KR102300324B1 - System and method for controlling formation flight based on anti-collision algorithm - Google Patents

Abstract

The present invention relates to a system for controlling a cluster flight of a drone and a method therefor, wherein the system for controlling the cluster flight of the drone according to one embodiment comprises: a distance measuring part that measures a distance between the drone and at least one obstacle adjacent to the drone; an obstacle position determination part that determines a position of the obstacle based on a measured distance and a preset collision detection area; and a flight control part that controls, in accordance with a result of determining the position of the obstacle, the flight of the drone based on at least one among a potential function for collision prevention and a cluster flight control algorithm. Therefore, the present invention is capable of allowing obstacles to be avoided.

Description

SYSTEM AND METHOD FOR CONTROLLING FORMATION FLIGHT BASED ON ANTI-COLLISION ALGORITHM

The present invention relates to a swarm flight control system and method, and more particularly, to a technical idea of avoiding an obstacle while maintaining a swarm shape when an obstacle is adjacent to a drone in swarm flight.

An unmanned aerial vehicle is an unmanned aerial vehicle whose flight is controlled through remote control or automatic control based on wireless communication without a pilot on board, and is commonly called a drone. is increasing

Specifically, drones started for military use, but recently, they have been widely used for various purposes such as high-altitude photography and product delivery, pesticide spraying, air quality measurement, forest fire monitoring and firefighting, communication, disaster environment response, R&D, etc. It has been mass-produced as a product and has entered an era where individuals can purchase it without any burden.

In this situation, recently, research on platoon flight in which a plurality of drones form a formation to perform special and complex missions including disaster relief, reconnaissance, and performances, rather than simply flying a single drone, is being actively conducted. is becoming

However, in the existing platoon flight process, there is a risk of colliding with each other because a plurality of drones do not recognize the position between the drones during the platoon formation process, and there is also a risk of colliding with an obstacle in the platoon flight process.

In addition, in the process of platoon flight, if only one drone among a plurality of drones forming a cluster recognizes an obstacle and performs an evasive maneuver, a problem may occur in which the overall shape of the cluster is broken.

Korean Patent Registration No. 10-2086701, "Method for creating a swarm flight scenario and an apparatus for performing the same" Korean Patent Registration No. 10-2096377, "Method for determining flight path for group flight of multiple flying vehicles"

An object of the present invention is to provide a swarm flight control system and method for a drone capable of avoiding obstacles while maintaining a swarm shape using a potential function for collision avoidance and a swarm flight control algorithm.

Another object of the present invention is to provide a drone swarm flight control system and method capable of more efficiently avoiding obstacles in consideration of a preset danger area and a caution area.

A drone swarm flight control system according to an embodiment of the present invention includes a distance measuring unit that measures a distance between a drone and at least one obstacle adjacent to the drone, and the position of the obstacle based on the measured distance and a preset collision detection area It may include an obstacle position determination unit for determining the position of the obstacle, and a flight control unit for controlling the flight of the drone based on at least one of a potential function for preventing collision and a swarm flight control algorithm according to the result of determining the position of the obstacle.

According to one side, the distance measuring unit may measure the distance between the obstacles using at least one of a LIDAR sensor and a laser sensor.

According to one side, the collision detection area may include a risky zone corresponding to a first radius preset using the drone as a reference point, and a cautionary zone corresponding to a second radius longer than the first radius. .

According to one side, the potential function is formed through a linear combination of a repelling force function of the drone based on the first radius and the measured distance and a time-varying step function based on the first radius, the second radius and the measured distance. It can be a function that becomes

According to one side, the swarm flight control algorithm is an algorithm formed through an operation based on the Laplacian matrix calculated from the location information tracking error and speed information tracking error of the drone calculated based on graph theory, and the location information of the drone cluster including the drone. can be

According to one side, the flight control unit differentiates the potential function, and when it is determined that the obstacle is located in the danger area, controls the flight of the drone based on the differentiated potential function, and when it is determined that the obstacle is located in the attention area, the differential potential function and It is possible to control the flight of the drone based on the swarm flight control algorithm.

A method for controlling swarm flight of drones according to an embodiment of the present invention includes measuring a distance between a drone and at least one obstacle adjacent to the drone by a distance measuring unit, and detecting a predetermined collision with the distance measured by the obstacle position determining unit Determining the position of the obstacle based on the area and controlling the flight of the drone based on at least one of a potential function for collision avoidance and a swarm flight control algorithm according to the result of determining the position of the obstacle by the flight controller. can

According to one side, the measuring of the distance may include measuring the distance between the obstacles using at least one of a LIDAR sensor and a laser sensor.

According to one side, the collision detection area may include a risky zone corresponding to a first radius preset using the drone as a reference point, and a cautionary zone corresponding to a second radius longer than the first radius. .

According to one side, the step of controlling the flight of the drone differentiates the potential function, and when it is determined that the obstacle is located in the danger area, controls the flight of the drone based on the differentiated potential function, and when it is determined that the obstacle is located in the area of interest The flight of the drone can be controlled based on the differentiated potential function and the swarm flight control algorithm.

According to one embodiment, the present invention can avoid obstacles while maintaining the swarm shape by using a potential function for collision avoidance and a swarm flight control algorithm.

According to an embodiment, the present invention can avoid obstacles more efficiently in consideration of a preset danger area and a caution area.

1 is a view for explaining a swarm flight control system of a drone according to an embodiment.
FIG. 2 is a view for explaining an example of controlling a swarm flight of a drone in a swarm flight control system of a drone according to an embodiment.
3 is a view for explaining an example of forming a collision detection area in the swarm flight control system of a drone according to an embodiment.
4 is a diagram for explaining an example of measuring a distance between a drone and an obstacle using a four-way laser sensor in the swarm flight control system of the drone according to an embodiment.
5 is a diagram for explaining an example of avoiding an obstacle in a swarm flight control system of a drone according to an embodiment.
6 is a view for explaining a simulation result of a swarm flight control system of a drone according to an embodiment.
7 is a view for explaining a method for controlling group flight of drones according to an embodiment.

Specific structural or functional descriptions of the embodiments according to the concept of the present invention disclosed herein are only exemplified for the purpose of explaining the embodiments according to the concept of the present invention, and the embodiment according to the concept of the present invention These may be embodied in various forms and are not limited to the embodiments described herein.

Since the embodiments according to the concept of the present invention may have various changes and may have various forms, the embodiments will be illustrated in the drawings and described in detail herein. However, this is not intended to limit the embodiments according to the concept of the present invention to specific disclosed forms, and includes changes, equivalents, or substitutes included in the spirit and scope of the present invention.

Terms such as first or second may be used to describe various elements, but the elements should not be limited by the terms. The above terms are used only for the purpose of distinguishing one element from another element, for example, without departing from the scope of rights according to the concept of the present invention, a first element may be named as a second element, Similarly, the second component may also be referred to as the first component.

When a component is referred to as being “connected” or “connected” to another component, it is understood that the other component may be directly connected or connected to the other component, but other components may exist in between. it should be On the other hand, when it is said that a certain element is "directly connected" or "directly connected" to another element, it should be understood that no other element is present in the middle. Expressions describing the relationship between elements, for example, “between” and “between” or “directly adjacent to”, etc. should be interpreted similarly.

The terminology used herein is used only to describe specific embodiments, and is not intended to limit the present invention. The singular expression includes the plural expression unless the context clearly dictates otherwise. In this specification, terms such as "comprise" or "have" are intended to designate that the described feature, number, step, operation, component, part, or combination thereof exists, and includes one or more other features or numbers, It should be understood that the possibility of the presence or addition of steps, operations, components, parts or combinations thereof is not precluded in advance.

Unless defined otherwise, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Terms such as those defined in a commonly used dictionary should be interpreted as having a meaning consistent with the meaning in the context of the related art, and should not be interpreted in an ideal or excessively formal meaning unless explicitly defined in the present specification. does not

Hereinafter, embodiments will be described in detail with reference to the accompanying drawings. However, the scope of the patent application is not limited or limited by these examples. Like reference numerals in each figure indicate like elements.

1 is a view for explaining a swarm flight control system of a drone according to an embodiment.

Referring to FIG. 1 , the swarm flight control system 100 according to an embodiment may avoid obstacles while maintaining the swarm shape by using a potential function for collision avoidance and a swarm flight control algorithm.

In addition, the swarm flight control system 100 may avoid obstacles more efficiently in consideration of a preset danger area and a caution area.

To this end, the swarm flight control system 100 may include a distance measurement unit 110 , an obstacle position determination unit 120 , and a flight control unit 130 .

The swarm flight control system 100 to be described below may be applied to at least one of a plurality of drones forming a cluster (ie, a drone cluster) and a ground station controlling the platoon flight of the plurality of drones.

For example, the distance measuring unit 110 , the obstacle position determining unit 120 , and the flight control unit 130 may be applied to each of a plurality of drones. In addition, the distance measuring unit 110 and the obstacle position determining unit 120 may be applied to each of a plurality of drones, and the flight control unit 130 may be applied to a ground station.

)를 측정할 수 있다.The distance measuring unit 110 according to an embodiment is a distance between the drone and at least one obstacle adjacent to the drone (

) can be measured.

As will be described below, a drone may refer to at least one drone among a plurality of drones performing group flight, and an obstacle may refer to other drones in flight as well as general objects or building structures. For example, each of the plurality of drones may be a quadcopter.

)를 측정할 수 있다. 바람직하게는, 거리 측정부(110)는 4방향 레이저 센서를 이용하여 거리(

)를 측정할 수 있다.According to one side, the distance measuring unit 110 is a distance between at least one obstacle using a lidar (LIDAR) sensor and a laser sensor (

) can be measured. Preferably, the distance measuring unit 110 uses a 4-way laser sensor to measure the distance (

) can be measured.

)를 측정할 수 있다. For example, the distance measuring unit 110 detects an obstacle within a detection area corresponding to a preset detection radius centered on the drone through the lidar sensor, the distance between the drone and the obstacle (

) can be measured.

In addition, the distance measuring unit 110 may further include an optical flow sensor to estimate the distance from the take-off point of the drone to the current location.

The obstacle position determining unit 120 according to an embodiment may determine the position of the obstacle based on the distance measured by the distance measuring unit 110 and a preset collision detection area.

)에 대응되는 위험 영역(risky zone)과, 제1 반경(

) 보다 긴 제2 반경(

)(즉,

>

)에 대응되는 주의 영역(cautionary zone)을 포함할 수 있다. 예를 들면 제2 반경(

)은 감지 반경보다 짧게 설정될 수 있다.According to one side, the collision detection area has a predetermined first radius (

) corresponding to the risky zone, and the first radius (

) greater second radius (

)(In other words,

>

) may include a cautionary zone corresponding to the . For example, the second radius (

) can be set shorter than the detection radius.

The flight controller 130 according to an embodiment may control the flight of the drone based on at least one of a potential function for preventing collision and a swarm flight control algorithm according to a result of determining the position of the obstacle.

According to one side, when it is determined that the obstacle is located outside the attention area, the flight control unit 130 performs only control according to the swarm flight of the drone, and when it is determined that the obstacle is located in the attention area, the potential function is automatically activated to swarm flight. The drone is controlled by balancing the repulsion/attraction force from the and potential function, and if it is determined that the obstacle is located in the dangerous area, the drone can be moved through the repulsion/attraction force so that the obstacle is located outside the attention area.

)를 미분하고, 장애물이 위험 영역에 위치한 것으로 판단되면 미분된 포텐셜 함수(

)에 기초하여 드론의 비행을 제어할 수 있다.Specifically, the flight control unit 130 is a potential function (

), and if it is determined that the obstacle is located in the danger area, the differentiated potential function (

) based on the drone's flight control.

)와 군집 비행 제어 알고리즘에 기초하여 드론의 비행을 제어할 수 있다.In addition, when it is determined that the obstacle is located in the area of interest, the flight control unit 130 may perform a differential potential function (

) and the swarm flight control algorithm, it is possible to control the flight of the drone.

)는 하기 수학식1과 같이 제1 반경(

) 및 측정된 거리(

)에 기초하는 드론의 반발력 함수(

)와, 제1 반경(

), 제2 반경(

) 및 측정된 거리(

)에 기초하는 시변 스텝 함수(step fuction)(

)의 선형적인 결합을 통해 형성되는 함수일 수 있다. According to one side, the potential function (

) is the first radius (

) and the measured distance (

) based on the drone's repulsion function (

) and the first radius (

), the second radius (

) and the measured distance (

) based on a time-varying step function (

) may be a function formed through a linear combination of

[Equation 1]

,

, 제1 반경(

) 및 제2 반경(

)은

조건을 만족하는 양의 상수일 수 있다. here,

,

, the first radius (

) and the second radius (

)silver

It may be a positive constant that satisfies the condition.

와 반발력 함수

각각은 하기 수학식2 및 수학식3과 같이 정의될 수 있다. Also, the time-varying step function

and the repulsion function

Each may be defined as in Equation 2 and Equation 3 below.

[Equation 2]

[Equation 3]

Here, n may be set to a constant of 2 or more.

)는 상술한 정의를 통해 도출된 반발력 함수(

)와, 어트랙티브 파트(attractive part)를 나타내는 시변 스텝 함수(

)의 선형적인 결합으로 구성되며, 어트랙티브 파트를 상수

및

로 독립적으로 조정할 수 있다.That is, the potential function (

) is the repulsive force function (

) and a time-varying step function representing the attractive part (

), which consists of a linear combination of

and

can be adjusted independently.

)와 속도 정보 추적 오류(

) 및 드론 군집의 위치정보로부터 산출되는 라플라시안 행렬(

)에 기초한 연산을 통해 형성되는 알고리즘일 수 있다.According to one side, the swarm flight control algorithm is based on the graph theory, and the tracking error (

) and speed information tracking error (

) and Laplacian matrix (

) may be an algorithm formed through an operation based on

)와 군집 비행 제어 알고리즘을 이용하여 주의 영역 및 위험 영역 각각에서 드론의 비행을 제어하는 방법을 보다 구체적으로 설명하기로 한다. Hereinafter, the potential function differentiated in the flight control unit 130 according to an embodiment (

) and a swarm flight control algorithm will be described in more detail to control the flight of the drone in each of the attention and danger areas.

Combining Equations 1 to 3 described above, a differentiated potential function can be defined as Equation 4 below.

[Equation 4]

)를 측정할 수 있으며, 이 경우 4방향 레이저 센서를 통해 측정된 거리 정보는 드론의 전방 및 후방에 대응되는 거리 정보(

,

)와 드론의 좌측과 우측 방향에 대응되는 거리 정보(

,

)를 포함할 수 있다. On the other hand, the swarm flight control system 100 is a distance (

) can be measured, and in this case, the distance information measured through the 4-way laser sensor is the distance information (

,

) and distance information (

,

) may be included.

That is, when the swarm flight control system 100 uses a four-way laser sensor, the differentiated potential function described through Equation 4 may be redefined as Equation 5 below.

[Equation 5]

According to one side, the swarm flight controlled through the swarm flight control system 100 may be performed through algebraic graph theory.

를 기반으로 하여 드론 군집에 대한 군집 비행을 제어할 수 있으며, 여기서

는 드론 군집을 구성하는 N개의 드론의 위치 정보,

는 N개의 드론간의 연결선 정보로 이는 i번째 드론과 j번째 드론이 동시에 상태정보를 송수신할 수 있음을 의미한다. 또한,

는 i번째 드론과 j번째 드론에 대한 가중 인접 행렬을 의미한다. In other words, the swarm flight control system 100 is in graph theory.

It is possible to control swarm flight for a drone swarm based on

is the location information of N drones constituting the drone cluster,

is connection line information between N drones, which means that the i-th drone and the j-th drone can transmit and receive status information at the same time. In addition,

is the weighted adjacency matrix for the i-th drone and the j-th drone.

For example, the flight controller 130 may form a drone cluster using four drones, but may control the four drones forming the drone cluster to form a rectangular cluster shape.

) 및 중심 위치(

)와 드론 군집을 형성하는 복수의 드론(i개의 드론) 각각에 대응되는 복수의 군집 형성 벡터(

)를 산출할 수 있으며, 여기서

는 0 보다 큰 실수의 집합을 의미하고,

는 3차원 실수의 집합을 의미할 수 있다.According to one side, the flight controller 130 uses a graph theory to control a central position (

) and the center position (

) and a plurality of clustering vectors (

) can be calculated, where

means the set of real numbers greater than 0,

may mean a set of 3D real numbers.

)와 군집 형성 벡터(

)를 이용하여 시간 t에서 드론 군집을 형성하는 i번째 드론의 위치 정보(

)와 i번째 드론의 속도 정보(

)를 산출할 수 있으며, 산출된 i번째 드론의 위치 정보(

)와 i번째 드론의 속도 정보(

)를 이용하여 하기 수학식6과 같이 i번째 드론의 위치 정보 추적 오류(

)와 i번째 드론의 속도 정보 추적 오류(

)를 산출할 수 있다(여기서,

). According to one side, the flight control unit 130 is the calculated center position (

) and the clustering vector (

) using the location information of the i-th drone forming a drone cluster at time t (

) and the speed information of the i-th drone (

) can be calculated, and the calculated location information (

) and the speed information of the i-th drone (

), as in Equation 6 below, the location information tracking error of the i-th drone (

) and the speed information tracking error of the i-th drone (

) can be calculated (where

).

[Equation 6]

)와 산출된 위치 정보 추적 오류(

) 및 속도 정보 추적 오류(

)를 이용하여 군집 비행 제어 알고리즘(

)을 산출할 수 있다. 여기서

및

는 드론 군집을 형성하는 드론간 위치와 속도의 균형을 맞추기 위한 계수이고,

는 하기 수학식7과 같이 정의될 수 있다. In addition, the flight control unit 130 calculates the speed information (

) and the calculated location information tracking error (

) and speed information tracking error (

) using the swarm flight control algorithm (

) can be calculated. here

and

is a coefficient for balancing the position and speed between drones forming a drone cluster,

can be defined as in Equation 7 below.

[Equation 7]

)와 산출된 군집 비행 제어 알고리즘(

)을 결합하여 드론의 비행을 제어하기 위한 제어 법칙(

)을 산출할 수 있다. In addition, the flight control unit 130, as shown in Equation 8, the differential potential function (

) and the calculated swarm flight control algorithm (

) to control the flight of the drone (

) can be calculated.

[Equation 8]

)에서 미분된 포텐셜 함수(

)만을 적용하여 드론의 비행을 제어하고, 장애물이 주의 영역 내에 위치한 것으로 판단되면 수학식8의 제어 법칙(

) 전체를 적용하여 드론의 비행을 제어하며, 장애물이 감지되지 않거나 주의 영역 외부에 위치한 것으로 판단되면 수학식8의 제어 법칙(

)에서

만을 적용하여 드론의 비행을 제어할 수 있으며, 이를 통해 추적 오류를 '0'으로 수렴하게 할 수 있다. That is, the flight controller 130 controls the flight of the drone using Equation (8), but when it is determined that the obstacle is located in the danger area, the control rule of Equation (8) (

) from the differential potential function (

) is applied to control the flight of the drone, and if it is determined that the obstacle is located within the area of interest, the control rule (

) to control the flight of the drone by applying the entire

)at

It is possible to control the flight of the drone by applying only

On the other hand, the flight controller 130 may calculate the tracking error represented by the sum of the set value for the target position and the set value for collision avoidance as shown in Equation 9 using Equation 6 below.

[Equation 9]

는 드론 군집 형성을 위한 기준 위치 벡터를 의미하고,

는 드론 군집 형성을 위한 기준 속도 벡터를 의미할 수 있다. here,

is a reference position vector for forming a drone cluster,

may mean a reference velocity vector for forming a drone cluster.

값이 고정되면 r(t)의 위치 변화에 따라 대응되는 드론의 위치도 변화할 수 있다. For example, when the first to fourth drones form a cluster, if the position of the first drone is (1, 1), when r(t) is (0, 0), f _p,1 (t) is ( 1, 1) and when r(t) is (1, 1), f _p,1 (t) can be (0, 0). In addition,

If the value is fixed, the position of the corresponding drone may also change according to the change in the position of r(t).

)로 표현할 수 있다. In addition, the flight control unit 130 provides the location information of the drone cluster, that is, information on whether or not the location of different drones in each of the plurality of drones constituting the drone cluster, is provided to the Laplacian matrix (

) can be expressed as

[Equation 10]

)은 드론의 군집 형상이 사각형 형상일 때를 예시하는 것으로, 여기서 대각행렬의 '2' 부분은 연결되어 있는 드론의 개수를 나타내고, 그 외의 부분은 어떤 드론과 연결되어 있는지를 나타낼 수 있다. Laplacian matrix in Equation 10 (

) exemplifies when the drone cluster shape is a rectangular shape, where the '2' part of the diagonal matrix indicates the number of connected drones, and the other parts may indicate which drones are connected.

Specifically, in Equation 10, all drones forming a cluster are connected to two aircraft, and since items (1, 2) and (1, 4) indicate '-1', aircraft 1 is number 2, You can see that it is connected to the 4th gas.

)을 산출할 수 있다. Through this, the flight controller 130 can calculate the control rule as shown in Equation 11 below, and using the control rule of Equation 8, the control rule of Equation 11, and the tracking error of Equation 9, Equation 12 below Control laws like (

) can be calculated.

[Equation 11]

는 N번째 드론의 위치 정보,

는 N번째 드론의 속도 정보,

는 드론 군집에 대응되는 3차 단위 행렬(three-order identity matrix)을 나타낸다. Here, the Nth drone among the plurality of drones constituting the drone cluster,

is the location information of the Nth drone,

is the speed information of the Nth drone,

denotes a three-order identity matrix corresponding to the drone cluster.

[Equation 12]

는 라플라시안 행렬의 항을 나타낸다. here,

denotes a term of the Laplacian matrix.

)에서 미분된 포텐셜 함수(

)만을 적용하여 드론의 비행을 제어하고, 장애물이 주의 영역 내에 위치한 것으로 판단되면 수학식12의 제어 법칙(

) 전체를 적용하여 드론의 비행을 제어하며, 장애물이 감지되지 않거나 주의 영역 외부에 위치한 것으로 판단되면 수학식12의 제어 법칙(

)에서

만을 적용하여 드론의 비행을 제어할 수 있다. That is, the flight control unit 130 controls the flight of the drone using Equation 12, but when it is determined that the obstacle is located in the danger area, the control rule of Equation 8 (

) from the differential potential function (

) is applied to control the flight of the drone, and if it is determined that the obstacle is located within the area of attention, the control law (

) to control the flight of the drone by applying the entire

)at

You can control the flight of the drone by applying only

FIG. 2 is a view for explaining an example of controlling a swarm flight of a drone in a swarm flight control system of a drone according to an embodiment.

Referring to FIG. 2 , reference numeral 200 shows an example of controlling group flight for a plurality of drones Q1 to Q4 based on graph theory.

)와 드론 군집을 형성하는 복수의 드론(Q1 내지 Q4) 각각에 대응되는 군집 형성 벡터(f_p,1(t) 내지 f_p,4(t))를 산출할 수 있다. According to reference numeral 200, the swarm flight control system according to an embodiment uses a graph theory to use a central position (

) And a plurality of drones forming a drone Community (Q1 to Q4), each cluster corresponding to a formation vector (f _{p, 1} (t) to _{p f,} can be calculated a ₄ (t)).

)와 군집 형성 벡터(f_p,1(t) 내지 f_p,4(t))에 기초하여 복수의 드론(Q1 내지 Q4) 각각에 대응되는 위치 정보 추적 오류 및 속도 정보 추적 오류를 산출할 수 있으며, 산출된 위치 정보 추적 오류 및 속도 정보 추적 오류에 기초하여 복수의 드론(Q1 내지 Q4)의 군집 비행을 제어하는 군집 비행 제어 알고리즘을 도출할 수 있다. In addition, the swarm flight control system calculates the center position (

) and the cluster formation vectors f _p,1 (t) to f _p,4 (t)), it is possible to calculate the location information tracking error and the speed information tracking error corresponding to each of the plurality of drones Q1 to Q4. Also, based on the calculated location information tracking error and speed information tracking error, it is possible to derive a swarm flight control algorithm for controlling the swarm flight of the plurality of drones Q1 to Q4.

3 is a view for explaining an example of forming a collision detection area in the swarm flight control system of a drone according to an embodiment.

Referring to FIG. 3 , reference numeral 300 denotes a risky zone and a cautionary zone, which are collision detection zones formed based on drones performing group flight.

)에 대응되는 위험 영역과, 제1 반경(

) 보다 긴 제2 반경(

)(즉,

<

)에 대응되는 주의 영역을 설정할 수 있다. Referring to reference numeral 300, the swarm flight control system according to an embodiment includes a first radius (

Hazard area corresponding to ) and the first radius (

) greater second radius (

)(In other words,

<

) can be set for the corresponding attention area.

In addition, when an obstacle is adjacent to at least one of a plurality of drones in swarm flight, the swarm flight control system may determine the location of the obstacle and control the flight of the drone based on the determination result.

Specifically, when it is determined that the obstacle is located outside the attention area, the swarm flight control system performs only control according to the swarm flight on the drone, and when it is determined that the obstacle is located in the attention area, the potential function is automatically activated to prevent The drone is controlled by balancing the repulsion/attraction force from the function, and if it is determined that the obstacle is located in the dangerous area, the drone can be moved through the repulsion/attraction force so that the obstacle is located outside the attention area.

4 is a diagram for explaining an example of measuring a distance between a drone and an obstacle using a four-way laser sensor in the swarm flight control system of the drone according to an embodiment.

)를 측정하는 예시를 도시한다. Referring to FIG. 4 , reference numeral 400 denotes a distance from a drone (quatcopter) in swarm flight using a 4-way laser sensor to an obstacle (

) is shown as an example of measuring.

,

)와 드론의 좌측과 우측 방향에 대응되는 거리 정보(

,

)를 포함할 수 있다.Referring to reference numeral 400 , the swarm flight control system according to an embodiment may measure distance information to an obstacle using a four-way laser sensor. In this case, the distance information measured through the four-way laser sensor is the front of the drone. and distance information corresponding to the rear (

,

) and distance information (

,

) may be included.

)에 대응되는 위험 영역(risky zone) 및 제2 반경(

)에 대응되는 주의 영역(cautionary zone)에 기초하여 드론의 전방(x축 방면)에 위치한 장애물까지의 거리(

)를 측정할 수 있다. For example, the swarm flight control system may include a four-way laser sensor and a first radius (

) corresponding to the risky zone and the second radius (

) based on the cautionary zone corresponding to the distance (

) can be measured.

5 is a diagram for explaining an example of avoiding an obstacle in a swarm flight control system of a drone according to an embodiment.

Referring to FIG. 5 , reference numeral 500 denotes another drone, that is, an obstacle O, when the other drone, that is, an obstacle O, approaches a plurality of drones Q1 to Q4 whose swarm flight is controlled through the swarm flight control system according to an embodiment. An example of avoiding O) is shown.

According to reference numeral 500, the swarm flight control system according to an embodiment prevents collision when an obstacle O is adjacent to the second drone Q2 in the process of controlling the swarm flight of the plurality of drones Q1 to Q4. The flight of the plurality of drones Q1 to Q4 as well as the second drone Q2 can be controlled based on at least one of a potential function and a swarm flight control algorithm for It is possible to easily avoid the obstacle (O) while maintaining the.

On the other hand, the swarm flight control system may control each of the plurality of drones Q1 to Q4 to form a preset swarm shape while avoiding a plurality of obstacles in the vicinity even when forming a first group.

6 is a view for explaining a simulation result of a swarm flight control system of a drone according to an embodiment.

Referring to FIG. 6 , FIGS. 6A to 6I show a plurality of drones (indicated by 'X' in FIG. 6 ) and a plurality of obstacles whose swarm flight is controlled through the swarm flight control system according to an embodiment. The simulation results performed for 56s to 72s using (Ob.1 to Ob.4) are shown.

According to (a) to (i) of Figure 6, it can be confirmed that the third obstacle (Ob.3) approaches the plurality of drones in a time interval of 56s to 62s, and accordingly, a swarm flight control system according to an embodiment By controlling a plurality of drones using a potential function for collision avoidance and a swarm flight control algorithm, a plurality of drones maintain a swarm shape for a time period of 64s to 72s, and separate the distance from the third obstacle (Ob.3) Thus, it can be confirmed that the collision with the third obstacle Ob.3 is avoided.

7 is a view for explaining a method for controlling group flight of drones according to an embodiment.

In other words, FIG. 7 is a view for explaining an operating method of a swarm flight control system of a drone according to an embodiment described with reference to FIGS. 1 to 6 , and among the contents described with reference to FIG. 7 later, through FIGS. 1 to 6 . A description that overlaps with the description will be omitted.

Referring to FIG. 7 , in the swarm flight control method according to an embodiment in step 710 , the distance measuring unit may measure a distance between a drone and at least one obstacle adjacent to the drone.

According to one side, in step 710 , the method for controlling swarm flight according to an embodiment may measure a distance between obstacles using at least one of a LIDAR sensor and a radar sensor.

Next, in step 720 , the method for controlling the swarm flight according to an embodiment may determine the position of the obstacle based on the distance measured by the obstacle position determining unit and a preset collision detection area.

According to one side, the collision detection area may include a risky zone corresponding to a first radius preset using the drone as a reference point, and a cautionary zone corresponding to a second radius longer than the first radius. .

Next, in step 730, the swarm flight control method according to an embodiment controls the flight of the drone based on at least one of a potential function for collision prevention and a swarm flight control algorithm according to the result of determining the position of the obstacle in the flight controller. can

According to one side, in step 730, the swarm flight control method according to the embodiment differentiates the potential function, and when it is determined that the obstacle is located in the danger area, controls the flight of the drone based on the differentiated potential function, and the obstacle is in the attention area If it is determined to be located at , it is possible to control the flight of the drone based on the differential potential function and the swarm flight control algorithm.

After all, using the present invention, it is possible to avoid obstacles while maintaining the swarm shape by using a potential function for collision avoidance and a swarm flight control algorithm.

In addition, it is possible to avoid obstacles more efficiently in consideration of a preset danger area and a caution area.

In addition, the system can be built using relatively inexpensive sensors such as optical flow sensors and laser distance sensors.

The device described above may be implemented as a hardware component, a software component, and/or a combination of the hardware component and the software component. For example, devices and components described in the embodiments may include, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable gate array (FPGA). , a programmable logic unit (PLU), microprocessor, or any other device capable of executing and responding to instructions, may be implemented using one or more general purpose or special purpose computers. The processing device may execute an operating system (OS) and one or more software applications running on the operating system. The processing device may also access, store, manipulate, process, and generate data in response to execution of the software. For convenience of understanding, although one processing device is sometimes described as being used, one of ordinary skill in the art will recognize that the processing device includes a plurality of processing elements and/or a plurality of types of processing elements. It can be seen that can include For example, the processing device may include a plurality of processors or one processor and one controller. Other processing configurations are also possible, such as parallel processors.

As described above, although the embodiments have been described with reference to the limited drawings, various modifications and variations are possible by those skilled in the art from the above description. For example, the described techniques are performed in a different order than the described method, and/or the described components of the system, structure, apparatus, circuit, etc. are combined or combined in a different form than the described method, or other components Or substituted or substituted by equivalents may achieve an appropriate result.

Therefore, other implementations, other embodiments, and equivalents to the claims are also within the scope of the following claims.

100: swarm flight control system 110: distance measuring unit
120: obstacle position determination unit 130: flight control unit

Claims (10)

a distance measuring unit measuring a distance between the drone and at least one obstacle adjacent to the drone;
an obstacle position determination unit for determining the position of the obstacle based on the measured distance and a preset collision detection area; and
A flight controller for controlling the flight of the drone based on at least one of a potential function for collision avoidance and a swarm flight control algorithm according to a result of determining the position of the obstacle
including,
The collision detection area is
It includes a risky zone corresponding to a first radius preset using the drone as a reference point, and a cautionary zone corresponding to a second radius longer than the first radius,
The flight control unit,
Differentiate the potential function, control the flight of the drone based on the differentiated potential function when it is determined that the obstacle is located in the dangerous area, and when it is determined that the obstacle is located in the attention area, the differentiated potential function and controlling the flight of the drone based on the swarm flight control algorithm.
Drone swarm flight control system. According to claim 1,
The distance measuring unit,
measuring the distance between the obstacles using at least one of a LIDAR sensor and a laser sensor
Drone swarm flight control system. delete a distance measuring unit measuring a distance between the drone and at least one obstacle adjacent to the drone;
an obstacle position determination unit for determining the position of the obstacle based on the measured distance and a preset collision detection area; and
A flight controller for controlling the flight of the drone based on at least one of a potential function for collision avoidance and a swarm flight control algorithm according to a result of determining the position of the obstacle
including,
The collision detection area is
It includes a risky zone corresponding to a first radius preset using the drone as a reference point, and a cautionary zone corresponding to a second radius longer than the first radius,
The potential function is
Formed through a linear combination of a repulsion force function of the drone based on the first radius and the measured distance and a time-varying step function based on the first radius, the second radius and the measured distance function
Drone swarm flight control system. a distance measuring unit measuring a distance between the drone and at least one obstacle adjacent to the drone;
an obstacle position determination unit for determining the position of the obstacle based on the measured distance and a preset collision detection area; and
A flight controller for controlling the flight of the drone based on at least one of a potential function for collision avoidance and a swarm flight control algorithm according to a result of determining the position of the obstacle
including,
The collision detection area is
It includes a risky zone corresponding to a first radius preset using the drone as a reference point, and a cautionary zone corresponding to a second radius longer than the first radius,
The swarm flight control algorithm is
An algorithm formed through an operation based on the Laplacian matrix calculated from the location information tracking error and speed information tracking error of the drone calculated based on graph theory and the location information of the drone cluster including the drone
Drone swarm flight control system. delete measuring, by a distance measuring unit, a distance between the drone and at least one obstacle adjacent to the drone;
determining, in the obstacle position determining unit, the position of the obstacle based on the measured distance and a preset collision detection area; and
controlling, in the flight controller, the flight of the drone based on at least one of a potential function for preventing collision and a cluster flight control algorithm according to a result of determining the position of the obstacle
including,
The collision detection area is
It includes a risky zone corresponding to a first radius preset using the drone as a reference point, and a cautionary zone corresponding to a second radius longer than the first radius,
The step of controlling the flight of the drone,
Differentiate the potential function, control the flight of the drone based on the differentiated potential function when it is determined that the obstacle is located in the dangerous area, and when it is determined that the obstacle is located in the attention area, the differentiated potential function and controlling the flight of the drone based on the swarm flight control algorithm.
How to control swarm flight of drones. 8. The method of claim 7,
Measuring the distance comprises:
measuring the distance between the obstacles using at least one of a LIDAR sensor and a laser sensor
How to control swarm flight of drones. delete delete

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