CN114237279B - A wind speed and direction detector based on a multi-rotor UAV and its detection method - Google Patents

Abstract

The invention belongs to the field of meteorological detection and the field of aircrafts, and discloses a wind speed and direction detector based on a multi-rotor unmanned aerial vehicle and a detection method thereof. The data acquisition unit comprises a flight data acquisition module and a control signal acquisition module; the flight control unit comprises a flight attitude control module and a flight position control unit. The real-time wind speed and direction output unit inputs the control signal data of the onboard central processing unit and the wind traveling data of the unmanned aerial vehicle, which are acquired in real time by the data acquisition unit, into a trained model to obtain the real-time wind speed and direction. The wind speed and direction detector does not need an additional sensor, can reduce dead weight of the unmanned aerial vehicle during working, and can enhance endurance. The wind speed and wind direction detection method is based on the data of the unmanned aerial vehicle, and is not easy to be interfered by the environment.

Description

A wind speed and direction detector based on a multi-rotor UAV and its detection method

Technical field

The invention relates to the field of meteorological detection and aircraft, and specifically to a wind speed and direction detector based on a multi-rotor unmanned aerial vehicle and a detection method thereof.

Background technique

Due to its low cost, easy deployment and high maneuverability, multi-rotor UAVs can make up for the shortcomings of traditional meteorological detection methods in small time ranges and small spatial scales, provide data sources for detailed meteorological detection, and play an important role in the field of meteorological detection. played an important role. However, the traditional weather detection method based on multi-rotor UAVs is to carry meteorological payloads on multi-rotor UAVs, such as wind speed sensors and wind direction sensors. Multi-rotor UAVs have problems such as poor endurance, small payload, small payload installation space, and poor wind resistance and harsh environment resistance. Traditional meteorological drones need to be equipped with many sensors on the drone, which places a heavy load on the drone; moreover, the wind measurement equipment on the multi-rotor drone platform will be interfered by rotor turbulence, affecting the accuracy of weather detection.

At present, most modifications to multi-rotor UAVs for weather detection are limited to wind resistance and increasing the load installation space. There is little research on how meteorological loads can avoid interference with the wind field of UAV rotors. The Shen Ao team of the National University of Defense Technology proposed a method to reduce the wind field interference of drones, but this method only relatively significantly improved the detection accuracy of temperature and humidity. It has no significant effect on improving the detection accuracy of wind speed and direction.

Contents of the invention

The purpose of the present invention is to propose a wind speed and direction detector based on a multi-rotor UAV and a detection method thereof in view of the shortcomings of the existing technology.

The scheme of the present invention is as follows:

A wind speed and wind direction detector based on a multi-rotor UAV, including a data acquisition unit, a functional relationship fitting unit, a flight control unit and a real-time wind speed and wind direction output unit. The data acquisition unit includes a flight attitude acquisition module and a control signal acquisition module. ;

The flight attitude acquisition module is used to obtain the flight attitude data and flight position data of the UAV, and transfer them to the airborne central processor;

The control signal acquisition module records the control signal for controlling the hovering of the drone through the onboard central processor;

The functional relationship fitting unit is established on the mapping relationship between the UAV rotor speed and the lift force F experienced by the UAV; based on the mapping relationship between the UAV rotor speed and the motor speed, the motor speed and the airborne central processing There is a mapping relationship between the control signal of the drone and the control signal of the airborne central processor. According to the lift force F when the drone is hovering in an indoor windless environment, it is equal to the gravity of the drone. Perform function fitting to obtain the functional relationship between the lift force F experienced by the drone and the control signal of the airborne central processor;

The flight control unit is used to control the flight of the aircraft and maintain the stability of the flight of the aircraft. The flight attitude data and flight position data of the UAV are processed by the central processor, and control instructions are sent to the controller to adjust the flight attitude and flight position of the aircraft through the controller; the controller is used to receive the control signal of the onboard central processor, control the speed of the motor, and thus adjust the flight attitude and flight position of the aircraft;

The real-time wind speed and direction output unit collects the control signal data of the airborne central processor in real time through the data acquisition unit, and obtains the unmanned aerial vehicle based on the functional relationship between the lift force F received by the unmanned aerial vehicle and the control signal of the airborne central processor. The amount of lift experienced when hovering in a windy environment; the direction of the wind force experienced by the drone can be obtained through the flight attitude of the drone collected in real time by the data acquisition unit.

Further, the data collection unit includes a gyroscope, a barometer and a GPS module.

Further, the flight control unit includes a flight attitude control module and a flight position control module. The flight attitude control module is used to control the UAV to maintain its yaw angle within a prescribed error range within a prescribed time in a windy environment. The flight position control module is used to control the drone to maintain its altitude and GPS position within a specified error range within a specified period of time in a windy environment.

The invention also discloses a wind speed and direction detection method based on a multi-rotor unmanned aerial vehicle, which calculates wind force based on control signals that control the stability of the unmanned aerial vehicle.

Further, a functional relationship fitting algorithm is included, and the functional relationship fitting algorithm is specifically:

Carry out UAV hovering simulation in an indoor windless environment, measure the rotor speed required for UAV hovering, change the weight of the aircraft, measure multiple sets of data, and fit the curve to obtain the mathematical relationship between the rotor speed and the lift it provides. There is a linear relationship between the rotor speed and the motor speed, and there is a one-to-one mapping relationship between the motor speed and the airborne central processor control signal. What is actually obtained is the mathematical relationship between the airborne central processor control signal and the rotor lift.

Further, it includes flight attitude control, which uses the flight control unit to make the UAV hover in a windy environment; the data collected by the flight attitude acquisition module includes the UAV's yaw angle, GPS position and altitude, and is controlled using a control algorithm. The UAV keeps its flight attitude and position unchanged under wind interference, that is, the UAV's yaw angle, GPS position, altitude and other data remain unchanged. At this time, the UAV is in a pitch and hover state.

Further, the flight attitude control uses the least squares method to calculate the flight data difference between the two moments of the UAV. When the value is less than the allowable error Δ and lasts for the specified time t, the UAV’s flight attitude and flight position are considered Stable, assuming the yaw angle of the UAV is α, when the following formula is satisfied, the forward direction of the UAV is considered to have not changed:

(α ₁ -α ₂ ) ² <Δ

(α ₁ -α ₃ ) ² <Δ

(α ₁ -α _n ) ² <Δ

t ₁ -t _n >t

Further, a wind force detection algorithm is included. The wind force detection algorithm is as follows: the flight control module is used to make the drone hover in a windy environment; the airborne central processor is used to obtain the values of the motors corresponding to each rotor when hovering. Control signal; according to the mathematical relationship between the above control signal and the rotor lift, the lift provided by each rotor is obtained. According to the lift and the weight of the drone, the wind force F _w on the drone can be obtained. The formula is as follows:

F·cosθ＝mg

F _w =F·sinθ.

Furthermore, it includes a wind direction detection algorithm, which is as follows: the flight control module obtains the aircraft altitude and aircraft deflection angle information by reading the values of internal sensor elements, and transmits the information to the onboard central processor, and obtains the forward direction of the drone when hovering in a windy environment, that is, the opposite direction of the wind direction, based on the deflection angle and the initial forward direction of the aircraft.

The beneficial effects of the present invention are:

(1) The wind speed and direction detector of the present invention does not require additional sensors and directly obtains wind speed and wind direction from the rotor speed, which reduces the weight of the UAV during operation, enhances endurance, and improves wind measurement accuracy.

(2) The wind speed and direction detection method of the present invention is based on the drone's own data and is not easily affected by environmental interference.

Description of drawings

Figure 1 is an overall structural block diagram of the present invention;

Figure 2(a) is a top view of the three-dimensional spatial coordinates of the UAV of the present invention;

Figure 2(b) is a front view of the three-dimensional space coordinates of the UAV of the present invention;

Figure 3 is a schematic diagram for calculating the wind force size according to the present invention;

Figure 4 is a schematic diagram of wind direction calculation according to the present invention.

Detailed ways

In order to explain the wind speed and direction detector and detection method based on the multi-rotor UAV of the present invention in more detail, the present invention will be described in detail below based on the accompanying drawings.

As shown in Figure 1, a wind speed and direction detector based on a multi-rotor UAV includes a data acquisition unit, a function relationship fitting unit, a flight control unit, and a real-time wind speed and direction output unit. in:

The data acquisition unit includes a flight attitude acquisition module and a control signal acquisition module.

The flight attitude acquisition module is used to acquire the flight attitude data and flight position data of the UAV, and transfer them to the airborne central processor; the flight attitude acquisition module includes a gyroscope, a barometer, and a GPS module.

The control signal acquisition module records the control signal for controlling the drone to hover through the onboard central processing unit.

The functional relationship fitting unit first establishes the mapping relationship between the UAV rotor speed and the lift force F experienced by the UAV; based on the mapping relationship between the UAV rotor speed and the motor speed, the motor speed and the airborne center There is a mapping relationship between the control signals of the processor, and it can be known that there is a mapping relationship between the lift force F experienced by the drone and the control signals of the airborne central processor. Function fitting is performed based on the fact that the lift force of the UAV when hovering in a windless indoor environment is equal to the gravity of the UAV, and the functional relationship between the lift force F experienced by the UAV and the control signal of the onboard central processor is obtained.

The flight control unit is used to control the flight of the aircraft and maintain the stability of the flight of the aircraft. The central processor processes the flight attitude data and flight position data of the drone, sends control instructions to the controller, and adjusts the flight attitude and flight position of the aircraft through the controller; the controller is used to receive control signals from the airborne central processor , control the speed of the motor to adjust the flight attitude and position of the aircraft. The flight control unit includes a flight attitude control module and a flight position control module.

The flight attitude control module is used to control the UAV to keep its yaw angle stable within a prescribed error range within a prescribed time in a windy environment.

The flight position control module is used to control the drone to maintain its altitude and GPS position within a specified error range within a specified period of time in a windy environment.

The real-time wind speed and direction output unit collects the control signal data of the airborne central processor in real time through the data acquisition unit. According to the functional relationship between the lift force F received by the drone and the control signal of the airborne central processor, the UAV is obtained. The amount of lift experienced when hovering in a windy environment; the flight attitude of the drone is collected in real time through the data acquisition unit to obtain the wind direction of the drone.

Further, the flight control unit maintains the stability of the flight of the aircraft. Specifically, the flight control module allows the UAV to hover in a windy environment; the flight attitude of the UAV will change in a windy environment, and the flight attitude will change. The data collected by the acquisition module includes the yaw angle, GPS position, altitude, etc. of the drone. The control algorithm is used to control the UAV to keep its flight attitude and position unchanged under wind interference, that is, to keep the UAV's yaw angle, GPS position, altitude and other data unchanged within the specified error range. At this time, there is no one The aircraft is in a pitch-hover state.

Furthermore, the yaw angle, GPS position, altitude and other data of the UAV are kept unchanged within a specified error range. Specifically, the flight data difference between two moments of the UAV is calculated using the least squares method. When the value is less than the allowable error △ and lasts for a specified time t, the flight attitude and flight position of the UAV are considered to be stable.

Furthermore, the wind force detection algorithm is as follows: the flight control module is used to make the drone hover in a windy environment; the control signal of the motor corresponding to each rotor during hovering is obtained through the onboard central processor; the lift provided by each rotor is obtained based on the mathematical relationship between the above control signal and the rotor lift. The wind force _Fw on the drone can be obtained based on the lift F and the drone's own weight mg. The formula is as follows:

F·cosθ＝mg

F _w =F·sinθ

Further, the wind direction detection algorithm is as follows: the flight control module obtains information such as aircraft altitude and aircraft deflection angle by reading the values of internal sensor elements, and transmits the information to the airborne central processor. Based on the deflection angle and the initial forward direction of the aircraft, the forward direction of the drone when hovering in a windy environment is obtained, that is, the opposite direction of the wind direction.

Specifically, a wind speed and direction detector based on a multi-rotor drone of the present invention can be only a multi-rotor drone with a flight control function and a central processing unit. The sensor used for flight control of the multi-rotor drone can be used as a flight attitude acquisition module of a data acquisition unit, and the central processing unit can be used as a real-time wind speed and direction output unit. The model training unit in the present invention can be completed on another device, and the obtained model can be directly input into the central processing unit.

Example

Let's take a quad-rotor drone with a flight control module and a central processor as an example. The specific working steps are as follows:

1) Functional relationship fitting: As shown in Figure 2(a) and 2(b), it is a schematic diagram of the three-dimensional spatial coordinates of the UAV of the present invention. The UAV hovering simulation is performed in an indoor windless environment to measure the unmanned The rotor speed required for the aircraft to hover. Change the weight of the aircraft, measure multiple sets of data, and fit the curve to obtain the mathematical relationship between the rotor speed and the lift it provides. Among them, the rotor speed has a linear relationship with the motor speed, and the motor speed has a one-to-one mapping relationship with the airborne central processor control signal. What is actually obtained is the mathematical relationship between the airborne central processor control signal and the rotor lift.

2) Flight attitude control: The flight control unit makes the UAV hover in a windy environment; specifically, the UAV's flight attitude will change in a windy environment, and the data collected by the flight attitude acquisition module includes: Yaw angle, GPS position, altitude, etc. of the human machine. The control algorithm is used to control the UAV to maintain its flight attitude and flying position unchanged under wind interference, that is, to keep the UAV's yaw angle, GPS position, altitude and other data unchanged. At this time, the UAV is in pitch and hover. state.

Furthermore, the least squares method is used to calculate the flight data difference between the two moments of the UAV. When the value is less than the allowable error Δ and lasts for the specified time t, the UAV's flight attitude and position are considered stable. Taking the yaw angle α of the UAV as an example, when the following formula is satisfied, the UAV’s forward direction is considered to have not changed:

(α ₁ -α ₂ ) ² <Δ

(α ₁ -α ₃ ) ² <Δ

(α ₁ -α _n ) ² <Δ

t ₁ -t _n >t

3) Wind detection algorithm: As shown in Figure 3, the flight control unit is used to control the drone's flight attitude to maintain hovering in a windy environment, and the control of the corresponding motor of each rotor during hovering is obtained through the airborne central processor. Signal; the lift provided by each rotor is obtained based on the mathematical relationship between the above control signal and the rotor lift. According to the lift force and the weight of the drone, the wind force F _w on the drone can be obtained. The formula is as follows:

F·cosθ＝mg

F _w =F·sinθ

4) Wind direction detection algorithm: As shown in Figure 4, the flight control module obtains information such as aircraft altitude and aircraft deflection angle by reading the values of internal sensor elements, and transmits the information to the airborne central processor. Based on the deflection angle and the initial forward direction of the aircraft, the forward direction of the drone when hovering in a windy environment is obtained, that is, the opposite direction of the wind direction.

The above embodiments are used to illustrate the present invention, rather than to limit the present invention. Within the spirit of the present invention and the protection scope of the claims, any modifications and changes made to the present invention fall within the protection scope of the present invention.

Claims (3)

1. The wind speed and direction detector comprises a data acquisition unit, a functional relation fitting unit, a flight control unit and a real-time wind speed and direction output unit, wherein the data acquisition unit comprises a flight attitude acquisition module and a control signal acquisition module;

the flight attitude acquisition module is used for acquiring flight attitude data and flight position data of the unmanned aerial vehicle and transmitting the flight attitude data and the flight position data to the airborne central processing unit;

the control signal acquisition module records a control signal for controlling the hovering of the unmanned aerial vehicle through an onboard central processing unit;

the functional relation fitting unit establishes a mapping relation between the rotating speed of the rotor wing of the unmanned aerial vehicle and the lifting force F born by the unmanned aerial vehicle; based on the mapping relation between the rotation speed of the rotor wing of the unmanned aerial vehicle and the rotation speed of the motor, the mapping relation between the rotation speed of the motor and the control signal of the airborne central processing unit is obtained, the mapping relation between the lifting force F borne by the unmanned aerial vehicle and the control signal of the airborne central processing unit is obtained, and function fitting is carried out according to the fact that the lifting force of the unmanned aerial vehicle when hovering in an indoor windless environment is equal to the gravity of the unmanned aerial vehicle, so that the function relation between the lifting force F borne by the unmanned aerial vehicle and the control signal of the airborne central processing unit is obtained;

the flight control unit is used for controlling the flight of the aircraft, keeping the flight stability of the aircraft, processing the flight attitude data and the flight position data of the unmanned aerial vehicle through the central processing unit, sending a control instruction to the controller, and adjusting the flight attitude and the flight position of the aircraft through the controller; the controller is used for receiving a control signal of the onboard central processing unit and controlling the rotating speed of the motor so as to adjust the flight attitude and the flight position of the aircraft;

the real-time wind speed and direction output unit acquires control signal data of the airborne central processing unit in real time through the data acquisition unit, and obtains the lift force of the unmanned aerial vehicle when hovering in a windy environment according to the functional relation between the lift force F of the unmanned aerial vehicle and the control signal of the airborne central processing unit; the unmanned aerial vehicle flight attitude acquired in real time through the data acquisition unit obtains the wind direction born by the unmanned aerial vehicle;

the method is characterized in that wind power is calculated according to a control signal for controlling the stability of the unmanned aerial vehicle;

the method comprises a functional relation fitting algorithm, wherein the functional relation fitting algorithm specifically comprises the following steps:

performing unmanned aerial vehicle hover simulation in an indoor windless environment, measuring the rotating speed of a rotor required by unmanned aerial vehicle hover, changing the weight of an aircraft, measuring a plurality of groups of data, and fitting a curve to obtain the mathematical relationship between the rotating speed of the rotor and the lift force provided by the rotating speed of the rotor, wherein the rotating speed of the rotor and the rotating speed of a motor have a linear relationship, and the rotating speed of the motor and a control signal of an onboard central processor have a one-to-one mapping relationship, so that the mathematical relationship between the control signal of the onboard central processor and the lift force of the rotor is actually obtained; the unmanned aerial vehicle hovers in a windy environment through a flight control unit; the data acquired by the flight attitude acquisition module comprise the yaw angle, the GPS position and the height of the unmanned aerial vehicle, the unmanned aerial vehicle is controlled by a control algorithm to keep the flight attitude and the flight position unchanged under the condition of air interference, namely, the data such as the yaw angle, the GPS position and the height of the unmanned aerial vehicle are kept unchanged, and the unmanned aerial vehicle is in a pitching hovering state at the moment;

the flight attitude control calculates the flight data difference between two moments of the unmanned aerial vehicle by using a least square method, and when the flight data difference is smaller than an allowable error delta and lasts for a specified time t, the flight attitude and the flight position of the unmanned aerial vehicle are considered to be stable, the yaw angle of the unmanned aerial vehicle is set as alpha, and when the following formula is satisfied, the advancing direction of the unmanned aerial vehicle is considered to be unchanged:

(α ₁ -α ₂ ) ² <Δ

(α ₁ -α ₃ ) ² <Δ

(α ₁ -α _n ) ² <Δ

t ₁ -t _n >t；

the wind power generation method comprises a wind power magnitude detection algorithm, wherein the wind power magnitude detection algorithm is as follows: the unmanned aerial vehicle hovers in a windy environment through the flight control module; the control signals of the motors corresponding to each rotor wing during hovering are obtained through an onboard central processing unit; according to the mathematical relationship between the control signals and the lift force of the rotor wings, the lift force provided by each rotor wing is obtained, and according to the lift force and the weight of the unmanned aerial vehicle, the wind force F borne by the unmanned aerial vehicle can be obtained _w The size, formula is as follows:

F·cosθ＝mg

F _w ＝F·sinθ；

the wind power direction detection method comprises a wind power direction detection algorithm, wherein the wind power direction detection algorithm is as follows: the flight control module obtains the information of the aircraft height and the aircraft deflection angle by reading the numerical values of the internal sensor elements, transmits the information to the airborne central processing unit, and obtains the advancing direction of the unmanned aerial vehicle in the windy environment when the unmanned aerial vehicle hovers, namely the opposite direction of the wind direction according to the deflection angle and the initial advancing direction of the aircraft.

2. The method of claim 1, wherein the data acquisition unit comprises a gyroscope, a barometer, and a GPS module.

3. The method of claim 2, wherein the flight control unit comprises a flight attitude control module for controlling the drone to maintain its yaw angle stable within a specified error range for a specified time in a windy environment, and a flight position control module for controlling the drone to maintain its altitude and GPS position stable within a specified error range for a specified time in a windy environment.