Method for UAV Path Planning to Support Multiple Ground Users

Abstract

The present invention relates to a method for establishing a basic path for a UAV to support multiple ground users. The present invention is characterized by comprising the steps of: placing UAVm, which is the m-th UAV among a plurality of UAVs, at an arbitrary IoT device ko among a plurality of IoT devices, and then finding another IoT device k1 located closest to ko; removing ko and k1 from Km, which is a total set of IoT devices, and adding a new path segment to Qm, which is a set of path segments, for the path between ko and k1; finding another IoT device k2 located closest to the remaining Km excluding ko and k1; and, when k2 is found, excluding some path segments of the path between ko and k1 from Qm and adding a new path segment including k2.

Description

Technology Field

[0001] The present invention relates to a method for setting a basic path of a UAV (unmanned aerial vehicle) to support multiple ground users. Background Technology

[0003] Generally, an Unmanned Aerial Vehicle (UAV) refers to an unmanned aircraft operated by remote control, such as a drone. Recently, unmanned aircraft are being used for a wide variety of purposes, not just military applications.

[0004] More specifically, the uses of unmanned aerial vehicles (UAVs) are becoming increasingly diverse, such as replacing manned aircraft to fly over dangerous areas and capturing images of the surroundings from a distance. Furthermore, the use of UAVs is gradually increasing, particularly in the form of quadcopters, which are widely known as drones.

[0005] Unmanned aerial vehicles (UAVs) for this purpose play the role of relaying information collected from the surroundings by ground-based IoT (Internet of Things) devices and transmitting it to base stations.

[0006] Here, the IoT device collects data in various fields such as disaster rescue, logistics transportation, monitoring, and surveillance, and transmits it to a separate central system to provide various services.

[0007] Therefore, the flight path of the UAV must be optimized to efficiently collect information from IoT devices on the ground that are widely distributed.

[0008] This is because, in order to operate multiple UAVs to support several IoT devices on the ground, the first step is to identify in advance the appropriate initial path for each UAV to visit the IoT devices assigned to it.

[0009] However, UAVs collect information received from IoT devices only during the limited operating time powered by batteries.

[0010] Therefore, due to the energy constraints of UAVs, it is difficult to collect data from widely distributed IoT devices. Consequently, cooperation among multiple UAVs is required in such cases, but since this incurs high management costs, finding the optimal number of UAVs is one of the most important issues in reducing overall operating costs.

[0011] Therefore, it is very important for UAVs to visit self-assigned ground IoT devices to establish an initial path and scale down, and development and research in this area are receiving significant attention. Prior art literature

[0013] Republic of Korea Patent Publication No. 2021-0156506 Republic of Korea Patent Publication No. 2020-0044946 The problem to be solved

[0014] Therefore, the present invention was devised to solve these problems and aims to present a systematic and efficient method for a UAV (unmanned aerial vehicle) to visit ground IoT devices assigned to it and establish an initial path.

[0015] In addition, the purpose is to provide a basic path setting method for a UAV that can expand the path to support other newly added IoT devices, starting with an IoT device close to the UAV, in order for the UAV to visit distributed IoT devices on the ground. means of solving the problem

[0017] A method for establishing a basic path for a UAV according to one embodiment comprises: a method performed in a computing device having one or more processors and a memory storing one or more programs executed by the one or more processors, and communicating with a plurality of UAVs to establish a path for a plurality of UAVs to pass through a plurality of IoT devices, the method comprising: a step of placing UAVm, which is the m-th UAV among a plurality of UAVs, at an arbitrary IoT device ko among a plurality of IoT devices, and then finding another IoT device k1 located closest to ko; a step of removing ko and k1 from a total set of IoT devices Km, and adding a new path segment to a set of path segments Qm, which is a path between ko and k1; a step of finding another IoT device k2 located closest to the remaining Km excluding ko and k1; and a step of, when k2 is found, excluding some path segments of the path between ko and k1 from Qm and adding a new path segment including k2.

[0018] The step of finding k2 above may be characterized by finding an intermediate point in Qm, which is a set of path segments, using the straight-line distance of the corresponding path segment, and finding the IoT device at the shortest location by comparing the intermediate point with other IoT devices.

[0019] The method for setting the basic path of the above UAV may further include the step of excluding the IoT device k2 from the above Km after adding a new path segment including the above k2.

[0020] It may be characterized by continuously repeating the execution until all of the above-mentioned IoT devices in the above km are excluded and there are no more IoT devices to assign to the UAV.

[0021] Optimization of the UAVm trajectory that maximizes the minimum transmission speed of the IoT device while maintaining the number of IoT devices assigned to the UAVm can be expressed by the following mathematical formula.

[0022] (Mathematical formula)

[0023]

[0024] : Association variable between the k-th IoT and the m-th UAV in time slot n

[0025] : Transmission rate between the k-th IoT and the m-th UAV in time slot n

[0026] : Number of time slots

[0027] : Number of UAVs

[0028] Number of IoT devices

[0029] : Location of UAV or IoT

[0030] It may be characterized by using the Edge Deployment Successive Convex Approximation (ED-SCA) technique to maximize the total transmission speed of the IoT devices while maintaining the number of IoT devices assigned to each of the above UAVs, find the minimum number of UAVs, and optimize the trajectory.

[0031] The above ED-SCA can use a heuristic method to minimize unnecessary UAVs by removing them. Effects of the invention

[0033] Therefore, the present invention optimizes the path of a UAV (unmanned aerial vehicle), thereby enabling the UAV to efficiently collect information from ground-based Internet of Things (IoT) devices.

[0034] In addition, by reducing the number of UAVs collecting information received through IoT devices, it is possible to not only cut operational costs but also increase the average transmission rate. Brief explanation of the drawing

[0036] FIG. 1 is a diagram showing the path setting of multiple UAVs (unmanned aerial vehicles) in an IoT (Internet of Things) communication system. Figure 2 is a diagram showing an algorithm representing the entire process of UAV deployment and removal. Figure 3 is a diagram showing the step of adding an IoT device to set the initial path of a UAV. FIG. 4 is a flowchart of a basic path setting method for a UAV to support multiple ground users according to an embodiment of the present invention. Figure 5 is a diagram showing an algorithm representing an initial trajectory for load balancing. FIG. 6 is a drawing for explaining a computing environment including a computing device according to an embodiment of the present invention. Specific details for implementing the invention

[0037] Hereinafter, specific embodiments of the present invention will be described with reference to the drawings. The following detailed description is provided to facilitate a comprehensive understanding of the methods, apparatuses, and / or systems described herein. However, this is merely illustrative and the present invention is not limited thereto.

[0038] In describing the embodiments of the present invention, detailed descriptions of known technologies related to the present invention are omitted if it is determined that such detailed descriptions may unnecessarily obscure the essence of the present invention. Furthermore, the terms described below are defined in consideration of their functions within the present invention, and these may vary depending on the intentions or practices of the user or operator. Therefore, such definitions should be based on the content throughout this specification. Terms used in the detailed description are intended merely to describe the embodiments of the present invention and should not be limiting in any way. Unless explicitly stated otherwise, expressions in the singular form include the meaning of the plural form. In this description, expressions such as "include" or "comprise" are intended to refer to certain characteristics, numbers, steps, actions, elements, parts thereof, or combinations thereof, and should not be interpreted to exclude the existence or possibility of one or more other characteristics, numbers, steps, actions, elements, parts thereof, or combinations thereof other than those described.

[0040] FIG. 1 is a diagram showing the path setting of multiple UAVs (unmanned aerial vehicles) in an IoT (Internet of Things) communication system; FIG. 2 is a diagram showing an algorithm showing the entire process of UAV deployment and removal; FIG. 3 is a diagram showing the step of adding an IoT device to set the initial path of a UAV; FIG. 4 is a flowchart of a method for setting a basic path of a UAV to support multiple ground users according to an embodiment of the present invention; FIG. 5 is a diagram showing an algorithm showing an initial trajectory for load balancing; and FIG. 6 is a diagram for explaining a computing environment including a computing device according to an embodiment of the present invention.

[0042] Referring to FIG. 1, a method for setting a basic path for a UAV to support multiple ground users according to an embodiment of the present invention relates to setting a basic path for multiple unmanned aerial vehicles (UAVs) in which a base station (BS) is deployed to improve data freshness and connectivity with an Internet of Things (IoT) device.

[0043] UAVs (100) for this purpose are used to collect data received from Internet of Things (IoT) devices (200) in response to wildfires, wildlife monitoring, weather research, military missions, etc.

[0044] Due to its high mobility, the UAV (100) can establish a direct communication link with a widely distributed IoT device (200). However, because the battery capacity is limited, the UAV (100) periodically returns to a charging station after completing its mission within a predetermined operating time.

[0045] Furthermore, as IoT devices (200) become widely distributed or their number increases, it becomes difficult to cover these devices with a single UAV. By collaborating with multiple UAVs (100), the widely distributed IoT devices (200) can be covered with a guaranteed quality of service (QoS) of wireless communication.

[0046] However, as the number of UAVs (100) increased, operating costs also increased significantly because the UAVs had to be controlled accurately and in a timely manner for collision avoidance and routing.

[0047] Therefore, it is important to determine the optimal number of UAVs while guaranteeing QoS in IoT applications.

[0048] For reference, the UAV (100) has a configuration including a camera unit (not shown) for capturing an image of an object, a GPS (Global Positioning System) unit (not shown) for verifying the coordinates of the object, a data collection unit (not shown) that communicates with an Internet of Things (IoT) device to recognize, acquire, and collect data such as information, and a communication unit (not shown) that communicates with an external server (not shown).

[0049] Referring to FIG. 1, the uplink system for IoT data collection consists of a set of UAVs (100) of set M and IoT devices (200) of set K.

[0050] In order to collect information from all IoT devices (200) within a set operating time (T), all UAVs (100) visit the assigned IoT devices (200) and return to their initial positions.

[0051] The above operating time T is divided into N identical time slots indexed by n, where N = {n|n = 0, . . . , N - 1}. The maximum travel distance of the UAV during the above time slot period is denoted by Smax, and the UAV returns to its initial position after the Nth time slot.

[0052] The position of the m-th UAV (m ∈ M) is represented as Xm[n] in time slot n.

[0053] The UAV (100) is considered to move along a constant altitude, and the IoT device (200) continuously collects data by monitoring the environment from a fixed location. The location of the k-th IoT device (200) is indicated as Xk, k ∈ K.

[0054] The association variable between the IoT device (200) and the UAV (100) is α when the IoT device (200) k is assigned to the m-th UAV (100) in time slot n. k,m [n] = 1, and if not, α k,m α when [n] = 0 k,m It is indicated as [n].

[0055] In addition, the association variable between IoT device k and UAVm is b when IoT device k is assigned to the m-th UAV. k,m = 1, and if not, b k,m If = 0, b k,m It is defined as.

[0056] It is assumed that all IoT devices (200) have an infinite number of unprocessed messages to transmit during continuous environmental monitoring. When a UAV (100) enters the transmission range Rc of the IoT device (200), the IoT device (200) recognizes the UAV (100) and transmits a message to the UAV (100).

[0057] A ground-to-air model (G2A) is considered as a wireless channel model for an up-link system. The G2A model between a ground IoT device (200) and a UAV (100) is generally classified into a line of sight (LoS) model and a non-line of sight (NLoS) model.

[0058] The link establishment probabilities of the Line of Sight (LoS) model and the Non-Line of Sight (NLoS) model between IoT device k and UAVm are as follows.

[0059] [Equation 1]

[0060] [Equation 2]

[0061] Here, the above θ k,m is the elevation angle between the IoT device k and the UAVm.

[0062] ε and δ are parameters that vary depending on the communication environment and channel conditions, and are shape parameters that reflect the characteristics of the actual environment. The average path loss for LoS and NLoS models is given in dB as follows in [Equation 3], [Equation 4], and [Equation 5].

[0063] [Equation 3]

[0064] [Equation 4]

[0065] [Equation 5]

[0066] Here, c is the speed of light, ηLoS and ηNLoS are the mean additional path losses, and h uav is the altitude of the UAV, fc is the carrier frequency, and d k,m [n] = rk,m[n] is the horizontal distance between IoT device k and UAVm, LLoS(k,m)[n] and LNLoS(k,m)[n] are the path losses of the LoS and NLoS links, respectively, and A = ηLoS - ηNLoS, ηNLoS is.

[0067] The transmission speed between IoT device k and UAVm is R k,m It is denoted by [n] and is equal to the following [Equation 6].

[0068] [Equation 6]

[0069] Here, g k,m [n] is gdB k,m [n] is a linear scale transformation, σ2 is the noise spectral density, Pk is the transmission power for IoT device k, and B is the total bandwidth.

[0070] Since operating costs are proportional to the number of UAVs (100), it is important to increase the transmission speed per UAV in the network while ensuring a minimum average transmission speed for all IoT devices (200).

[0071] Therefore, the problem considered is formulated as follows.

[0072]

[0073] Here, (7b) is a constraint on the maximum travel distance over time slots.

[0074] (7c) means that the UAV (100) returns to its initial position in the Nth time slot.

[0075] (7d) is αk,m This means that it takes an integer value of 0 or 1, and (7e) and (7f) indicate that a single IoT device must communicate with a specific UAV throughout the operation time to reduce interference between IoT devices.

[0076] (7g) indicates that the sum of the association variables between the IoT device (200) and UVAm (100) is less than Nmax.

[0077] (7h) guarantees that the average transmission speed of all IoT devices (200) must be higher than the minimum guaranteed speed ηmin.

[0078] Due to the non-convex nature of (7a), (7b), (7d), and (7h), the formulated problem (P1) is divided into two subproblems: finding the minimum number of UAVs required and optimizing the trajectories of the UAVs. To minimize the number of UAVs required, the following subproblem (P2) is designed.

[0079] [8a]

[0080] The above subproblem determines the initial deployment of UAVs and the allocation of IoT devices (200) to UAVs (100). However, in the second subproblem, an average transmission speed constraint (see 7h) is considered. Since problem (P2) is still non-convex, a new algorithm ED-SCA is proposed to solve it. This is because it is difficult to find the optimal number of mobile UAVs compared to fixed UAVs.

[0081] In the initial stage of the above ED-SCA algorithm, it is assumed that the UAV (100) is fixed, and then the optimal number of UAVs is determined.

[0082] Next, the number of UAVs (100) is gradually reduced so that all IoT devices (200) can be covered by a reduced amount of UAVs (100). In the next step, the optimal number of mobile UAVs (100) is determined. However, since the number of IoT devices (200) allocated to the UAVs (100) is not the same, the allocation of these IoT devices (200) to the UAVs (100) is further adjusted.

[0083] Below, we will explain the initial deployment of the UAV (100).

[0084] Initially, the UAV (100) is positioned at the convex hull edge of an unassigned IoT device (200), and this process is repeated until all IoT devices are assigned.

[0085] Specifically, the first step is to construct a convex hull of a given set K.

[0086] K hull represents a set of IoT devices that constitute the convex boundary of distributed IoT devices K.

[0087] In the next step, K labeled u1 hull Select an arbitrary IoT device and assign it to UAVm. Since the maximum distance between two nodes assigned to UAVm is 2Rc, all IoT devices within 2Rc of u1 are considered in the set of IoT devices Km of UAVm.

[0088] Next, the number of IoT devices within km is limited to Nmax. The UAVm selects the IoT device with the longest distance within km from u1.

[0089] Finally, the UAV is placed at an intermediate position between u1 and u2. After placing the UAV around the corners of the convex hull Khull, this process is repeated for the remaining uncovered IoT devices, Kuncov.

[0090] The number of UAVs gradually decreases in the aforementioned deployment, assuming a fixed number of UAVs. However, all IoT devices (200) must be covered by a reduced number of UAVs (100), and first, the UAV with the largest number of neighboring UAVs is selected and removed.

[0091] Here, the UAV at the distance is a UAV within a distance of 2Rc from the considered UAV. The IoT device assigned to the removed UAV is reassigned to another UAV that has the shortest distance from each IoT device.

[0092] When the number of allocated IoT devices (100) reaches the maximum, this removal process ends. The overall process of UAV deployment and removal is shown in Algorithm 1 of FIG. 2.

[0093] If the number of UAVs (100) is reduced, an inequality in the number of IoT devices (200) assigned to each UAV (100) may occur. To mitigate this, initial trajectory planning and load distribution are required.

[0094] In load balancing, the set of trajectory segments for a UAVm is denoted as Qm, where a trajectory segment is a movement vector of a UAVm visiting a single IoT device (100) and can be represented as (device ID at departure, device ID at arrival), and this list of segments constitutes the entire trajectory for the UAVm.

[0095] An exemplary process for initial trajectory planning is shown in Fig. 3. A description of the basic path setting method for the UAV is to be given with reference to Fig. 4.

[0096] Assuming there are multiple UAVs, the proposed technique is explained for the m-th UAV. Since interactions between UAVs are not considered, the content of the technique is the same whether there is one UAV or multiple UAVs.

[0097] Before explaining this step, I would like to explain the following facts in advance.

[0098] The set of path segments of the m-th UAV m is set as Qm.

[0099] Here, the path segment refers to a straight path between two IoT devices when the UAV visits one IoT device and then visits another IoT device. This path segment is a vector that can be represented by (IoT device ID located at the starting point, IoT device ID located at the destination point).

[0100] The set of IoT devices assigned to the UAVm is represented as Km = {k0,k1,k2,k3,...}. A description of the specific steps for setting the initial path of the above UAVm will be provided with reference to Fig. 4.

[0101] First, multiple IoT devices (200) are deployed, and multiple UAVs (100) are flown along an orbit (S100).

[0102] Referring again to FIGS. 1 and FIGS. 3 mentioned earlier, a UAV (100) flies along an arbitrary trajectory and obtains information such as data from an IoT device (200).

[0103] When the m-th UAV in a group of multiple UAVs (100) is placed as UAVm, the UAVm is placed as ko, which is any one IoT device among multiple IoT devices (200), and then another IoT device k1, which is located closest to ko, is found (S200).

[0104] Set Qm as an empty set, Km = {k0, k1, k2, k3,...}.

[0105] After placing UAVm at device k0, find the IoT device k1 that is closest to UAVm from the set Km of IoT devices assigned to UAVm.

[0107] In the set of IoT devices (200), Km, ko and k1 are removed in the previous step S200, and a new path segment is added to the set of path segments Qm (S300).

[0108] The above step adds new path segments (k0, k1) and (k1, k0) to Qm. Remove k0 and k1 from Km. This corresponds to the first trajectory iteration in Fig. 3.

[0110] UAVm finds another IoT device k2 that is closest to the set of IoT devices (200) excluding k0 and k1 (S400).

[0111] In the above S400, the midpoint is determined by the straight-line distance of the corresponding path segment in Qm, which is the set of path segments. For example, for the path segment (k0, k1), the midpoint corresponds to the straight-line distance between k0 and k1.

[0112] The goal is to find the IoT device with the shortest location compared to the aforementioned midpoint and other IoT devices.

[0113] In other words, by comparing the intermediate points obtained from Qm with all IoT devices in Km, we find k2, which is the intermediate point and IoT device forming the shortest distance.

[0115] When k2 is found in the above S400, the path segments k0 and k1 are excluded from the above Qm, and a new path segment is added (S500).

[0116] If (k0, k1) is assumed to be the path segment closest to the IoT device k2, the path segment (k0, k1) is removed from Qm. At the same time, new path segments (k0, k2) and (k2, k1) are added to Qm. Then, k2 is removed from Km. The above step S500 corresponds to the second trajectory iteration in Fig. 3. That is, when a new path segment is added in S500, k2 is excluded from Km.

[0117] In this way, the steps S100 to S500 are continuously repeated until a movement path is established between all IoT devices belonging to km.

[0118] This is executed repeatedly until all IoT devices (200) of Km are excluded in the same manner as in the above step, so that there are no more IoT devices to assign to the UAV (100). That is, it is executed repeatedly until a movement path is established between all IoT devices (200) belonging to Km.

[0120] Figure 5 is a diagram showing an algorithm representing an initial trajectory for load balancing.

[0121] Below, we will explain the procedure for balancing the load by distributing it.

[0122] The load balancing process causes IoT device k3 (see FIG. 3) to be assigned a new UAVm′ (not shown). When the UAVm enters the transmission range of IoT device k3, the total distance the UAVm will travel when visiting IoT device k3 is calculated.

[0123] If the calculated travel distance of the UAVm is shorter than the travel distance of the UAV', the IoT device k3 is transmitted to the UAVm. This allows the travel distances between the UAVs (100) to be balanced and the total travel distance to be reduced.

[0124] Below, we will explain the trajectory optimization of UAVs.

[0125] ED-SCA (Edge Deployment Successive Convex Approximation) further optimizes the trajectory of each UAV after assigning IoT devices (200) to UAVs (100). The trajectory optimization of ED-SCA aims to maximize the total transmission speed of IoT devices while maintaining the amount of IoT devices (200) assigned to each UAV (100), adopt the initial trajectory of the algorithm of FIG. 2 and the algorithm of FIG. 5, and guarantee a minimum average transmission speed in the IoT devices (200).

[0126] Therefore, the trajectory optimization problem is formulated as shown in Equations 9a and 9b below.

[0127]

[0128] Here, (9b) acts as a relaxation of the integer constraint (7d). To handle the non-convex of (9a), the SCA (Continuous Convex Approximation) technique is applied.

[0129] Specifically, the non-convex objective function is iteratively approximated by an affine function until convergence is guaranteed. The iteration of this process is quantified by the integer variable z. R k,m [n] is non-convex with respect to Xm[n], (Xm[n] - Xk) 2 It can be approximated as convex.

[0130] The first-order Taylor expansion of this can be expressed as follows in [Equation 10], [Equation 11], and [Equation 12].

[0131] [Equation 10]

[0132] [Equation 11]

[0133] [Equation 12]

[0134] Here, [Equation 10] represents the z-th lower bound of the original function.

[0135] ζ of [Equation 11] z k,m and β of [Equation 12] z k,m R in [Equation 10] lb k,m It is the gradient with the z-th value of [n], and g z k,m is the z-th channel gain. In the next (z+1)-th iteration, the previously approximated value is adopted. Therefore, the formulated trajectory optimization problem can be convexly approximated as follows.

[0136]

[0137] P4 of the above equation [13a] can be solved using a convex-optimization method.

[0139] Accordingly, the basic path setting method for a UAV to support a ground user according to one embodiment of the present invention can reduce costs and improve the performance of a UAV network, and ED-SCA (Edge Array - Continuous Convex Approximation) solves the problem by dividing it into two dependent problems: finding the minimized amount of UAVs and optimizing their trajectories.

[0140] The above ED-SCA uses a heuristic method to minimize the amount of necessary UAVs by removing unnecessary UAVs.

[0141] In this way, when the UAV (100) finds the optimal trajectory, the sum of the transmission speeds is maximized and the minimum transmission speed for each IoT device (200) is guaranteed.

[0142] Therefore, the basic path setting method according to one embodiment of the present invention has the advantage of evenly allocating resources to each UAV (100) and balancing the travel distance.

[0144] FIG. 6 is a block diagram illustrating a computing environment (10) including a computing device suitable for use in exemplary embodiments. In the illustrated embodiments, each component may have different functions and capabilities in addition to those described below, and may include additional components in addition to those described below.

[0145] The illustrated computing environment (10) includes a computing device (12). In one embodiment, the computing device (12) may be, for example, a server (not shown) or a terminal (not shown). The computing device (12) may be a device for setting a basic path for a UAV. The computing device (12) may communicate with each UAV (100) and obtain location information of each UAV (100). The computing device (12) may obtain fixed location information of each IoT device (200). Based on this, the computing device (12) may set a basic path for each UAV (100).

[0146] The computing device (12) includes at least one processor (14), a computer-readable storage medium (16), and a communication bus (18). The processor (14) can cause the computing device (12) to operate according to the exemplary embodiment described above. For example, the processor (14) can execute one or more programs stored in the computer-readable storage medium (16). The one or more programs may include one or more computer-executable instructions, and the computer-executable instructions may be configured to cause the computing device (12) to perform operations according to the exemplary embodiment when executed by the processor (14).

[0147] A computer-readable storage medium (16) is configured to store computer-executable instructions or program code, program data and / or other suitable forms of information. A program (20) stored in the computer-readable storage medium (16) includes a set of instructions executable by a processor (14). In one embodiment, the computer-readable storage medium (16) may be memory (volatile memory such as random access memory, non-volatile memory, or a suitable combination thereof), one or more magnetic disk storage devices, optical disk storage devices, flash memory devices, or other forms of storage media that are accessed by a computing device (12) and capable of storing desired information, or a suitable combination thereof.

[0148] The communication bus (18) interconnects various other components of the computing device (12), including the processor (14) and the computer-readable storage medium (16).

[0149] The computing device (12) may also include one or more input / output interfaces (22) and one or more network communication interfaces (26) that provide interfaces for one or more input / output devices (24). The input / output interfaces (22) and network communication interfaces (26) are connected to a communication bus (18). The input / output devices (24) may be connected to other components of the computing device (12) through the input / output interfaces (22). An exemplary input / output device (24) may include an input device such as a pointing device (such as a mouse or trackpad), a keyboard, a touch input device (such as a touchpad or touchscreen), a voice or sound input device, various types of sensor devices and / or imaging devices, and / or an output device such as a display device, a printer, a speaker and / or a network card. An exemplary input / output device (24) may be included inside the computing device (12) as a component constituting the computing device (12), or it may be connected to the computing device (12) as a separate device distinct from the computing device (12).

[0151] Although representative embodiments of the present invention have been described in detail above, those skilled in the art will understand that various modifications can be made to the above-described embodiments without departing from the scope of the present invention. Therefore, the scope of the present invention should not be limited to the described embodiments, but should be defined by the claims set forth below as well as equivalents thereof. Explanation of the symbols

[0155] 100 : UAV 200 : IoT Devices

Claims

Claim 1 A method performed in a computing device comprising one or more processors and a memory storing one or more programs executed by said one or more processors, wherein the computing device communicates with a plurality of UAVs to set a path for the plurality of UAVs to pass through a plurality of IoT devices, wherein UAVm, which is the m-th UAV among the plurality of UAVs, is an arbitrary IoT device k among the plurality of IoT devices. o After placing it at, the above k o A step of finding another IoT device k1 located closest to ; k in Km, which is the total set of the IoT devices o and remove the above k1, and in Qm, which is a set of path segments, the above k o A step of adding a new path segment to the path between and k1; the UAVm and k o and a step of finding another IoT device k2 located closest to the remaining km excluding the above k1; and when the above k2 is found, the above k o A method for setting a basic path of a UAV, characterized by including the step of excluding some path segments from the path between the above k1 and the above Qm, and adding a new path segment including the above k2. Claim 2 A method for establishing a basic path for a UAV according to claim 1, wherein the step of finding k2 is characterized by finding an intermediate point in Qm, which is a set of path segments, by the straight-line distance of the corresponding path segment, and finding the IoT device at the shortest position by comparing the intermediate point with other IoT devices. Claim 3 A method for setting a basic path for a UAV according to claim 1, wherein the method further includes the step of excluding the IoT device k2 from the Km after adding a new path segment including k2. Claim 4 A method for setting a basic path for a UAV, characterized in that, in paragraph 3, all IoT devices of the above Km are excluded and the process is repeated until there are no more IoT devices to be assigned to the UAV. Claim 5 In claim 1, the optimization of the UAVm trajectory, which enables maximizing the minimum transmission speed of the IoT device while maintaining the number of IoT devices assigned to the UAVm, is a method for setting the basic path of a UAV, expressed by the following mathematical formula. (Mathematical formula) : Association variable between the k-th IoT and the m-th UAV in time slot n : Transmission rate between the k-th IoT and the m-th UAV in time slot n : Number of time slots : Number of UAVs Number of IoT devices : Location of UAV or IoT Claim 6 A method for setting a basic path for a UAV according to claim 1, characterized by using the ED-SCA (Edge Deployment Successive Convex Approximation) technique to maximize the total transmission speed of the IoT devices while maintaining the number of IoT devices assigned to each of the UAVs, find the minimum number of UAVs, and optimize the trajectory. Claim 7 A method for setting a basic path for a UAV according to claim 6, characterized in that the ED-SCA uses a heuristic method to minimize unnecessary UAVs by removing them. Claim 8 A computer program stored on a non-transitory computer-readable storage medium, wherein the computer program comprises one or more instructions, and when the instructions are executed by a computing device having one or more processors and communicating with a plurality of UAVs to set a path for the plurality of UAVs to pass through a plurality of IoT devices, the computing device causes UAVm, the m-th UAV among the plurality of UAVs, to be any IoT device k among the plurality of IoT devices. o After placing it at, the above k o A step of finding another IoT device k1 located closest to ; k in Km, which is the total set of the IoT devices o and remove the above k1, and in Qm, which is a set of path segments, the above k o A step of adding a new path segment to the path between and k1; the UAVm and k o and a step of finding another IoT device k2 located closest to the remaining km excluding the above k1; and when the above k2 is found, the above k o A computer program that performs the step of excluding some path segments of the path between and k1 from Qm and adding a new path segment including k2.

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