the motor is an abnormal blade when the pwm value of the diagonal motor of the motor is less than a preset second threshold; otherwise, determining the blade corresponding to the motor as a normal blade.
3. The method of claim 1, wherein determining the tension of the drone as a function of the speed of rotation of the motor for each normal blade comprises:
the pitch angle and the roll angle of the unmanned aerial vehicle are obtained, and the pitch angle and the roll angle of the unmanned aerial vehicle are determined according to the rotating speed of a motor corresponding to each normal blade.
4. The method of claim 1 or 3, wherein said estimating the mass of the drone as a function of the tension and the gravitational acceleration of the drone comprises:
acquiring the vertical speed of the unmanned aerial vehicle, and determining the resistance of the unmanned aerial vehicle according to the vertical speed of the unmanned aerial vehicle and the determined resistance coefficient;
and estimating the mass of the unmanned aerial vehicle according to the tension and the gravity acceleration of the unmanned aerial vehicle and the resistance of the unmanned aerial vehicle.
5. The method of claim 4, wherein estimating the mass of the drone as a function of the tension of the drone, the acceleration of gravity, and the drag of the drone comprises:
and acquiring the vertical acceleration of the unmanned aerial vehicle, and estimating the mass of the unmanned aerial vehicle according to the pulling force and the gravity acceleration of the unmanned aerial vehicle, the resistance of the unmanned aerial vehicle and the vertical acceleration of the unmanned aerial vehicle.
6. The method of claim 5, wherein estimating the mass of the drone as a function of the tension of the drone, the acceleration of gravity, the resistance of the drone, and the vertical acceleration of the drone comprises:
and estimating the mass of the unmanned aerial vehicle based on an iterative least square method according to the tension and the gravity acceleration of the unmanned aerial vehicle, the resistance of the unmanned aerial vehicle and the vertical acceleration of the unmanned aerial vehicle.
7. The method of claim 6, wherein estimating the mass of the drone based on an iterative least squares method based on the tension of the drone, the acceleration of gravity, the resistance of the drone, and the vertical acceleration of the drone comprises:
according to
Figure FDA0003073381910000021
Estimating a mass of the drone, wherein V > 0 represents upward movement of the drone and V < 0 represents downward movement of the drone; v is the vertical speed of the unmanned aerial vehicle, g is the acceleration of gravity, azIs the vertical acceleration, kfThe coefficient of resistance is m, the mass of the unmanned aerial vehicle is F, the tension of the unmanned aerial vehicle is theta, the pitch angle of the unmanned aerial vehicle is theta, and phi is the roll angle of the unmanned aerial vehicle;
definition of
Figure FDA0003073381910000022
b＝F·cosθ·cosφ；
Calculating h as a.h + c.ATA, wherein a is a preset memory coefficient, and c is a preset weighting coefficient; calculating covariance matrix P ═ h-1(ii) a Calculating gain matrix K ═ P · AT；
According to x ═ xlast+K·(b-bpredict) Estimating the mass of the drone, wherein bpredict＝Ax。
8. The method of claim 7, wherein the defining is performed
Figure FDA0003073381910000023
Figure FDA0003073381910000031
After b is F · cos θ · cos Φ, h is calculated as a · h + c · aTBefore A, the method further comprises:
calculating h ═ h'last+ATA, judging whether h' calculated in a preset first time length is reversible or not, and if so, carrying out subsequent calculation of h as a.h + c.ATAnd A.
9. The method of claim 8, wherein the calculating h '═ h'last+ATAfter a, the method further comprises:
calculating covariance matrix P '═ h'-1(ii) a Calculating the information matrix S ═ Slast+ATb; an initial mass estimation value x 'P' S is calculated.
10. The method of claim 8, wherein said criterion x ═ x is satisfiedlast+K·(b-bpredict) After estimating the quality of the drone, the method further comprises:
judging whether the difference value of any two qualities of the unmanned aerial vehicle obtained by calculation in a preset second time length is smaller than a preset threshold value, and if so, determining that the quality of the unmanned aerial vehicle is effective; if not, determining that the quality of the unmanned aerial vehicle is invalid, adjusting the preset memory coefficient, or the preset weighting coefficient, or the preset first duration, and re-estimating the quality of the unmanned aerial vehicle.
11. An unmanned aerial vehicle quality estimation device, the device comprising:
the acquisition module is used for acquiring a pulse width modulation pwm value of each motor in the unmanned aerial vehicle;
the determining module is used for determining that the blade corresponding to the motor is an abnormal blade when the pwm value of the motor is greater than a preset first threshold and the pwm value of the diagonal motor of the motor is less than a preset second threshold for each motor; otherwise, determining the blade corresponding to the motor as a normal blade; wherein the preset first threshold is greater than the preset second threshold;
and the estimation module is used for determining the tension of the unmanned aerial vehicle according to the rotating speed of the motor corresponding to each normal blade, and estimating the mass of the unmanned aerial vehicle according to the tension and the gravity acceleration of the unmanned aerial vehicle.
12. The apparatus according to claim 11, wherein the determining module is specifically configured to determine, for each motor, that a pwm value of the motor is greater than a preset first threshold within a preset detection time or a preset detection duration, and when the pwm value of a diagonal motor of the motor is smaller than a preset second threshold, determine that the blade corresponding to the motor is an abnormal blade; otherwise, determining the blade corresponding to the motor as a normal blade.
13. The apparatus according to claim 11, wherein the estimation module is specifically configured to obtain a pitch angle and a roll angle of the drone, and determine the tension of the drone according to a rotation speed of a motor corresponding to each normal blade, and the pitch angle and the roll angle of the drone.
14. The apparatus according to claim 11 or 13, wherein the estimation module is specifically configured to obtain a vertical velocity of the drone, determine a drag of the drone according to the vertical velocity of the drone and the determined drag coefficient; and estimating the mass of the unmanned aerial vehicle according to the tension and the gravity acceleration of the unmanned aerial vehicle and the resistance of the unmanned aerial vehicle.
15. The apparatus according to claim 14, wherein the estimation module is specifically configured to obtain a vertical acceleration of the drone, and to estimate the mass of the drone based on the tension of the drone, the acceleration of gravity, the resistance of the drone, and the vertical acceleration of the drone.
16. The apparatus according to claim 15, wherein the estimation module is specifically configured to estimate the mass of the drone based on an iterative least squares method based on the tension of the drone, the acceleration of gravity, the resistance of the drone and the vertical acceleration of the drone.
17. The apparatus according to claim 16, wherein the estimation module is specifically configured to be based on
Figure FDA0003073381910000041
Estimating a mass of the drone, wherein V > 0 represents upward movement of the drone and V < 0 represents downward movement of the drone; v is the vertical speed of the unmanned aerial vehicle, g is the acceleration of gravity, azIs the vertical acceleration, kfThe coefficient of resistance is m, the mass of the unmanned aerial vehicle is F, the tension of the unmanned aerial vehicle is theta, the pitch angle of the unmanned aerial vehicle is theta, and phi is the roll angle of the unmanned aerial vehicle;
definition of
Figure FDA0003073381910000042
b＝F·cosθ·cosφ；
Calculating h as a.h + c.ATA, wherein a is a preset memory coefficient, and c is a preset weighting coefficient; calculating covariance matrix P ═ h-1(ii) a Calculating gain matrix K ═ P · AT；
According to x ═ xlast+K·(b-bpredict) Estimating the mass of the drone, wherein bpredict＝Ax。
18. The apparatus of claim 17, wherein the apparatus further comprises:
first judging moduleBlock for calculating h '═ h'last+ATA, judging whether h' calculated in a preset first time length is reversible or not, if so, triggering the estimation module to perform subsequent calculation on h ═ a · h + c · ATAnd A.
19. The apparatus of claim 18, wherein the apparatus further comprises:
a calculation module for calculating a covariance matrix P '═ h'-1(ii) a Calculating the information matrix S ═ Slast+ATb; an initial mass estimation value x 'P' S is calculated.
20. The apparatus of claim 18, wherein the apparatus further comprises:
the second judgment module is used for judging whether the difference value of any two qualities of the unmanned aerial vehicle calculated in a preset second time length is smaller than a preset threshold value or not, and if so, determining that the quality of the unmanned aerial vehicle is effective; if not, determining that the quality of the unmanned aerial vehicle is invalid, adjusting the preset memory coefficient, or the preset weighting coefficient, or the preset first duration, and re-estimating the quality of the unmanned aerial vehicle.
21. An electronic device is characterized by comprising a processor, a communication interface, a memory and a communication bus, wherein the processor and the communication interface are used for realizing mutual communication by the memory through the communication bus;
a memory for storing a computer program;
a processor for implementing the method steps of any one of claims 1 to 10 when executing a program stored in the memory.
22. A computer-readable storage medium, characterized in that a computer program is stored in the computer-readable storage medium, which computer program, when being executed by a processor, carries out the method steps of any one of claims 1-10.
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