KR102262835B1 - Drone control apparatus and method for efficient military drone operations - Google Patents

Abstract

Provided, in the present invention, are a device and method for controlling a drone capable of calculating the number of drones that can perform a given mission economically and efficiently and operating the drones according to the calculated number to maximize the effectiveness of the drone by considering energy consumption amount, mission performance time, and battery consumption amount in an environment with spatial and temporal constraints such as in a strategy area or in a disaster situation.

Description

DRONE CONTROL APPARATUS AND METHOD FOR EFFICIENT MILITARY DRONE OPERATIONS

The present invention relates to an apparatus and method for controlling a drone, and to an apparatus and method for controlling a drone for an efficient military operation operation.

Among the core technologies of the 4th industrial revolution, the Internet of Things is the most actively industrialized and spotlighted field. In particular, recently, technologies that combine drones and IoT technologies have emerged and are being used for military purposes as well as traffic monitoring, filming, delivery, and lifesaving. Accordingly, various studies using drones are being conducted, but existing studies on drone operation aim to minimize the energy consumption of drones while guaranteeing the quality of service (QoS) of wireless channel users.

However, when using drones for disaster or military operation, the limitations of drones and realistic conditions must be considered together. In other words, not only space and time constraints must be considered, but also limited purchase budget, battery capacity, and time and budget for training manpower for drone operation must be considered together. In particular, in the case of military drones or disaster response drones, it is necessary to strictly adhere to the operational time limit, so it is necessary to consider the optimal drone operation method that can perform the required mission within the time limit.

Korea Registered Patent No. 10-2017390 (Registered on 2019.09.02.)

An object of the present invention is to provide an apparatus and method for controlling a drone so that a mission to be performed using a drone is economically and efficiently performed.

Another object of the present invention is to estimate the number of drones that can optimally perform a mission according to the energy consumption, mission execution time, and battery consumption of the drone in an environment where spatial and temporal constraints exist, such as in the operation area or the occurrence of a disaster situation. , to provide a drone control device and method that can maximize the effectiveness of drone operation.

A drone control apparatus according to an embodiment of the present invention for achieving the above object includes: a mission setting unit for acquiring a mission to be performed using at least one drone; Information collection for collecting information on an environment of a mission area including at least one mission location where the mission is to be performed, and a channel for communicating with the at least one drone and the drone at each of the at least one mission location part; Based on the collected information, the total drone consumption energy, total drone operation time, and total drone battery consumption of the drone for performing the mission at each of the entire mission positions in the mission area are calculated, and the total drone battery consumption required for the mission is calculated from the total drone consumption energy. Determining the number of energy-based drones that is the number of drones, determining the number of time-based drones that are the number of drones required for mission performance from the total drone operating time, and determining the number of drones required for mission performance from the total drone battery consumption a drone optimization unit that determines the number of battery-based drones and obtains an optimal number of drones for performing the mission from the number of energy-based drones, the number of time-based drones, and the number of battery-based drones; and a drone control unit that selects a number of drones corresponding to the optimal number of drones, and communicates and controls the selected drones to perform a specified mission at each of at least one mission location.

The drone optimizer calculates the energy consumption for each mission consumed in each of at least one mission in the mission area, and calculates the total drone energy consumption according to the total number of mission locations in the mission area and the energy consumption for each mission, an energy consumption calculation unit dividing the energy consumed by the drone by the available energy of the drone in a fully charged state collected in advance to determine the number of the energy-based drones; Calculates the time consumed for each mission during one cycle when one drone performs a mission at one mission location, calculates the total time consumed to perform the entire mission from the time consumed by each mission, and calculates the The time-based drone is obtained by comparing the included mission time limit with the total consumption time to obtain a smaller time as the total drone operation time, and dividing the obtained total drone operation time by the pre-collected drone operation time in a fully charged state a task execution time calculation unit to determine the number; Calculate the battery consumption for each mission consumed in each of at least one mission location, calculate the total drone battery consumption according to the total number of mission locations in the mission area and the battery consumption for each mission, and calculate the total drone battery consumption in advance. a battery consumption calculation unit dividing the drone battery capacity in a fully charged state to determine the number of battery-based drones; and a drone number determining unit configured to determine a maximum value among the energy-based number of drones, the time-based number of drones, and the battery-based number of drones as the optimal number of drones.

The energy consumption calculation unit calculates separately and calculates the communication energy consumed by the drone to perform communication when performing a mission and the propulsion energy consumed to move to the mission position of the drone, and sums the calculated propulsion energy and the communication energy. It is possible to calculate the energy consumption for each task.

The energy consumption calculation unit calculates communication power consumption by dividing line-of-sight communication and non-line-of-sight communication into line-of-sight communication and non-line-of-sight communication according to a channel state between the drone and the drone control device disposed in each of at least one mission position, and The transmission rate is calculated by analyzing the average path loss from the communication power consumption, the communication energy is calculated according to the transmission rate, the data transmission time at each mission location, and the size of the data to be transmitted, Calculate the hovering power and the moving power, which is the power required for moving the drone, respectively, multiply the hovering power by the time the drone transmits data at the mission position, and multiply the moving power by the time the drone moves to the adjacent mission position, The propulsion energy can be calculated by summing them.

The task execution time calculator may determine the number of battery-based drones by dividing the total drone battery consumption by the sum of the drone battery capacity and the drone charging time.

A drone control method according to another embodiment of the present invention for achieving the another object includes: acquiring a mission to be performed using at least one drone; collecting information on an environment of a mission area including at least one mission location at which the mission is to be performed, and a channel through which the at least one drone and the drone are placed in each of the at least one mission location to perform communication; Based on the collected information, the total drone consumption energy, the total drone operation time, and the total drone battery consumption of the drone for performing the mission at each of the entire mission locations in the mission area are calculated, and the total drone battery consumption required for the mission is calculated from the total drone consumption energy. Determining the number of energy-based drones that is the number of drones, determining the number of time-based drones that are the number of drones required for mission performance from the total drone operating time, and determining the number of drones required for mission performance from the total drone battery consumption determining the number of battery-based drones, and obtaining the optimal number of drones for performing the mission from the number of energy-based drones, the number of time-based drones, and the number of battery-based drones; and selecting a number of drones corresponding to the optimal number of drones, and communicating and controlling the selected drones to perform a specified mission at each of at least one mission location.

Therefore, the drone control apparatus and method according to an embodiment of the present invention calculates the number of drones that can economically and efficiently perform a given mission in an environment where spatial and temporal constraints exist, such as when an operational area or a disaster situation occurs, and , it is possible to maximize the utility of the drone by operating the drone according to the calculated number.

1 shows a relationship between a drone and a drone control device performing communication.
2 shows a schematic structure of a drone control apparatus according to an embodiment of the present invention.
FIG. 3 is a diagram for explaining a concept in which the drone control apparatus of FIG. 2 operates a drone.
4 shows a drone control method according to an embodiment of the present invention.
5 to 9 show simulation results of the performance of the drone control method according to the present embodiment.

In order to fully understand the present invention, the operational advantages of the present invention, and the objects achieved by the practice of the present invention, reference should be made to the accompanying drawings illustrating preferred embodiments of the present invention and the contents described in the accompanying drawings.

Hereinafter, the present invention will be described in detail by describing preferred embodiments of the present invention with reference to the accompanying drawings. However, the present invention may be embodied in various different forms, and is not limited to the described embodiments. In addition, in order to clearly describe the present invention, parts irrelevant to the description are omitted, and the same reference numerals in the drawings indicate the same members.

Throughout the specification, when a part "includes" a certain component, it means that other components may be further included, rather than excluding other components unless otherwise stated. In addition, terms such as "... unit", "... group", "module", and "block" described in the specification mean a unit that processes at least one function or operation, which is hardware, software, or hardware. and a combination of software.

1 shows a relationship between a drone and a drone control device performing communication, FIG. 2 shows a schematic structure of a drone control device according to an embodiment of the present invention, and FIG. 3 is a drone control device of FIG. It is a drawing for explaining the concept of operation.

As shown in FIG. 1 , in the present embodiment, at least one drone 101 may perform wireless communication with the drone control device 103 to be controlled by the drone control device 103 , and the drone control device 103 may be controlled by the drone control device 103 . ) to move to a designated location or perform a designated action. At this time, the plane on which the drone flies and moves is called a flying plane 102 .

In addition, the drone control device 103 may be a gateway GW of the Internet of Things (IoT) using a drone. The drone control device 103, as a service provider, analyzes the number of drones suitable for performing a given mission, and controls the operation of the at least one drone 101 according to the analyzed number, so that the at least one drone 101 . Allow each to perform its assigned task. In particular, in this embodiment, the drone control device 103 calculates the energy consumption, mission execution time, and battery consumption of the drone, and analyzes the optimal number of required drones based on the calculation result, thereby maximizing the effectiveness of drone operation. let it be

Referring to FIG. 2 , the drone control device 103 according to the present embodiment includes a communication unit 210 , a mission setting unit 220 , an information collection unit 230 , a drone optimization unit 240 , and a drone control unit 250 . may include

The communication unit 210 performs wireless communication with at least one drone in a predetermined manner. The drone control device 103 may perform RF communication with at least one drone 101 using general radio waves, but in some cases, free-space optics, which is wireless optical communication performed using light in the air. : Hereinafter, FSO) communication may be performed, and RF communication and FSO communication may be used together.

In addition, the communication unit 210 may communicate with an external system (not shown) to control at least one drone 101 to receive an authorization for a mission to be performed, or to transmit a result of the drone 101 performing the mission. Here, the external system may be a command and control system corresponding to the purpose of using the drone 101 . For example, when the drone 101 is used for military purposes, it may be an operational control system, and when the drone 101 is used for disaster response, it may be a fire control system.

The mission setting unit 220 stores a mission to be performed using at least one drone 101 . The task setting unit 220 may receive and store a task authorized from an external system through the communication unit 210 , or may receive and store a task authorized by a user command or the like. In this case, the stored mission includes at least one mission location indicating a location where the mission should be performed and a mission time limit (L _max ) at which the mission should be performed.

In addition, the mission setting unit 220 may receive and store the entire mission to be performed using at least one drone, and divide the stored mission into detailed missions. As shown in FIG. 3 , the mission setting unit 220 divides the entire designated mission area when the entire mission area in which the mission is to be performed is designated as shown in (a), and a plurality of mission areas as shown in (b). Hovering location can be specified.

Here, as an example, it is assumed that the mission setting unit 220 divides the entire mission area into a grid structure to designate N mission locations, but the method and number of mission locations may be variously modified. In addition, the entire mission area may not be authorized, and a plurality of mission locations may be directly designated and the mission may be authorized.

In this embodiment, it is assumed that the mission to be performed by the drone 101 is, for example, collecting information at the mission point, and thus the drone 101 performs hovering at the mission point to collect information. There is a need. Therefore, it is assumed that the drone 101 is a rotary-wing type drone capable of stationary flight.

The information collection unit 230 collects and stores environmental information, channel information, and drone information of each mission location. The information collection unit 230 may acquire environmental information such as an environment-related constant and an air density ρ of each mission location. In addition, the information collection unit 230 determines whether the drone 101 should perform Line of Sight (LoS) communication with the drone control device 103 when the drone 101 is located at each mission position. It is possible to collect channel information for each mission location by determining whether NLoS) communication needs to be performed. Here, as shown in FIG. 1 , the information collection unit 230 may collect channel information by considering the altitude H of the drone 101 at each mission position. In addition, the information collecting unit 230 as drone information, the weight of the drone itself (m _v ), the weight of the payload (m _p ), the propeller radius of the drone ( r _p ), the number of propellers (n _p ), the motor and Propeller efficiency (ε) and lift-to-drag ratio (r), etc. can be collected.

The information collection unit 230 may receive and store environmental information, channel information, and drone information from various external information providing systems through the communication unit 210 . For example, in the case of environmental information and channel information, authorization may be obtained from a server of the Korea Meteorological Administration, and in the case of drone information, authorization may be obtained from a drone manufacturer's server, or information on operating drones may be stored in advance.

The drone optimization unit 240 determines the optimal number of drones for performing a mission based on the information collected by the information collection unit 230 . The drone optimization unit 240 may include an energy consumption calculation unit 241 , a mission execution time calculation unit 242 , a battery consumption calculation unit 243 , and a number of drones determination unit 244 .

The energy consumption calculation unit 241 calculates the total energy consumption required to complete the mission, and calculates the minimum number of drones required to complete the mission according to the energy consumption as the number of energy-based drones. And the mission execution time calculation unit 242 calculates the total time required to complete the mission, and calculates _{the minimum number of drones such that the calculated total time is within the mission limit time (L max} ) as the number of time-based drones do. Meanwhile, the battery consumption calculation unit 243 calculates the minimum number of drones required to perform a mission as the number of battery-based drones according to the battery capacity of each drone and the battery consumption required for performing the mission.

The number of drones determining unit 244 includes the number of energy-based drones and the number of time-based drones and battery-based drones calculated by each of the energy consumption calculation unit 241 , the mission execution time calculation unit 242 , and the battery consumption calculation unit 243 . is authorized, and the largest number of drones is determined as the optimal number of drones for performing the mission.

First, the energy consumption calculation unit 241 moves to one mission position among all N mission positions _{and calculates the energy consumption (E one} ) for each mission consumed during one cycle of performing the mission to perform the entire mission. The energy consumption is calculated, and the energy consumption for each mission consumed during one cycle (E _one ) is the propulsion energy (E _{prl ) for the flight of the drone 101 and the communication energy (E com} ) for communication with the drone control device 103 . ) to calculate Propulsion energy (E _prl ) and communication energy (E _com ) are the most important energy for stably operating the drone even in the event of various emergency situations such as mission interruption. Although additional energy consumption may be generated in addition to movement and communication of the drone 101 when performing a mission of the drone, except for a special mission, the amount of additional energy consumption is relatively insignificant and is not considered here.

In addition, in calculating the communication energy E _com , the energy consumption calculation unit 241 calculates communication power consumption according to Equations 1 and 2 by first classifying line-of-sight communication and non-line-of-sight communication situations in consideration of channel information.

Here, α and β are environmental linkage constants (eg, urban environment, rural environment), and θ is an elevation angle between the drone control device 103 and the drone 101, as shown in FIG. 1 .

Meanwhile, the average path loss is calculated by Equation (3).

Here, PL _LoS and PL _NLoS may be expressed by Equations 4 and 5 as a path loss model in a line-of-sight environment and a non-line-of-sight environment, respectively.

Here, f _c is a carrier frequency, d is the distance between the drone 101 and the drone control device 103, and c is the speed of light. And ξ _LoS and ξ _NLoS are environmental linkage constants.

_{And the transmission rate (R i} ) at the i-th mission position among all N mission positions is expressed by Equation (6).

로 계산될 수 있으며, N₀는 잡음 전력 스펙트럼 밀도(noise power spectrum density)를 나타낸다.Here, W represents bandwidth, p _i is transmission power, and G _i is channel gain.

can be calculated as , N ₀ represents a noise power spectrum density.

)과 전송되는 데이터 크기(D_i)를 고려하면, N개의 전체 임무 위치에서 소모되는 통신 에너지(E_com)는 수학식 7에 따라 계산될 수 있다.Data transfer time at the i-th mission position (

) and the transmitted data size (D _i ), the communication energy (E _com ) consumed in all N mission positions can be calculated according to Equation (7).

Here, η is a transmission coefficient, and p _static means power consumed when data is not transmitted.

On the other hand, the energy consumption calculation unit 241 _{first calculates the propulsion energy (E prl} ) for the flight of the drone 101 , the hovering power (P _hov ) and the drone ( 101), the power required for the moving flight, the moving power (P _trs ) is calculated according to Equations 8 and 9, respectively.

where m is the weight of the drone and represents the sum (m = m _v + m _{p ) of the weight of the drone itself (m v} ) and the weight of the payload (m _p ), g is the acceleration of gravity, and r _p is the radius of the drone’s propeller ( r _p ), and n _p represents the number of propellers of the drone. And ρ represents the air density.

Here, ε represents the efficiency of the motor and propeller, and r represents the lift ratio.

As shown in (c) of FIG. 3 , when the drone 101 performs a mission at a specific i-th mission position and then moves to an adjacent i+1-th mission position, from Equations 8 and 9, The propulsion energy (E _prl ) may be calculated by Equation (10).

는 드론(101)이 i번째 임무 위치에서 i+1번째 임무 위치로 이동하는데 소요되는 시간을 나타내고, l_i,i+1 은 i번째 임무 위치와 i+1번째 임무 위치 사이의 거리를 나타내고, 전송률(R_i)은 수학식 6에서 이미 계산되었다. 에너지 소비량 계산부(241)는 수학식 7에서 계산된 통신 에너지(E_com)와 수학식 10에서 계산된 추진 에너지(E_prl)의 합으로 전체 임무를 수행하기 위한 전체 에너지(E_tot)를 수학식 11에 따라 계산할 수 있다.here

denotes the time it takes for the drone 101 to move from the i-th mission position to the i+1-th mission position, l _i,i+1 denotes the distance between the i-th mission position and the i+1-th mission position, The data rate (R _i ) has already been calculated in Equation (6). The energy consumption calculation unit 241 is the sum of the communication energy (E _{com ) calculated in Equation 7 and the propulsion energy E prl} calculated in Equation 10 to calculate the _{total energy (E tot} ) for performing the entire task. It can be calculated according to Equation 11.

When the total energy (E _{tot ) is calculated from Equation 11, the energy consumption (E one} ) for each task consumed during one cycle of performing the mission by moving to one of the total N mission positions is expressed by the equation 12 can be calculated according to

where P _one is the minimum power required for one cycle. P _one can be calculated by Equation 13 according _{to the time (T hov} ) for performing a mission while stationary flight at each mission location given the data size (D) to be collected at one mission location.

When the energy consumption per mission (E _one ) is calculated according to Equation 12, the total drone energy consumption (E _t ) can be calculated by Equation 14 according to the total number of mission locations (N) and the energy consumption per mission (E _{one ).} can

Here, τ is an environmental variable and may be specified according to the weather and the level of aging of the drone.

)를 수학식 15에 따라 계산할 수 있다.When the total drone energy consumption (E _t ) is calculated, the energy consumption calculation unit 241 calculates the number of energy-based drones _{( E full} ) and the total drone energy consumption (E _{t ) in a fully charged state.}

) can be calculated according to Equation 15.

는 올림값을 출력하는 올림값 함수이다.here

is a rounded value function that outputs a rounded value.

_{Meanwhile, the mission execution time calculation unit 242 may calculate the consumption time (T one} ) for each mission for one cycle in which one drone performs a mission at one mission location by Equation (16).

Where T _hov denotes how long the drones are still flying in order to perform the task in a task where, T _trs is calculated (T dividing the moving speed of the moving distance (d _m) to the movement time of the drone drone (v) _trs = d _m / v).

When the consumption time (T _one ) for each task is obtained from Equation 16, the total operating time (T _t ) for performing all N tasks may be calculated by Equation 17.

When the total time spent (T _t ) is calculated, the smaller time is obtained as the total drone operation time (T _D ) by comparing the given mission time limit (L _max ) with the total time spent (T _{t ) required to complete the mission} do.

)를 수학식 18에 따라 계산할 수 있다.Based on the drone operating time (T _full ) and the total drone operating time (T _D ) in a fully charged state, the number of time-based drones (

) can be calculated according to Equation 18.

Here, k represents the number of drone charging times, and T _char represents the time required to fully charge one drone.

)를 계산하는 경우, 모든 드론이 항시 충전 상태가 아니므로, 임무 수행에 차질이 발생될 수 있다. 이에 수학식 18에서는 드론 충전 횟수(k)와 드론 충전 시간(T_char)을 함께 고려하여 시간 기반 드론 개수(

)를 계산한다.In the case of drones, the charging time of the drone is relatively long compared to the actual flight time. Therefore, the number of time-based drones (

), since all drones are not always in a charged state, a setback may occur in mission performance. Thus, in equation (18) in consideration with the number of charge cycles drone (k) and the drone charging time (T _char), time-based drone number (

) is calculated.

_{The battery consumption calculation unit 243 calculates the battery consumption (B one} ) for each mission required for one cycle in which one drone performs a mission at one mission location according to Equation 19.

And, from the battery consumption (B _one ) for each mission, the total drone battery consumption (B _t ) for performing the entire mission may be calculated according to Equation (20).

)를 수학식 21에 따라 계산한다.From the total battery consumption (B _t ) and the drone battery capacity at full charge (B _full ), the number of battery-based drones (

) is calculated according to Equation 21.

)와 임무 수행 시간 계산부(242)에서 계산된 시간 기반 드론 개수(

) 및 배터리 소비량 계산부(243)에서 계산된 배터리 기반 드론 개수(

)를 인가받는다.The number of drones determining unit 244 is the number of energy-based drones calculated by the energy consumption calculation unit 241 (

) and the number of time-based drones calculated by the mission execution time calculation unit 242 (

) and the number of battery-based drones calculated by the battery consumption calculation unit 243 (

) is approved.

)와 시간 기반 드론 개수(

) 및 배터리 기반 드론 개수(

) 중 최대 개수를 수학식 22와 같이 추출하여 임무 수행을 위해 요구되는 최소의 최적 드론 개수(

)를 판별한다.If at least one of the requirements of energy consumption, mission performance time, and battery consumption is not met, the entire mission cannot be fully performed. Therefore, the number of drones determining unit 244 is the number of applied energy-based drones (

) and the number of time-based drones (

) and the number of battery-powered drones (

) by extracting the maximum number of drones as in Equation 22, and the minimum optimal number of drones required to perform the mission (

) is determined.

)를 판별하는 기법을 여기서는 드론 부대 전개 최적화(Drone Force Deployment Optimization: DFDO) 알고리즘이라고 한다.As described above, the optimal number of drones required for the drone optimization unit 240 to perform a mission (

) is referred to herein as a Drone Force Deployment Optimization (DFDO) algorithm.

)에 따라 임무 수행을 위해 운용할 드론(101)을 선택하고, 임무 설정부(220)에서 설정된 임무 위치별 임무에 따라 통신부(210)를 통해 선택된 드론(101)으로 제어 신호를 전송하여, 선택된 적어도 하나의 드론(101)이 지정된 임무 위치에서의 임무를 수행하도록 한다. 이때 드론 제어부(250)는 다수의 드론이 선택된 경우, 선택된 드론들의 임무 위치가 중첩되지 않도록 하고, 드론이 임무 지역의 임무 위치들 사이를 이동할 때 동선이 최적의 동선을 계산하여 이동 거리가 최소화되도록 제어할 수 있다.The drone control unit 250 determines the optimal number of drones determined by the drone optimization unit 240 (

) to select the drone 101 to be operated to perform the mission, and transmit a control signal to the selected drone 101 through the communication unit 210 according to the mission for each mission location set in the mission setting unit 220, At least one drone 101 is to perform a mission in a designated mission position. In this case, when a plurality of drones are selected, the drone control unit 250 prevents the mission positions of the selected drones from overlapping, and calculates the optimal movement route when the drone moves between the mission positions in the mission area so that the movement distance is minimized. can be controlled

4 shows a drone control method according to an embodiment of the present invention.

Referring to FIGS. 1 to 3 , the drone control method of FIG. 4 is described. First, a task to be performed using at least one drone is acquired and stored ( S11 ). Here, the mission includes the entire mission area containing at least one mission location where the mission must be performed and the mission time limit (L _max ) within which the mission must be performed.

When the mission is acquired, information for performing the mission is collected (S12). The information collected here may collect and store environmental information, channel information, and operating drone information for each of at least one mission location in the mission area. Here, the information may be collected and stored in advance according to the case, or may be acquired along with the mission.

)를 판별하고(S30), 이와 함께 드론의 임무 수행 시간에 기초하여 요구되는 드론의 개수인 시간 기반 드론 개수(

)를 판별한다(S40). 또한 운용 되는 드론의 배터리 소비량을 고려하여 요구되는 드론의 개수인 배터리 기반 드론 개수(

)를 판별한다(S50).When information for mission performance is collected, based on the collected information, the number of energy-based drones (

) is determined (S30), and the number of time-based drones (the number of drones required based on the drone's mission execution time)

) is determined (S40). In addition, the number of battery-based drones (

) is determined (S50).

)를 판별하기 위해서는, 우선 임무 지역의 적어도 하나의 임무 각각에서 소비되는 임무별 에너지 소비량(E_one)을 계산한다(S31). 여기서 임무별 에너지 소비량(E_one)은 드론이 임무 수행시에 통신을 수행하기 위해 소비하는 통신 에너지(E_com)와 드론의 임무 위치로 이동하기 위해 소비하는 추진 에너지(E_prl)를 구분하여 각각 수학식 7 및 10에 따라 계산하고, 계산된 추진 에너지(E_prl)와 통신 에너지(E_com)를 수학식 12와 같이 결합하여 계산될 수 있다.Number of energy-based drones (

_{), first, the amount of energy consumption (E one} ) for each mission consumed in each of at least one mission in the mission area is calculated (S31). Here, the energy consumption for each mission (E _one ) is divided into _{communication energy (E com} ) consumed by the drone to perform communication when performing a _{mission and propulsion energy (E prl} ) consumed to move to the mission position of the drone. It can be calculated according to Equations 7 and 10, and calculated by _{combining the calculated propulsion energy (E prl} ) and the communication energy (E _com ) as in Equation 12.

When the energy consumption for each mission (E _one ) is calculated, the total drone energy consumption (E _t ) is calculated according to Equation 14 according to the total number of mission locations in the mission area (N) and the energy consumption for each mission (E _{one )} (S32).

)를 획득한다(S33).And, as shown in Equation 15, the total drone energy consumption (E _t ) is divided by the drone available energy (E _full ) in a fully charged state and rounded up to increase the number of energy-based drones (

) is obtained (S33).

)를 판별하기 위해, 우선 하나의 드론이 하나의 임무 위치에서 임무를 수행하는 1사이클 동안 소요되는 임무별 소비 시간(T_one)을 수학식 16에 따라 계산한다. 그리고 계산된 임무별 소비 시간으로부터 전체 임무를 수행하기 위해 소요되는 총 소비 시간(T_t)을 수학식 17에 따라 계산한다(S42). 총 소비 시간(T_t)이 계산되면, 주어진 임무 제한 시간(L_max)과 임무를 완료하기 위해 필요한 총 운용 시간(T_t)을 비교하여 더 작은 시간을 총 드론 운용 시간(T_D)으로 획득하고, 획득된 총 드론 운용 시간(T_D)을 완전 충전 상태의 드론 운용 시간(T_full)과 드론의 충전 시간(T_char)의 합 나누고 올림하여 시간 기반 드론 개수(

)를 계산한다(S43).On the other hand, the number of time-based drones (

_{), first, the time consumed for each mission (T one} ) required for one cycle in which one drone performs a mission at one mission location is calculated according to Equation 16. Then, from the calculated consumption time for each task, the total consumption time (T _t ) required to perform the entire task is calculated according to Equation 17 ( S42 ). Once the total consumption time (T _t ) is calculated, the smaller time is obtained as the total drone operation time (T _D ) by comparing the given mission time limit (L _max ) with the total operating time required to complete the mission (T _{t )} Then, the total drone operation time (T _D ) obtained is divided by the sum of the drone operation time (T _full ) in the fully charged state and the drone charging time (T _char ) and rounded up to increase the number of time-based drones (

) is calculated (S43).

)를 판별하기 위해서는 먼저, 각 임무 위치에서 소요되는 드론의 배터리 소비량을 나타내는 임무별 배터리 소비량(B_one)을 수학식 19에 따라 계산한다(S51). 임무별 배터리 소비량(B_one)이 계산되면, 전체 임무를 수행하기 위한 총 드론 배터리 소비량(B_t)을 수학식 20에 따라 계산한다(S52). 그리고 총 드론 배터리 소비량(B_t)을 완전 충전 상태의 드론 배터리 용량(B_full)으로 나누고 올림하여, 배터리 기반 드론 개수(

)를 계산한다(S53).Also, the number of battery-powered drones (

_{), first, the battery consumption (B one} ) for each mission indicating the battery consumption of the drone required at each mission position is calculated according to Equation 19 (S51). When the battery consumption (B _one ) for each task is calculated, the total drone battery consumption (B _t ) for performing the entire mission is calculated according to Equation 20 ( S52 ). Then, by dividing the total drone battery consumption (B _t ) by the drone battery capacity (B _full ) in a fully charged state, the number of battery-based drones (

) is calculated (S53).

)와 시간 기반 드론 개수(

) 및 배터리 기반 드론 개수(

)가 판별되면 이중 최대 개수를 임무 수행을 위해 요구되는 최소의 최적 드론 개수(

)로 판별한다(S60).Number of energy-based drones (

) and the number of time-based drones (

) and the number of battery-powered drones (

) is determined, the maximum number among them is the minimum optimal number of drones required to perform the mission (

) is determined (S60).

)에 대응하는 개수의 드론을 선택한다(S70). 그리고 선택된 드론과 통신을 수행하여 임무 지역 내에서 선택된 드론이 서로 임무 위치가 중첩되지 않고, 모든 임무 위치에서 지정된 임무를 수행하도록 제어한다(S80).The optimal number of drones (

) and select the number of drones corresponding to (S70). And by performing communication with the selected drone, the selected drone within the mission area is controlled so that the mission positions do not overlap with each other and perform the specified mission in all mission positions (S80).

5 to 9 show simulation results of the performance of the drone control method according to the present embodiment.

)를 나타낸다. 도 5에 도시된 바와 같이, 본 실시예에 따른 드론 제어 방법인 DFDO 알고리즘은 임무 지역의 크기에 대략적으로 비례하여 최적 드론 개수(

)를 판별하지만, 임무 지역의 환경이나 드론 상태 등과 같이 수집되는 정보에 따라 일부 변동을 나타냄을 알 수 있다.5 is an optimal number of drones determined according to the size of the mission area (

) is indicated. As shown in FIG. 5 , the DFDO algorithm, which is a drone control method according to the present embodiment, is approximately proportional to the size of the mission area and the optimal number of drones

), but it can be seen that it shows some fluctuations depending on the information collected, such as the environment of the mission area or the state of the drone.

6 shows a result of comparing the performance of the DFDO algorithm according to the present embodiment and the recently published PETROL (Power Control) algorithm. As described above, the PETROL algorithm designed to minimize power consumption because the charging time of the drone is relatively long compared to the available flight time, as shown in FIG. application is not suitable. On the other hand, the DFDO algorithm of the present embodiment allows the mission to be completed within a shorter and uniform time compared to the PETROL algorithm, so it can be seen that it is an algorithm very suitable for performing an operational mission or responding to a disaster.

)를 조절함으로써, 임무가 항시 임무 제한 시간(L_max) 내에 수행될 수 있도록 한다. 도 7에서 DFDO 알고리즘에 따른 그래프의 하단에 적힌 숫자는 판별된 최적 드론 개수(

)를 나타낸다. 그리고 DFDO 알고리즘의 하부 및 상부에 도시된 그래프는 각각 최적 드론 개수(

)보다 드론의 개수를 3개 증가시킨 경우와 3개 감소시킨 경우에 임무 완수를 위해 소요되는 시간을 나타낸다.7 illustrates a time taken to complete a mission when the mission limit time (L _max ) is specified and the size of the mission area is changed. As can be seen in FIG. 7 , the DFDO algorithm according to this embodiment is the optimal number of drones _{(L max ) according to the mission limit time (L max ), even if the size of the mission area is changed.}

), so that the task can always be performed within the _{task limit time (L max ).} In FIG. 7, the number written at the bottom of the graph according to the DFDO algorithm is the determined optimal number of drones (

) is indicated. And the graphs shown in the lower and upper parts of the DFDO algorithm are the optimal drone count (

) shows the time it takes to complete the mission when the number of drones is increased by 3 and when the number of drones is decreased by 3.

)보다 드론의 개수를 3개 감소시키는 경우, 임무 제한 시간(L_max) 이내에 임무를 완수하지 못하게 되는 반면, 3개 증가시키게 되면, 드론 운용의 효용성이 크게 낮아지게 된다.7, the optimal number of drones (

), if the number of drones is reduced by 3, the mission _{cannot be completed within the mission limit time (L max} ), whereas if the number is increased by 3, the effectiveness of drone operation is greatly reduced.

8 illustrates a change in interference power according to an increase in the number of operated drones when the size of the mission area is the same. As shown in FIG. 7 , if the number of drones is increased, the time required to complete the mission is reduced. However, as shown in FIG. 8 , the interference power increases in proportion to the increase in the number of drones, and thus the outage probability of the drone increases. That is, if the number of drones is excessively increased, the drones may become inoperable, which may impede mission performance.

)와 시간 기반 드론 개수(

) 및 배터리 기반 드론 개수(

)의 변화와 이에 의해 판별되는 최적 드론 개수(

)를 나타낸다.9 shows the number of energy-based drones according to the number of mission positions (

) and the number of time-based drones (

) and the number of battery-powered drones (

) and the optimal number of drones (

) is indicated.

)는 시간 기반 드론 개수(

)에 따르지만, 임무 위치의 개수가 증가함에 따라 최적 드론 개수(

)는 에너지 기반 드론 개수(

) 또는 배터리 기반 드론 개수(

)를 따르게 됨을 알 수 있다. 도 9에서는 에너지 기반 드론 개수(

)와 배터리 기반 드론 개수(

)가 유사하게 나타나 있으나, 실제로는 드론 운용 환경에 따라 에너지 기반 드론 개수(

)와 배터리 기반 드론 개수(

) 또한 차이가 발생된다.As shown in FIG. 9, when the number of mission positions is relatively small, the optimal number of drones (

) is the number of time-based drones (

), but as the number of mission locations increases, the optimal number of drones (

) is the number of energy-based drones (

) or the number of battery-powered drones (

) can be seen to follow. 9, the number of energy-based drones (

) and the number of battery-powered drones (

) is similar, but in reality, the number of energy-based drones (

) and the number of battery-powered drones (

) also makes a difference.

The method according to the present invention may be implemented as a computer program stored in a medium for execution by a computer. Here, the computer-readable medium may be any available medium that can be accessed by a computer, and may include all computer storage media. Computer storage media includes both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data, and read dedicated memory), RAM (Random Access Memory), CD (Compact Disk)-ROM, DVD (Digital Video Disk)-ROM, magnetic tape, floppy disk, optical data storage, and the like.

Although the present invention has been described with reference to the embodiment shown in the drawings, which is merely exemplary, those skilled in the art will understand that various modifications and equivalent other embodiments are possible therefrom.

Accordingly, the true technical protection scope of the present invention should be defined by the technical spirit of the appended claims.

101: drone 103: drone control device
210: communication unit 220: mission setting unit
230: information collection unit 240: drone optimization unit
241: energy consumption calculation unit 242: task performance time calculation unit
243: battery consumption calculation unit 244: number of drones determining unit
250: drone control unit

Claims (10)

a mission setting unit for acquiring a mission to be performed using at least one drone;
Information collection for collecting information on an environment of a mission area including at least one mission location where the mission is to be performed, and a channel for communicating with the at least one drone and the drone at each of the at least one mission location part;
Based on the collected information, the total drone consumption energy, total drone operation time, and total drone battery consumption of the drone for performing the mission at each of the entire mission positions in the mission area are calculated, and the total drone battery consumption required for the mission is calculated from the total drone consumption energy. Determining the number of energy-based drones that is the number of drones, determining the number of time-based drones that are the number of drones required for mission performance from the total drone operating time, and determining the number of drones required for mission performance from the total drone battery consumption a drone optimization unit that determines the number of battery-based drones and obtains an optimal number of drones for performing the mission from the number of energy-based drones, the number of time-based drones, and the number of battery-based drones; and
a drone control unit that selects a number of drones corresponding to the optimal number of drones, and communicates and controls the selected drones to perform a specified mission at each of at least one mission position,
The drone optimization unit
calculating the energy consumption for each mission consumed in each of at least one mission in the mission area, calculating the total drone energy consumption according to the total number of mission positions in the mission area and the energy consumption for each mission, and calculating the total drone energy consumption an energy consumption calculation unit that determines the number of energy-based drones by dividing it by the available energy of the drones in a fully charged state collected in advance;
Calculates the time consumed for each mission during one cycle when one drone performs a mission at one mission location, calculates the total time consumed to perform the entire mission from the time consumed by each mission, and calculates the The time-based drone is obtained by comparing the included mission time limit with the total consumption time to obtain a smaller time as the total drone operation time, and dividing the obtained total drone operation time by the pre-collected drone operation time in a fully charged state a task execution time calculation unit to determine the number;
Calculate the battery consumption for each mission consumed in each of at least one mission location, calculate the total drone battery consumption according to the total number of mission locations in the mission area and the battery consumption for each mission, and calculate the total drone battery consumption in advance. a battery consumption calculation unit dividing the drone battery capacity in a fully charged state to determine the number of battery-based drones; and
and a drone number determining unit configured to determine a maximum value among the number of energy-based drones, the number of time-based drones, and the battery-based number of drones as an optimal number of drones.
delete According to claim 1, wherein the energy consumption calculation unit
When the drone performs a mission, the communication energy consumed to perform communication and the propulsion energy consumed to move to the drone's mission position are divided and calculated, and the calculated propulsion energy and the communication energy are added to the energy consumption for each mission. A drone control unit that counts. The method of claim 3, wherein the energy consumption calculation unit
According to the channel state between the drone disposed at each of at least one mission position and the drone control device, communication power consumption is calculated by dividing line-of-sight communication and non-line-of-sight communication, and averaged from line-of-sight communication power consumption and non-line-of-sight communication power consumption. calculating the transmission rate by analyzing the path loss, calculating the communication energy according to the transmission rate, the data transmission time at each mission location, and the size of the data to be transmitted,
The hovering power, which is the power required for stationary flight of the drone, and the moving power, which is the power required for the moving flight of the drone, are respectively calculated, the hovering power is multiplied by the time the drone transmits data at the mission location, and the moving power is A drone control device that calculates the propulsion energy by multiplying and summing the travel time to an adjacent mission location. According to claim 1, wherein the task execution time calculation unit
A drone control device for determining the number of battery-based drones by dividing the total drone battery consumption by the sum of the drone battery capacity and the drone charging time. acquiring a mission to be performed using at least one drone;
collecting information on an environment of a mission area including at least one mission location at which the mission is to be performed, and a channel through which the at least one drone and the drone are placed in each of the at least one mission location to perform communication;
Based on the collected information, the total drone consumption energy, the total drone operation time, and the total drone battery consumption of the drone for performing the mission at each of the entire mission locations in the mission area are calculated, and the total drone battery consumption required for the mission is calculated from the total drone consumption energy. Determining the number of energy-based drones that is the number of drones, determining the number of time-based drones that are the number of drones required for mission performance from the total drone operating time, and determining the number of drones required for mission performance from the total drone battery consumption determining the number of battery-based drones, and obtaining the optimal number of drones for performing the mission from the number of energy-based drones, the number of time-based drones, and the number of battery-based drones; and
Selecting a number of drones corresponding to the optimal number of drones, and communicating and controlling the selected drones to perform a specified task at each of at least one task location,
The step of obtaining the optimal number of drones is
calculating the energy consumption for each mission consumed in each of at least one mission in the mission area, calculating the total drone energy consumption according to the total number of mission positions in the mission area and the energy consumption for each mission, and calculating the total drone energy consumption determining the number of energy-based drones by dividing it by the available energy of the drones in a fully charged state collected in advance;
Calculates the time consumed for each mission during one cycle when one drone performs a mission at one mission location, calculates the total time consumed to perform the entire mission from the time consumed by each mission, and calculates the Comparing the included mission time limit and the total consumption time, a smaller time is obtained as the total drone operation time, and the time-based drone is divided by the obtained total drone operation time by the pre-collected drone operation time in a fully charged state. determining the number;
Calculate the battery consumption for each mission consumed in each of at least one mission location, calculate the total drone battery consumption according to the total number of mission locations in the mission area and the battery consumption for each mission, and calculate the total drone battery consumption in advance. determining the number of battery-based drones by dividing the drone battery capacity in a fully charged state; and
and determining a maximum value among the number of energy-based drones, the number of time-based drones, and the number of battery-based drones as an optimal number of drones.
delete The method of claim 6, wherein determining the number of energy-based drones comprises:
When the drone performs a mission, the communication energy consumed to perform communication and the propulsion energy consumed to move to the drone's mission position are divided and calculated, and the calculated propulsion energy and the communication energy are added to the energy consumption for each mission. How to control the drone to calculate. The method of claim 8, wherein determining the number of energy-based drones comprises:
According to the channel state between the drone disposed at each of at least one mission position and the drone control method, communication power consumption is calculated by dividing line-of-sight communication and non-line-of-sight communication, and averaged from line-of-sight communication power consumption and non-line-of-sight communication power consumption. calculating the transmission rate by analyzing the path loss, calculating the communication energy according to the transmission rate, the data transmission time at each mission location, and the size of the data to be transmitted; and
The hovering power, which is the power required for stationary flight of the drone, and the moving power, which is the power required for the moving flight of the drone, are respectively calculated, the hovering power is multiplied by the time the drone transmits data at the mission location, and the moving power is and calculating the propulsion energy by multiplying and summing time to move to an adjacent mission position. The method of claim 6, wherein determining the number of battery-based drones comprises:
A drone control method for determining the number of battery-based drones by dividing the total drone battery consumption by the sum of the drone battery capacity and the drone charging time.

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