WO2021213541A1

WO2021213541A1 - Method for assessing personal injury risk of unmanned aerial vehicle crash in out-of-control or power-loss fault state

Info

Publication number: WO2021213541A1
Application number: PCT/CN2021/095437
Authority: WO
Inventors: 韩鹏
Original assignee: 中国民航大学
Priority date: 2020-10-09
Filing date: 2021-05-24
Publication date: 2021-10-28
Also published as: CN112184042B; CN112184042A

Abstract

A method for assessing the personal injury risk of an unmanned aerial vehicle crash in an out-of-control or power-loss fault state. The method comprises the steps of: establishing a falling track prediction model for an unmanned aerial vehicle in an out-of-control or power-loss fault state; setting a boundary condition; calculating position coordinates of an unmanned aerial vehicle crash location; calculating the falling speed and falling kinetic energy of the unmanned aerial vehicle; and calculating the casualty rate of people on the ground, taking the casualty rate as a quantitative index, assessing the risk of the unmanned aerial vehicle at the unmanned aerial vehicle crash location, etc.

Description

Risk assessment method for unmanned aerial vehicle out of control or loss of power failure state and falling to the ground and hurting people

References to related applications

This disclosure requires all the rights and interests of the invention patent application filed with the State Intellectual Property Office of the People's Republic of China on October 9, 2020, with the application number 202011072742.7, titled "The Risk Assessment Method for the Risk of Falling to the Ground and Injury by the Drone Out of Control or Loss of Power Failure State" , And incorporate its entire content into this article by reference.

Technical field

The present disclosure belongs to the technical field of unmanned aerial vehicles, and in particular relates to a method for assessing the risk of an unmanned aerial vehicle falling to the ground and hurting people when it is out of control or loses power.

Background technique

The prediction of the area where the UAV falls and the estimation of the kinetic energy of the fall is a key link in assessing the risk of injury from its fall. Most of the current researches use the modeling of the UAV's failure trajectory to predict the landing area. The main modeling methods include empirical estimation method and modeling analysis method. Among them, the empirical estimation method is based on the weight, size and category of the UAV, referring to the historical data of manned aircraft or UAV crashes, to estimate the trajectory and affected area after the UAV fails. The empirical estimation method can be further divided into two methods based on aircraft weight and aircraft size and type. The modeling analysis method is mainly based on the aerodynamic model and the kinematic model to predict the fall track, and calculate the range of the affected area after the UAV fails.

There are two main problems in the prior art. One is that there is no model for predicting the trajectory of the unmanned aerial vehicle that is out of control or power failure; the other is that there is no model that considers the error of the failure boundary condition, including the initial failure state. Failure position error and failure initial velocity error.

However, as the input of failure trajectory modeling, it is first necessary to determine its initial failure location, initial failure velocity and related errors, and use this as the failure boundary condition. The current technology lacks this research. The initial position of the drone before it fails and falls has a significant impact on the accuracy of the prediction output, which is directly related to the risk of ground personnel. Therefore, it is necessary to carry out research on the risk assessment method of unmanned aerial vehicle's loss of control or loss of power failure state based on trajectory prediction.

Public content

The risk assessment method of the unmanned aerial vehicle falling out of control or losing power failure state provided by the present disclosure includes the following steps in order:

1) Establish a prediction model for the landing track when the UAV is out of control or power failure;

2) Set the boundary conditions when the UAV is out of control or loses power failure state;

3) Substitute the boundary conditions of the UAV in the out-of-control or power-off failure state determined in step 2) into step 1) to calculate the UAV's trajectory prediction model established in the out-of-control or power-off failure state of the UAV. The location coordinates of the crash site, and analyze the error distribution at the crash site;

4) Substituting the boundary conditions of the unmanned aerial vehicle determined in step 2) in the out-of-control or out-of-power failure state into the step 1) established UAV out-of-control or out-of-power failure state in the landing track prediction model to calculate the UAV Falling speed and falling kinetic energy; and

5) According to the kinetic energy of UAV falling in step 4), combined with the protection coefficient of ground shelter and the energy threshold of casualties, calculate the casualty rate of ground personnel, and use this as a quantitative index to evaluate the drone in step 3) Obtain the risk at the location where the drone fell.

In step 1), the predictive model of the flight path when the UAV is out of control or power failure state is:

Where: m is the mass of the drone, in kg; g is the acceleration of gravity, in m/s ² ; x, y, and h are the displacements in the longitudinal, lateral and height directions, in m; t is none The falling time of the man-machine, the unit is s; D _x , D _y , and D _h are the air resistance in the longitudinal, lateral and height directions, and the unit is N;

Expanding the air resistance with the windward area of the drone and the air resistance coefficient, equation (1) can be expressed as equation (2):

Where: c _D is the air resistance coefficient; ρ _A is the density of air is ^{_{kg / m 3; A x,}} A y, A h is the frontal area of each longitudinal, lateral and height directions, in units of m ^2.

In step 2), the boundary conditions of the unmanned aerial vehicle in the out-of-control or power-loss fault state are shown in formula (3):

Where: x, y, h, v are the initial longitudinal position, lateral position, height position and initial velocity _{of the failure respectively; x 0} , y ₀ , h ₀ , v ₀ are the longitudinal coordinate and lateral direction of the initial failure prediction point, respectively Coordinates, height coordinates and the predicted value of the initial failure speed; ε _x , ε _y , ε _h , and ε _v are the longitudinal coordinate error, the lateral coordinate error, the height coordinate error and the velocity error, respectively.

In step 3), the boundary conditions determined in step 2) are substituted for the unmanned aerial vehicle's out-of-control or power-loss fault state determined in step 1) to establish the unmanned aerial vehicle's out-of-control or power-off fault condition. In, the method to calculate the location coordinates of the drone's crash site is:

Take the coordinates of the initial prediction point of failure (x ₀ , y ₀ , h ₀ ) as the origin to establish the UAV trajectory prediction coordinate system, then step 2) The unmanned aerial vehicle described in formula (3) is out of control or loses power. The boundary conditions can be expressed as formula (4):

Incorporating the integral of formula (2) into formula (4), the formula for calculating the crash location of the drone as shown in formulas (5)—(7) is obtained:

Where v _0x and v _0y are respectively the predicted value of the initial failure speed. v ₀ is the longitudinal and lateral velocity components in the UAV track prediction coordinate system; ε _vx and ε _vy are the velocity errors ε _v in the unmanned The longitudinal and lateral velocity error components in the aircraft trajectory prediction coordinate system;

From equation (7), it can be calculated that when the drone's operating altitude is h ₀ , its landing time is:

Substituting formula (8) into formula (5) and formula (6), the running distance of the UAV in the longitudinal and lateral directions is calculated as:

In step 4), the landing speed of the drone is calculated according to formula (10):

The kinetic energy of UAV falling to the ground is calculated according to formula (11):

In the formula, E is the kinetic energy of the drone falling to the ground, and the unit is J.

In step 5), the formula for calculating the casualty rate of ground personnel is:

In the formula: P _f is the casualty rate of ground personnel; P _s is the protection coefficient of ground shelters, and the value is 1; α is the impact energy required for the casualty rate of 50% _{when P s =6, and the value is 100kJ} ; Β is _{the energy threshold for casualties when P s} tends to 0, and the value is 34J; E is the kinetic energy of the drone when the drone is out of control or power failure; k is the correction factor.

Certain embodiments of the method for assessing the risk of a person falling to the ground in a failure state of a drone provided by the present disclosure have the following beneficial effects: the failure or loss of power state of the drone is considered in the process of establishing a failed ground trajectory prediction model. Accurate kinematics behavior, and in the process of setting prediction boundary conditions, considering the error of the initial failure position and the error of the initial failure velocity, it can more accurately predict the crash location and kinetic energy of the UAV. This method can be used for the UAV to fall to the ground. Quantitative assessment of injury risk provides more effective support.

Description of the drawings

Fig. 1 is a schematic diagram of the boundary conditions of the unmanned aerial vehicle in the state of failure of control or loss of power in the present disclosure.

Figure 2 is a schematic diagram of the UAV track prediction coordinate system in this disclosure.

Figure 3 is a schematic diagram of the UAV track prediction coordinate system in this disclosure.

Detailed ways

The present disclosure will be described in detail below with reference to the drawings and specific embodiments.

The risk assessment method of the unmanned aerial vehicle falling out of control or losing power failure state provided by the present disclosure includes the following steps 1), 2), 3), 4) and 5) in order:

Simulate the landing track of the UAV under the condition of loss of control or power failure as a free fall motion or projectile motion model, and consider the air resistance during the fall to analyze the force of the UAV. In the modeling process, the influence of wind on the UAV's falling process is ignored. The motion of the UAV after falling is decomposed into longitudinal, lateral and height directions, and its motion equation is shown in equation (1), and this is used as the prediction model of the UAV's falling track in the state of loss of control or power failure. .

Where: m is the mass of the drone, in kg; g is the acceleration of gravity, in m/s ² ; x, y, and h are the displacements in the longitudinal, lateral and height directions, in m; t is none The falling time of the man-machine, the unit is s; D _x , D _y , and D _h are the air resistance in the longitudinal, lateral and height directions, and the unit is N.

The boundary conditions of the failure state of the unmanned aerial vehicle out of control or loss of power are shown in Fig. 1, including the initial position of failure and the initial velocity of failure. The initial position of failure includes longitudinal position, lateral position and height. The initial failure position is composed of the coordinates of the initial failure prediction point and the coordinate error. The initial failure velocity is composed of the initial failure velocity prediction value and the velocity error. The boundary conditions are shown in equation (3).

The position error of the UAV follows the three-dimensional Gaussian distribution law with a mean value of zero, and the position error is independent of each other in the longitudinal, lateral and height directions. The speed error of the UAV is determined by its design performance, usually within ±20%, given by the design parameters.

The longitudinal coordinate error, lateral coordinate error, altitude coordinate error and speed error of the UAV all follow the normal distribution law, which is determined by the design performance of the UAV and is given by the design parameters.

Take the coordinates of the initial prediction point of failure (x ₀ , y ₀ , h ₀ ) as the origin to establish the UAV track prediction coordinate system as shown in Figure 2, then step 2) The UAV described in equation (3) is out of control or The boundary conditions under the power-loss fault state can be expressed as formula (4):

In the formula, v _0x and v _0y are respectively the longitudinal and lateral velocity components of the predicted value v _{0 of the} initial failure velocity in the UAV track prediction coordinate system. ε _vx and ε _vy are respectively the longitudinal and lateral velocity error components of the velocity error ε _v in the UAV track prediction coordinate system.

As shown in equation (9), the crash location of the UAV can be calculated based on this, and its error distribution histogram is shown in Figure 3.

4) Substituting the boundary conditions of the unmanned aerial vehicle determined in step 2) in the out-of-control or out-of-power failure state into the step 1) established UAV out-of-control or out-of-power failure state in the landing track prediction model to calculate the UAV Falling speed and falling kinetic energy;

The landing speed of the drone is calculated according to formula (10):

5) According to the kinetic energy of UAV falling in step 4), combined with the protection coefficient of ground shelter and the energy threshold of casualties, calculate the casualty rate on the ground according to equation (12), and use this as a quantitative indicator to evaluate the drone The risk at the crash site of the drone obtained in step 3):

In the formula: P _f is the casualty rate of ground personnel; P _s is the protection coefficient of ground shelters, and the value is 1; α is the impact energy required for the casualty rate of 50% _{when P s =6, and the value is 100kJ} ; Β is _{the energy threshold for casualties when P s} tends to 0, and the value is 34J; E is the kinetic energy of the UAV falling to the ground when the UAV is out of control or power failure; k is the correction factor.

Claims

The risk assessment method of a drone falling to the ground and hurting people in a failure state of losing control or loss of power includes the following steps in order:

1) Establish a prediction model for the landing track when the UAV is out of control or power failure;

2) Set the boundary conditions when the UAV is out of control or loses power failure state;

3) Substitute the boundary conditions of the UAV in the out-of-control or power-off failure state determined in step 2) into step 1) to calculate the UAV's trajectory prediction model established in the out-of-control or power-off failure state of the UAV. The location coordinates of the crash site, and analyze the error distribution at the crash site;

4) Substituting the boundary conditions of the unmanned aerial vehicle determined in step 2) in the out-of-control or out-of-power failure state into the step 1) established UAV out-of-control or out-of-power failure state in the landing track prediction model to calculate the UAV Falling speed and falling kinetic energy; and

5) According to the kinetic energy of the UAV in step 4), combined with the protection coefficient of the ground shelter and the energy threshold of casualties, calculate the casualty rate of ground personnel, and use this as a quantitative indicator to evaluate the drone in step 3) Obtain the risk at the location where the drone fell.
The method for assessing the risk of falling to the ground and hurting people in a failure state of a UAV out of control or loss of power according to claim 1, wherein: in step 1), the prediction model of the landing track of the UAV in the state of out of control or power out of power failure for:

Where: m is the mass of the drone, in kg; g is the acceleration of gravity, in m/s 2 ; x, y, and h are the displacements in the longitudinal, lateral and height directions, in m; t is none The falling time of the man-machine, the unit is s; D x , D y , and D h are the air resistance in the longitudinal, lateral and height directions, and the unit is N;

Expanding the air resistance with the windward area of the drone and the air resistance coefficient, equation (1) can be expressed as equation (2):

Where: c D is the air resistance coefficient; ρ A is the density of air is kg / m 3; A x, A y, A h is the frontal area of each longitudinal, lateral and height directions, in units of m 2.
The method for assessing the risk of falling to the ground and hurting people in a failure state of a UAV out of control or loss of power according to claim 1, wherein: in step 2), the boundary conditions of the UAV out of control or out of power failure state are as follows ( 3) Shown:

Where: x, y, h, v are the initial longitudinal position, lateral position, height position and initial velocity of the failure respectively; x 0 , y 0 , h 0 , v 0 are the longitudinal coordinate and lateral direction of the initial failure prediction point, respectively Coordinates, height coordinates and the predicted value of the initial failure speed; ε x , ε y , ε h , and ε v are the longitudinal coordinate error, the lateral coordinate error, the height coordinate error and the velocity error, respectively.
The method for assessing the risk of injury by falling to the ground in a failure state of a UAV out of control or loss of power according to claim 1, wherein: in step 3), said step 2) determines that the UAV is out of control or in a failure state of power loss. Substitute the boundary conditions of step 1) into the prediction model of the flight path of the UAV under the condition that the UAV is out of control or power failure. The method to calculate the location coordinates of the UAV crash is:

Use the coordinates of the initial prediction point of failure (x 0 , y 0 , h 0 ) as the origin to establish the UAV trajectory prediction coordinate system, then step 2) The unmanned aerial vehicle described in formula (3) is out of control or loses power. The boundary conditions can be expressed as formula (4):

Incorporating the integral of formula (2) into formula (4), the formula for calculating the crash location of the drone as shown in formulas (5)—(7) is obtained:

Where v 0x and v 0y are respectively the predicted value of the initial failure speed. v 0 is the longitudinal and lateral velocity components in the UAV track prediction coordinate system; ε vx and ε vy are the velocity errors ε v in the unmanned The longitudinal and lateral velocity error components in the aircraft trajectory prediction coordinate system;

From equation (7), it can be calculated that when the drone's operating altitude is h 0 , its landing time is:

Substituting formula (8) into formula (5) and formula (6), the running distance of the UAV in the longitudinal and lateral directions is calculated as:
The method for assessing the risk of falling to the ground and hurting people in a failure state of an unmanned aerial vehicle out of control or loss of power according to claim 1, wherein: in step 4), the falling speed of the unmanned aerial vehicle is calculated according to formula (10):

The kinetic energy of UAV falling to the ground is calculated according to formula (11):

In the formula, E is the kinetic energy of the drone falling to the ground, and the unit is J.
The method for assessing the risk of injury by falling to the ground in a failure state of a UAV out of control or loss of power according to claim 1, wherein: in step 5), the calculation formula for the casualty rate of ground personnel is:

In the formula: P f is the casualty rate of ground personnel; P s is the protection coefficient of ground shelters, and the value is 1; α is the impact energy required for the casualty rate of 50% when P s =6, and the value is 100kJ ; Β is the energy threshold for casualties when P s tends to 0, and the value is 34J; E is the kinetic energy of the UAV falling to the ground when the UAV is out of control or power failure; k is the correction factor.