Control server

Abstract

This control server comprises: a memory that stores at least one instruction; and at least one processor that executes the at least one instruction, wherein the at least one processor may control swarm mobile objects to land in an arbitrary area within a storage facility.

Description

Control server

[0001] The present invention relates to a control server and method, and an analysis device and method.

[0002] More specifically, the present invention relates to a control server and a method capable of controlling the takeoff and landing of a swarm of drones and managing and operating the swarm of drones by reconstructing captured data taken by each of the swarm drones.

[0003] In addition, the present invention relates to an analysis device and method that analyzes captured data taken by each of the swarm moving bodies through a Generative Artificial Intelligence Model (e.g., ChatGPT) and provides it to the user, thereby enabling the user to easily recognize the overall situation of the coordinates being captured by the swarm moving bodies or the situation of a specific area without having to examine each captured data individually.

[0004]

[0005] The content described in this section merely provides background information regarding the present embodiment and does not constitute prior art.

[0006] Recently, drones are being developed for inspecting structures (e.g., wind turbines) or tracking and / or shooting down targets (e.g., illegal drones), and with recent technological advancements, drones have been commercialized across various industrial sectors. As this drone technology develops, there are attempts to incorporate automated methods into drone operations.

[0007] In conventional methods, when managing and operating a drone swarm, each drone is aligned and loaded at a station or storage facility, and takes off and lands at a designated location. In this process, the conventional method requires precise control of the takeoff and landing of each individual drone included in the swarm.

[0008] Meanwhile, when operating drone swarms, numerous drones must transmit their respective camera information to a ground control system (GCS), but there was also a problem with limited bandwidth.

[0009] In particular, if the swarm consists of thousands or tens of thousands of drones rather than just tens of thousands, operating the swarm drones in the conventional way will realistically cause a significant number of problems and inefficiencies.

[0010] Meanwhile, in existing swarm drone operation systems, since a single user (the drone pilot) operates multiple drones simultaneously, there was the inconvenience of the pilot having to individually check the video footage captured by each drone. Additionally, there was a bandwidth burden caused by video data transmitted from multiple drones.

[0011] Accordingly, there is a sufficient need for technology to effectively visualize or post-process the footage captured by each drone during swarm drone operations.

[0012]

[0013] The object of the present invention is to a control server and a method capable of improving operational efficiency for a swarm of drones by efficiently controlling the takeoff and landing of the swarm of drones.

[0014] Another objective of the present invention is to provide a control server and a method that can solve the existing problem of limited bandwidth by receiving and restoring data captured by each individual drone included in a swarm of drones in a vector form, and thereby improve the operational efficiency of the swarm of drones.

[0015] Another objective of the present invention is to provide an analysis device and method that allows a user (pilot) to easily recognize the overall situation of the coordinates being captured by the swarm of moving bodies or the situation of a specific area without having to examine each captured data individually, by analyzing the captured data captured by each swarm of moving bodies through a generative AI model and providing it to the user.

[0016] The objects of the present invention are not limited to those mentioned above, and other unmentioned objects and advantages of the present invention may be understood from the following description and will be more clearly understood by the embodiments of the present invention. Furthermore, it will be readily apparent that the objects and advantages of the present invention can be realized by the means and combinations thereof set forth in the claims.

[0017]

[0018] A control server managing a cluster of mobile bodies including a plurality of mobile bodies according to some embodiments of the present invention and a storage facility for storing said cluster of mobile bodies includes a memory for storing at least one instruction and at least one processor for executing said at least one instruction, wherein the processor can control said cluster of mobile bodies to land in any area within said storage facility.

[0019] In addition, the processor can control the takeoff of the swarm of mobiles by transmitting a takeoff control signal to the swarm of mobiles.

[0020] In addition, the processor may provide the takeoff control signal to a plurality of vehicles included in the swarm of vehicles simultaneously, or provide the takeoff control signal to each of the plurality of vehicles included in the swarm of vehicles at different times.

[0021] Additionally, when the processor simultaneously provides the takeoff control signal to a plurality of vehicles included in the swarm of vehicles, at a first time point, it provides a first takeoff control signal to the plurality of vehicles, and at a second time point which is later in time series than the first time point, it determines a takeoff parameter for each of the plurality of vehicles, determines a vehicle ready for takeoff based on the takeoff parameter, and provides a second takeoff control signal to the determined vehicle ready for takeoff.

[0022] In addition, the thrust provided to the moving body by the second takeoff control signal may have a larger value compared to the thrust provided to the moving body by the first takeoff control signal.

[0023] Additionally, the above-mentioned takeoff parameters include position information of the mobile body, and the processor can determine that the mobile body whose position information differs from the location of the storage facility by more than a predefined threshold is the ready mobile body.

[0024] In addition, the above-mentioned takeoff parameters include acceleration information of the moving body, and the processor can determine that the moving body whose acceleration information is greater than or equal to a predefined threshold is the ready moving body.

[0025] In addition, when the processor provides the takeoff control signal to each of the plurality of mobile bodies included in the swarm of mobile bodies at different times, it may prioritize providing the takeoff control signal to the mobile body among the swarm of mobile bodies that has a smaller distance from the door of the storage facility.

[0026] In addition, when the processor provides the takeoff control signal at different times for each of the plurality of vehicles included in the swarm of vehicles, it may prioritize providing the takeoff control signal to the vehicle among the swarm of vehicles that landed at the storage facility later in priority.

[0027] Additionally, the processor can control the swarm of mobiles to land in the storage without being pre-aligned to predefined coordinates within the storage.

[0028] In addition, the processor may close the door of the storage facility according to the recovery rate of the swarm of mobiles when controlling the landing of the swarm of mobiles.

[0029] In addition, the processor can determine the recovery rate of the swarm of mobile bodies based on at least one of the location information of each of the plurality of mobile bodies, communication information with the plurality of mobile bodies, and weight information of the storage facility.

[0030] In addition, the processor can generate reconstruction data for the captured data based on the captured data captured by the swarm of moving bodies through the camera.

[0031] Additionally, each of the plurality of moving bodies includes an encoder that encodes the captured data into a vector, and the processor includes a decoder that generates the reconstructed data by decoding a vector corresponding to each of the plurality of moving bodies and restoring the captured data, wherein the encoder and the decoder may be components included in a transformer network.

[0032] In addition, the decoder can restore the captured data to its original form or restore it to a post-processed form.

[0033] An analysis device for analyzing image data captured by a plurality of mobile bodies according to some embodiments of the present invention comprises a memory for storing at least one instruction and at least one processor for executing said at least one instruction, wherein the processor acquires the image data captured by at least one of the plurality of mobile bodies and generates analysis information, which is data obtained by analyzing the image data, by inputting the image data into a pre-trained analysis model, wherein the analysis model may include a Generative Artificial Intelligence Model that is pre-trained to output the analysis information when the image data is input.

[0034] Additionally, the analysis device is positioned in a control center that controls the plurality of mobile bodies, and the control center may include at least one of a server that communicates with the plurality of mobile bodies, a controller that controls the movement of the plurality of mobile bodies, and a station that stores and stores the plurality of mobile bodies.

[0035] Additionally, the plurality of moving bodies includes a first moving body to an nth moving body, and the shooting data includes a first shooting data to an nth shooting data, wherein n is a natural number greater than or equal to 2, and the processor can generate the analysis information by receiving the first shooting data to the nth shooting data from each of the first moving body to the nth moving body and inputting the first shooting data to the nth shooting data into the analysis model.

[0036] In addition, the analysis device may be placed in at least one of the plurality of moving bodies.

[0037] Additionally, the plurality of moving bodies includes a first moving body to an nth moving body, and the shooting data includes a first shooting data to an nth shooting data. The processor generates first analysis information to nth analysis information for each of the first shooting data to nth shooting data through the analysis model, wherein n is a natural number greater than or equal to 2. The analysis device includes a first analysis device to an nth analysis device disposed in each of the first moving body to the nth moving body. The first analysis device to the nth analysis device generates the first analysis information to the nth analysis information based on each of the first shooting data to the nth shooting data, and can transmit the first analysis information to the nth analysis information to a control center that controls the plurality of moving bodies.

[0038] In addition, the plurality of mobile bodies may include a reference mobile body that communicates with a control center controlling the plurality of mobile bodies and transmits the analysis information to the control center.

[0039] In addition, the reference moving body may be determined through at least one criterion among a random selection among the plurality of moving bodies, a distance from a control center controlling the plurality of moving bodies, a positional relationship between the plurality of moving bodies, and the use of the moving bodies.

[0040] Additionally, the analysis device includes a reference analysis device positioned on the reference mobile body, and the processor of the reference analysis device receives the shooting data from other mobile bodies among the plurality of mobile bodies excluding the reference mobile body, generates the analysis information by inputting the shooting data of the reference mobile body and the shooting data of the other mobile bodies, respectively, into the analysis model, and transmits the analysis information to the control center.

[0041] Additionally, the analysis device may include a reference analysis device placed on the reference moving body and other analysis devices placed on other moving bodies excluding the reference moving body.

[0042] Additionally, each of the above other analysis devices generates each of the above analysis information based on each of the above shooting data, and the processor of the above reference analysis device generates the above analysis information based on the shooting data captured from the above reference analysis device, and can transmit to the control center information including the above analysis information received from the above other analysis devices and at least a portion of the above analysis information generated by the above reference analysis device.

[0043] Additionally, the above-mentioned other analysis device includes a first other analysis device and a second other analysis device, and the processor of the first other analysis device generates the analysis information based on the captured data taken from the first other analysis device and transmits it to the second other analysis device, and the processor of the second other analysis device generates the analysis information based on the captured data taken from the second other analysis device and transmits the analysis information generated from the first other analysis device and the analysis information generated from the second other analysis device to the reference analysis device, and the processor of the reference analysis device generates the analysis information based on the captured data taken from the reference analysis device and transmits information including at least a portion of the analysis information received from the first other analysis device and the second other analysis device and the analysis information generated by the reference analysis device to the control center.

[0044] In addition, the analysis model may be pre-trained to generate at least one of the following as analysis information: situation information analyzing the situation of the shooting data, control information controlling the behavior of the moving object according to the analyzed situation, and guide information guiding the behavior of the user of the analysis device according to the analyzed situation.

[0045] Additionally, the plurality of moving bodies may capture different coordinate regions to generate the captured data for the plurality of coordinate regions, and the processor may receive a selection of at least one of the plurality of coordinate regions or at least one of the plurality of moving bodies from the user of the analysis device, and output the analysis information regarding the captured data corresponding to the selected result to the user.

[0046] In addition, the analysis model can combine the results of analyzing a plurality of the aforementioned shooting data and, based on the combined results, generate a single reconstruction data for a coordinate area corresponding to the plurality of the aforementioned shooting data as the analysis information.

[0047] Additionally, the other moving body includes a first moving body and a second moving body, the first moving body transmits first shooting data captured from the first moving body to the second moving body, the second moving body transmits the first shooting data received from the first moving body and the second shooting data captured from the second moving body to the reference analysis device, and the processor of the reference analysis device generates the analysis information by inputting the shooting data of the reference moving body and the first shooting data and the second shooting data received from the second moving body, respectively, into the analysis model, and can transmit the analysis information to the control center.

[0048] An analysis method performed by an analysis device that analyzes image data captured by a plurality of moving bodies according to some embodiments of the present invention comprises the steps of acquiring image data captured by at least one of the plurality of moving bodies and generating analysis information, which is data obtained by analyzing the image data, by inputting the image data into a pre-trained analysis model, wherein the analysis model may include a Generative Artificial Intelligence Model that is pre-trained to output the analysis information when the image data is input.

[0049]

[0050] A control server and method according to some embodiments of the present invention can improve operational efficiency for a swarm of drones by controlling the takeoff of the swarm of drones. More specifically, a control server and method according to some embodiments of the present invention can increase the takeoff speed of the swarm of drones and reduce the resource consumption required for takeoff by setting a sequence to take off all swarm of drones densely packed in a storage area.

[0051] In addition, the control server and method according to some embodiments of the present invention can improve operational efficiency for swarm drones by controlling the landing of swarm drones. More specifically, the control server and method according to some embodiments of the present invention can achieve automation of swarm drone management by setting a predetermined condition for closing the door of a storage facility, and thereby improve the efficiency of drone management.

[0052] In addition, the control server and method according to some embodiments of the present invention can solve the existing problem of limited bandwidth by receiving data captured by each individual drone included in a swarm of drones as compressed data in a vector form and then restoring it using high-performance resources in the control server.

[0053] The analysis device and method according to some embodiments of the present invention can improve the operational efficiency of a swarm of moving bodies. That is, the analysis device and method according to some embodiments of the present invention have a novel effect of enabling a user (pilot) to easily recognize the overall situation of the coordinates being captured by the swarm of moving bodies or the situation of a specific area without having to examine each captured data individually, by analyzing the captured data taken by each swarm of moving bodies through a generative AI model and providing it to the user.

[0054] In addition, the analysis device and method according to some embodiments of the present invention can not only analyze the situation but also automatically control the behavior of a moving object according to the analyzed situation or automatically provide necessary behavioral guidelines to the user.

[0055] In addition, the analysis device and method according to some embodiments of the present invention can optimize bandwidth usage by providing analysis information about the captured data to the user instead of the captured data captured by the mobile body, and in particular, can improve communication efficiency through an information collection method using a reference drone.

[0056] In addition, the analysis device and method according to some embodiments of the present invention can be used for security missions (e.g., identification of armed intruders), fire missions, etc., and thus have high potential for practical application.

[0057] In addition to the above, specific effects according to some embodiments of the present invention are described together with the following explanation of specific details for implementing the invention.

[0058]

[0059] FIG. 1 is a drawing for illustrating a control system according to some embodiments of the present invention.

[0060] FIG. 2 is a diagram illustrating the relationship between a mobile body, a controller, a server, and a station according to some embodiments of the present invention.

[0061] FIG. 3 is a block diagram of a moving body according to some embodiments of the present invention.

[0062] FIG. 4 is a drawing for explaining a guard formed on the outside of a moving body according to some embodiments of the present invention.

[0063] FIG. 5 is a drawing for illustrating a storage facility according to some embodiments of the present invention.

[0064] FIG. 6 illustrates a storage unit with a movable body embedded therein according to some embodiments of the present invention.

[0065] FIG. 7 is a block diagram of a control server according to some embodiments of the present invention.

[0066] FIG. 8 is a diagram illustrating the process of a control server controlling the takeoff of a mobile body according to some embodiments of the present invention.

[0067] FIG. 9 is a diagram illustrating the process of a control server controlling the landing of a mobile body according to some embodiments of the present invention.

[0068] FIG. 10 is a diagram illustrating the process of a control server according to some embodiment of the present invention reconstructing shooting data generated by each of a plurality of moving bodies.

[0069] FIG. 11 is a block diagram of an analysis device according to some embodiments of the present invention.

[0070] FIG. 12 is a flowchart relating to the operation of an analysis device according to some embodiments of the present invention.

[0071] FIG. 13 is a diagram illustrating how a swarm of moving bodies generates image data according to some embodiments of the present invention.

[0072] FIG. 14 is a diagram illustrating how a processor according to some embodiment of the present invention generates analysis information from captured data through an analysis model.

[0073] FIG. 15 is a diagram illustrating the neural network structure of an analysis model according to some embodiments of the present invention.

[0074] FIG. 16 illustrates an example of analysis information according to some embodiments of the present invention.

[0075] FIG. 17 illustrates another example of analysis information according to some embodiments of the present invention.

[0076] FIG. 18 is a drawing for explaining a first embodiment regarding the arrangement of an analysis device according to some embodiments of the present invention.

[0077] FIG. 19 is a drawing for explaining a second embodiment regarding the arrangement of an analysis device according to some embodiments of the present invention.

[0078] FIG. 20 is a drawing for explaining a third embodiment regarding the arrangement of an analysis device according to some embodiments of the present invention.

[0079] FIG. 21 is a drawing for explaining a fourth embodiment regarding the arrangement of an analysis device according to some embodiments of the present invention.

[0080] FIG. 22 is a drawing for explaining a fifth embodiment regarding the arrangement of an analysis device according to some embodiments of the present invention.

[0081] FIG. 23 is a drawing for explaining a sixth embodiment regarding the arrangement of an analysis device according to some embodiments of the present invention.

[0082]

[0083] Terms and words used in this specification and claims shall not be interpreted as being limited to their general or dictionary meanings. In accordance with the principle that an inventor may define the concept of a term or word to best describe their invention, they shall be interpreted in a meaning and concept consistent with the technical spirit of the invention. Furthermore, since the embodiments described in this specification and the configurations illustrated in the drawings are merely one embodiment of the invention and do not represent the entire technical spirit of the invention, it should be understood that various equivalents, modifications, and applicable examples capable of replacing them may exist at the time of filing this application.

[0084] The terms first, second, A, B, etc., as used in this specification and claims may be used to describe various components, but said components should not be limited by said terms. These terms are used solely for the purpose of distinguishing one component from another. For example, without departing from the scope of the present invention, the first component may be named the second component, and similarly, the second component may be named the first component.

[0085] The terms used in this specification and claims are used merely to describe specific embodiments and are not intended to limit the invention. The singular expression includes the plural expression unless the context clearly indicates otherwise. In this application, terms such as "comprising" or "having" should be understood as not precluding the existence or addition of the features, numbers, steps, actions, components, parts, or combinations thereof described in the specification.

[0086] Unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as generally understood by those skilled in the art to which this invention pertains.

[0087] Terms such as those defined in commonly used dictionaries should be interpreted as having meanings consistent with their meanings in the context of the relevant technology, and should not be interpreted in an ideal or overly formal sense unless explicitly defined in this application.

[0088] In addition, each component, process, procedure, or method included in each embodiment of the present invention may be shared within a scope that is not technically contradictory to one another.

[0089] Hereinafter, with reference to FIGS. 1 to FIGS. 10, we will examine a control server and a method according to some embodiments of the present invention.

[0090]

[0091] FIG. 1 is a drawing for illustrating a control system according to some embodiments of the present invention.

[0092] Referring to FIG. 1, a control system (1) according to some embodiments of the present invention may include a mobile body (10), a storage facility (20), and a control server (hereinafter referred to as “CS”). However, the embodiments of the present invention are not limited thereto, and it is obvious that other configurations not shown in FIG. 1 may be included in the control system (1), that any one of the mobile body (10), the storage facility (20), and the control server (CS) may be omitted, or that some configurations among the mobile body (10), the storage facility (20), and the control server (CS) may be integrated.

[0093] The moving body (10) may include an object capable of moving along the ground, sea, air, etc.

[0094] For example, the mobile body (10) may include an aircraft. In this case, the aircraft may include any flying vehicle, such as a drone, UAV (Unmanned Aerial Vehicle), UAM (Unmanned Aerial Mobility), airplane, or helicopter. For convenience of explanation, the following description will assume that the mobile body (10) is an aircraft.

[0095] The mobile body (10) may fly alone or together with multiple mobile bodies capable of collaboration. In other words, the mobile body (10) may include multiple mobile bodies, and these multiple mobile bodies may form a swarm of mobile bodies. Additionally, the mobile body (10) may also collaborate with other types of devices, such as vehicles and robots.

[0096] Hereinafter, a moving body (10) according to some embodiments of the present invention will be described in more detail with reference to FIGS. 2 to 4.

[0097]

[0098] FIG. 2 is a diagram illustrating the relationship between a mobile body, a controller, a server, and a station according to some embodiments of the present invention.

[0099] Referring to FIGS. 1 and FIGS. 2, the mobile body (10) can be connected to a server (30), a controller (40), and a station (50).

[0100] The server (30), controller (40), and station (50) can control the mobile body (10) independently or in combination. For example, the server (30), controller (40), and station (50) can control the operation (e.g., movement, rotation, etc.) of the mobile body (10) or control the filming of the mobile body (10).

[0101] The moving body (10) includes at least one camera and can photograph a target (TG) using the camera. For example, the camera may be installed in a position advantageous for photography during the flight of the moving body (10) (e.g., an area at the front or lower front of the moving body (10) that is not obscured by a propeller, etc.). Meanwhile, the moving body (10) may fly using a Global Navigation Satellite System (GNSS) and / or an Inertial Navigation System (INS).

[0102] The mobile body (10) can transmit and receive data with the server (30), the controller (40), and / or the station (50). Additionally, the controller (40) and the server (30), the server (30) and the station (50), and the station (50) and the controller (40) can transmit and receive data to and from each other.

[0103] Here, the data may include data necessary for controlling the flight of the moving body (10), data regarding the flight video of the moving body (10), and data regarding the video of the moving body (10) capturing an object, target, etc. The flight video represents the field of view of the moving body (10) when the moving body (10) is flying. For example, the flight video may be a dynamic image acquired in real time, but is not limited thereto. The video of the moving body (10) capturing an object, target, etc. may include an image captured while the moving body (10) is flying around an object, target, etc.

[0104] The user can control the mobile body (10) using the interface of the controller (40). For example, the controller (40) can generate a control signal based on user input received through the interface and transmit the control signal to the mobile body (10). The controller (40) can transmit the control signal to the mobile body (10) using a wireless communication method. The control signal may be a signal that controls the flight, attitude, navigation, etc., of the mobile body (10).

[0105] The moving body (10) can rotate the propeller by controlling the motor according to a control signal received from the controller (40). The moving body (10) can perform movement, rotation, etc. by changing the speed and / or attitude, etc. by the rotation of the propeller. Here, the attitude of the moving body (10) can be represented as pitch (Y), roll (X), yaw (Z), etc. Additionally, the moving body (10) can perform the shooting of a target (TG), etc. according to a control signal received from the controller (40).

[0106] The controller (40) may further include a display device, and the user can view flight footage of the moving body (10) and / or captured images of the target (TG) through the display device.

[0107] The controller (40) may be a device with an application installed to control the mobile body (10). For example, the device with the application installed may be a various portable device such as a smartphone, tablet, smart pad, laptop, or wearable device.

[0108] The server (30) or station (50) can control the mobile body (10) by directly transmitting a control signal to the mobile body (10). Additionally, the mobile body (10) can transmit flight footage and / or captured images of the target (TG), etc., to the server (30), controller (40), and station (50).

[0109] Meanwhile, the storage facility (20) in FIG. 1 may be a configuration corresponding to the station (50) in FIG. 2. In other words, the storage facility (20) may be a configuration included in the station (50), or the station (50) may be a configuration included in the storage facility (20).

[0110] Additionally, the control server (CS) in FIG. 1 may be a configuration corresponding to the server (30) in FIG. 2. In other words, the control server (CS) may be a configuration included in the server (30), or the server (30) may be a configuration included in the control server (CS).

[0111]

[0112] FIG. 3 is a block diagram of a moving body according to some embodiments of the present invention.

[0113] Referring to FIGS. 1 and FIGS. 3, the moving body (10) may include a sensor (110), a camera (120), a memory (130), a driving device (140), a communication device (150), and a processor (160). However, the components of the moving body (10) are not limited to those shown in FIGS. 3. In other words, the moving body (10) may include at least one additional component in addition to the components shown in FIGS. 3, or at least one of the components shown in FIGS. 3 may be excluded.

[0114] The sensor (110) detects various information necessary for the operation of the moving body (10), such as the moving body (10) itself, the surrounding environment of the moving body (10), identification of the target (TG), and verification of the distance between the moving body (10) and the target (TG), etc. The sensor (110) may include, but is not limited to, a gyro sensor, a barometer, an ultrasonic sensor, a magnetic sensor, an acceleration sensor, a proximity sensor, a lidar, a radar and / or a position sensor (e.g., a GPS sensor, etc.).

[0115] For example, a gyroscope sensor and / or an accelerometer sensor can measure the three-axis angular velocity of the moving body (10). A barometer sensor can measure changes in atmospheric pressure and / or atmospheric pressure around the moving body (10). An ultrasonic sensor can measure the distance between the moving body (10) and the ground or the target (TG). A magnetic sensor is a type of terrestrial magnetism sensor (compass sensor) and can detect geomagnetic information. A proximity sensor can measure the proximity of the target (TG) to the moving body (10) and the distance between the moving body (10) and the target (TG), and may include an ultrasonic sensor capable of measuring the distance to the target (TG) from a signal reflected from the target (TG) by outputting ultrasonic waves. A GPS sensor can calculate the current coordinates (x, y, z) of the moving body (10) using GPS signals.

[0116] The sensor (110) may include an attitude and heading reference system (AHSR). For example, the attitude and heading reference system may include an inertial sensor or an inertial measurement unit (IMU). For example, the attitude and heading reference system may include a gyroscope, an accelerometer, and a magnetic sensor, and fuse the sensor values ​​to obtain the attitude value ( It can output , θ, ψ). Here, the attitude value( , θ, ψ) can be an angle based on 3D coordinates (x-axis coordinate, y-axis coordinate, z-axis coordinate) according to GPS coordinates.

[0117] The camera (120) can generate shooting data by shooting an object, target, etc., according to the instructions of the processor (160). For example, the moving body (10) may include at least one camera, and may include a low-resolution camera and / or a high-resolution camera. The camera (120) may be combined with a gimbal capable of adjusting the angle. Accordingly, the shooting angle of the camera (120) can be adjusted by the gimbal.

[0118] The memory (130) may include any non-transient computer-readable recording medium. As an example, the memory (130) may include a permanent mass storage device such as random access memory (RAM), read-only memory (ROM), a disk drive, a solid state drive (SSD), or flash memory. As another example, a permanent mass storage device such as ROM, an SSD, flash memory, or a disk drive may be a separate permanent storage device distinct from the memory. Additionally, the memory (130) may store an operating system (OS) and at least one program code.

[0119] These software components may be loaded from a computer-readable recording medium separate from the memory (130). This separate computer-readable recording medium may be a recording medium that can be directly connected to a computer and may include, for example, computer-readable recording media such as a floppy drive, disk, tape, DVD / CD-ROM drive, or memory card. Alternatively, the software components may be loaded into the memory (130) via a communication device (150) that is not a computer-readable recording medium. For example, at least one program may be loaded into the memory (130) based on a computer program installed by files provided through the communication device (150) by developers or a file distribution system that distributes installation files for applications.

[0120] The memory (130) can store commands, information, and / or data related to the operation of each component included in the mobile body (10). For example, the memory (130) may store various algorithms or models that can be used when the mobile body (10) performs flight, inspection, etc.

[0121] The driving device (140) controls the driving of the motor at a speed and direction according to the instructions of the processor (160), and accordingly, the rotational speed and direction of a propeller, etc. connected to the motor can be controlled. For example, the driving device (140) may include a motor and a propeller.

[0122] The communication device (150) performs data communication between the mobile body (10) and an external device. For example, the communication device (150) can communicate with the controller (40), server (30) and / or station (50) using various communication methods such as infrared communication, RF (Radio Frequency) communication, Wi-Fi communication, ZigBee communication, Bluetooth communication, laser communication, UWB (Ultra-Wideband) communication, LTE, 5G, 6G, and Wireless LAN. However, the communication methods employed by the communication device (150) are not limited to those described above.

[0123] For example, the communication device (150) can communicate with at least one of the storage facility (20) and the control server (CS) in FIG. 1.

[0124] The processor (160) can process instructions of a computer program by performing basic arithmetic, logic, and input / output operations. Here, instructions may be provided from memory (130) or external devices (e.g., storage (20), control server (CS), server (30), controller (40), station (50), etc.). Additionally, the processor (160) can control the overall operation of other components included in the mobile body (10).

[0125] At this time, the functions performed by each module included in the processor (160) may be performed by a single processor or by each separate processor. The processor (160) may perform operations or data processing regarding the control and / or communication of at least one other component of the mobile body (10). Additionally, the processor (160) may be implemented as an array of multiple logic gates, or as a combination of a general-purpose microprocessor and a memory storing a program that can be executed on the microprocessor. For example, the processor (160) may include a general-purpose processor, a central processing unit (CPU), a microprocessor, a digital signal processor (DSP), a controller, a microcontroller, a state machine, etc. In some environments, the processor (160) may include an application-specific integrated circuit (ASIC), a programmable logic device (PLD), a field programmable gate array (FPGA), etc. For example, the processor (160) may refer to a combination of processing devices such as a combination of a digital signal processor (DSP) and a microprocessor, a combination of multiple microprocessors, a combination of one or more microprocessors combined with a digital signal processor (DSP) core, or any other combination of such configurations.

[0126]

[0127] FIG. 4 is a drawing for explaining a guard formed on the outside of a moving body according to some embodiments of the present invention.

[0128] Referring to FIGS. 1 and FIGS. 4, a guard (hereinafter referred to as “G”) may be formed on the outside of a moving body (10) according to some embodiments of the present invention. In other words, the guard (G) may be placed on the outside of the moving body (10).

[0129] The guard (G) can perform the role of protecting the moving body (10). In other words, the guard (G) is positioned outside the moving body (10) to protect the moving body (10) from external physical impacts, etc.

[0130] At this time, the guard (G) may have a predefined shape. For example, the guard (G) may include a polygonal shape having a predefined number of nodes and edges as shown in FIG. 4, but the embodiments of the present invention are not limited thereto, and the guard (G) may be formed in other shapes (e.g., circular, spherical).

[0131] Meanwhile, the mobile body (10) can be stored, take off, fly, perform missions, and land with the guard (G) deployed.

[0132]

[0133] Referring again to FIG. 1, the storage facility (20) can store and manage the mobile body (10). At this time, as described above, the mobile body (10) may be a cluster of mobile bodies including a plurality of mobile bodies, and the storage facility (20) can store, store, and manage the cluster of mobile bodies.

[0134] In some examples, the storage facility (20) may have a shape capable of containing the mobile body (10). For example, the storage facility (20) may have a shape in which the mobile body (10) can be stored, but embodiments of the present invention are not limited thereto.

[0135] Hereinafter, a storage facility (20) according to some embodiments of the present invention will be described in detail with reference to FIGS. 5 and FIGS. 6.

[0136]

[0137] FIG. 5 is a drawing for explaining a storage facility according to some embodiment of the present invention. FIG. 6 illustrates a storage facility according to some embodiment of the present invention with a movable body embedded therein. More specifically, FIG. 5 illustrates a state in which the door (Door, hereinafter referred to as “D”) of the storage facility (20) is closed, and FIG. 6 illustrates a state in which the door (D) of the storage facility (20) is open.

[0138] Referring to FIGS. 1, FIGS. 5 and FIGS. 6, a storage facility (20) according to some embodiments of the present invention is a device for storing, storing, and managing a mobile body (10).

[0139] In some examples, the storage unit (20) may have a shape capable of containing the mobile body (10).

[0140] For example, the storage facility (20) may have the form of a container in which a cluster of mobile bodies (10) including a plurality of mobile bodies (10) can be stored, as illustrated in FIGS. 5 and 6, but the embodiments of the present invention are not limited thereto. In this case, the storage facility (20) in the form of a container may include a door (D) capable of opening and closing.

[0141] In FIGS. 5 and 6, for convenience of explanation, a container, which is an example of a storage container (20), is shown to have the shape of a rectangular prism, but the embodiments of the present invention are not limited thereto, and it is obvious that the container may have other shapes (e.g., cube, sphere, frustum, etc.).

[0142] Meanwhile, such a storage facility (20) may include other configurations for the management and control of a mobile body (10). For example, the storage facility (20) may include a sensor, a communication unit, etc. The sensor may sense location information regarding the location of the storage facility (20) (e.g., GPS information), weight information regarding the weight of a mobile body (10) stored in the storage facility (20), etc. The communication unit may transmit the location information, weight information, and / or door opening / closing information regarding whether the door (D) is open or closed, etc., sensed by the sensor, to a control server (CS).

[0143]

[0144] Referring again to FIG. 1, the control server (CS) can manage the mobile body (10) and the storage (20). At this time, as described above, the mobile body (10) may include a group of mobile bodies, and in this case, the control server (CS) can manage and control the group of mobile bodies and the storage (20).

[0145] Hereinafter, a control server (CS) according to some embodiments of the present invention will be described with reference to FIG. 7.

[0146]

[0147] FIG. 7 is a block diagram of a control server according to some embodiments of the present invention.

[0148] Referring to FIGS. 1 and FIGS. 7, the control server (CS) may include a memory (hereinafter referred to as “M”) and a processor (hereinafter referred to as “P”). However, the embodiments of the present invention are not limited thereto, and the control server (CS) may additionally include a communication unit capable of communicating with a mobile body (10), a storage facility (20), etc. For convenience of explanation, the following description will assume a case where the processor (P) performs wired or wireless communication with a mobile body (10), a storage facility (20), etc.

[0149] Memory (M) may include any non-transient computer-readable recording medium. As an example, memory (M) may include a permanent mass storage device such as random access memory (RAM), read-only memory (ROM), a disk drive, a solid state drive (SSD), or flash memory. As another example, a permanent mass storage device such as ROM, an SSD, flash memory, or a disk drive may be a separate permanent storage device distinct from memory. Additionally, an operating system (OS) and at least one program code may be stored in memory (M).

[0150] These software components may be loaded from a computer-readable recording medium separate from memory (M). This separate computer-readable recording medium may be a recording medium that can be directly connected to a computer and may include, for example, computer-readable recording media such as a floppy drive, disk, tape, DVD / CD-ROM drive, or memory card. Alternatively, the software components may be loaded into memory (M) via a communication device (150) that is not a computer-readable recording medium. For example, at least one program may be loaded into memory (M) based on a computer program installed by files provided through the communication device (150) by developers or a file distribution system that distributes installation files for applications.

[0151] Memory (M) can store commands, information, and / or data related to the operation of each component within the control system (1), such as the mobile body (10) and the storage (20). For example, memory (M) can store instructions that enable the processor (P) to perform various operations described in this document during execution. For another example, memory (M) can store various algorithms or models that can be used when the processor (P) performs control, management, etc., regarding the swarm of mobile bodies.

[0152] The processor (P) can process instructions of a computer program by performing basic arithmetic, logic, and input / output operations. Here, instructions may be provided from memory (M) or external devices (e.g., server (30), controller (40), station (50), etc.). Instructions may also be referred to by the aforementioned name 'Instruction'. At this time, the processor (P) may be operatively connected to memory (M) to perform overall functions regarding the management and control of the swarm of moving bodies. Additionally, the processor (P) can generally control the operation of other components included in the control server (CS) not shown in FIG. 7.

[0153] At this time, the functions performed by each module included in the processor (P) may be performed by a single processor or by separate processors. The processor (P) may execute operations or data processing regarding the control and / or communication of at least one other component of the control server (CS). Additionally, the processor (P) may be implemented as an array of multiple logic gates, or as a combination of a general-purpose microprocessor and memory storing a program that can be executed on the microprocessor. For example, the processor (P) may include a general-purpose processor, a central processing unit (CPU), a microprocessor, a digital signal processor (DSP), a controller, a microcontroller, a state machine, etc. In some environments, the processor (P) may include an application-specific integrated circuit (ASIC), a programmable logic device (PLD), a field programmable gate array (FPGA), etc. For example, the processor (P) may refer to a combination of processing devices such as a combination of a digital signal processor (DSP) and a microprocessor, a combination of multiple microprocessors, a combination of one or more microprocessors combined with a digital signal processor (DSP) core, or any other combination of such configurations.

[0154] The processor (P) can manage and control the mobiles (10) and the storage (20) for smooth management and operation of the group of mobiles of the control system (1).

[0155] In some examples, the processor (P) can control the takeoff, landing, etc. of the storage (20) of the mobile body (10). In this process, the processor (P) can additionally control the opening and closing of the door (D in FIG. 5 and 6) of the storage (20). At this time, the processor (P) can receive information from the storage (20) regarding whether the door (D in FIG. 5 and 6) is open or closed.

[0156] For example, the processor (P) may not determine the alignment position of each vehicle (10) included in the swarm of vehicles within the storage (20). In other words, the processor (P) may not determine the alignment position of each vehicle (10) included in the swarm of vehicles within the internal space of the storage (20). That is, when controlling the takeoff and landing of the vehicles (10), the processor (P) may control the vehicles to take off from the storage (20) or land in the storage (20) without being pre-aligned to predefined coordinates within the storage (20). For example, the processor (P) may only instruct each vehicle (10) to land within the storage (20) (i.e., any area within the storage (20)), and may not instruct it to land at a specific location within the storage (20). Accordingly, the mobile body (10) can take off from the storage (20) or land at the storage (20) at a random location inside the storage (20), that is, at any location.

[0157] That is, in the case of the conventional method, the mobile body (10) adopts a method of being aligned to predefined coordinates (set coordinates) in a storage facility (20), launcher, station, etc. However, when the number of mobile bodies (10) is expanded to thousands or tens of thousands to form a swarm of mobile bodies, the method of individually aligning the mobile bodies (10) included in such a swarm of mobile bodies to the set coordinates causes significant inefficiency and practical impossibility. Accordingly, the processor (P) of the present invention can ensure efficiency in the take-off and landing control process for the swarm of mobile bodies by controlling the mobile body (10) to take off without being aligned to the set coordinates or by controlling the mobile body (10) to land without being aligned to the set coordinates.

[0158] Hereinafter, with reference to FIGS. 8 and FIGS. 9, the process of the processor (P) controlling the take-off and landing of the mobile body (10) will be explained in more detail.

[0159]

[0160] FIG. 8 is a diagram illustrating the process of a control server controlling the takeoff of a mobile body according to some embodiments of the present invention.

[0161] Referring to FIGS. 1, 7 and 8, a processor (P) according to some embodiments of the present invention can control a mobile body (10) to take off from a storage facility (20).

[0162] At this time, as described above, the mobile body (10) stored in the storage (20) may not be aligned to the set coordinates. That is, a processor (P) according to some embodiments of the present invention can control the takeoff of a cluster of mobile bodies that are not pre-aligned to the pre-defined set coordinates within the storage (20).

[0163] To this end, the processor (P) may transmit a control signal to the storage (20) to open the door (D) of the storage (20), but embodiments of the present invention are not limited thereto.

[0164] Next, the processor (P) may transmit a takeoff control signal to the mobile body (10) to take off from the storage (20). At this time, the takeoff control signal may include a signal that provides thrust to the mobile body (10) or allows the mobile body (10) to generate thrust. At this time, the processor (P) may transmit a takeoff control signal to each of the multiple mobile bodies (10) included in the swarm of mobile bodies.

[0165] In some examples, the processor (P) can control the swarm of mobiles to take off simultaneously. In other words, the processor (P) can simultaneously transmit a take-off control signal to each of the multiple mobiles (10) included in the swarm of mobiles.

[0166] For example, the processor (P) can transmit a first takeoff control signal to all of the plurality of mobile bodies (10) included in the swarm of mobile bodies at a first time point, and then at a second time point which is later in time series than the first time point, determine the takeoff parameters for each of the plurality of mobile bodies (10), determine the ready mobile bodies that are ready for takeoff based on the determined takeoff parameters, and transmit a second takeoff control signal to the determined ready mobile bodies.

[0167] At this time, the thrust provided by the second takeoff control signal to the moving body (10) may have a larger value than the thrust provided by the first takeoff control signal to the moving body (10). In other words, the second takeoff control signal may be a takeoff control signal that generates a stronger thrust than the first takeoff control signal.

[0168] The takeoff parameters may include sensing data for the mobile body (10) sensed during the takeoff process of the mobile body (10).

[0169] For example, the takeoff parameters may include location information (e.g., GPS information) of the moving body (10) sensed by the sensor (110 in FIG. 3) of the moving body (10). At this time, the processor (P) may compare the location information of the moving body (10) received from each moving body (10) with the location information of the storage facility (20) received from the storage facility (20), and determine a ready moving body based on the comparison result. For example, the processor (P) may determine a moving body (10) that has location information that differs from the location information of the storage facility (20) by more than a predefined threshold as a ready moving body.

[0170] That is, if the mobile bodies (10) are crowded and entangled inside the storage facility (20), if all the mobile bodies (10) attempt to take off with strong thrust, the interference between the mobile bodies (10) becomes severe. Therefore, the processor (P) first determines the minute movement status of each of the multiple mobile bodies (10) by applying weak thrust (transmitting the first takeoff control signal), and then determines the mobile body (10) that has exited the storage facility (20) and has a large difference in location information from the storage facility (20) as the mobile body ready for takeoff, and controls the takeoff of the entire group of mobile bodies by applying strong thrust (transmitting the second takeoff control signal). At this time, the closer the adjacent mobile body (10_N) is to the door (D) of the storage facility (20), the more likely it is to take off preferentially compared to the separated mobile body (10_F) that is farther away from the door (D).

[0171] As another example, the takeoff parameters may include acceleration information of the moving body (10) sensed by the sensor (110 in FIG. 3) of the moving body (10). In this case, the processor (P) can determine a ready moving body based on the acceleration information for each of the multiple moving bodies (10) included in the swarm of moving bodies. As an example, the processor (P) can determine a moving body (10) whose acceleration information is greater than or equal to a predefined threshold as a ready moving body.

[0172] That is, when the mobile bodies (10) are crowded and entangled inside the storage facility (20), with the door (D) open, the adjacent mobile body (10_N) has a relatively larger physical space around it compared to the separated mobile body (10_F). Accordingly, the adjacent mobile body (10_N) can have a greater acceleration due to this physical space when receiving the first takeoff control signal and generating thrust compared to the separated mobile body (10_F). Therefore, the processor (P) first determines the minute acceleration change of each of the multiple mobile bodies (10) by applying a weak thrust (transmitting the first takeoff control signal), and then controls the takeoff of the entire group of mobile bodies by determining the mobile body (10) with a large acceleration that has exited the storage facility (20) as a ready mobile body that is ready for takeoff and applying a strong thrust (transmitting the second takeoff control signal). At this time, the closer the adjacent moving body (10_N) is to the door (D) of the storage facility (20), the more it can be taken off preferentially compared to the separated moving body (10_F) that is farther away from the door (D).

[0173] In some other examples, the processor (P) can control the swarm of mobiles to take off sequentially. In other words, the processor (P) can transmit a take-off control signal to each of the multiple mobiles (10) included in the swarm of mobiles at different times.

[0174] For example, the processor (P) can prioritize the delivery of a takeoff control signal to a mobile body (10) adjacent to the door (D) of the storage facility (20) among a plurality of mobile bodies (10) included in a cluster of mobile bodies. In other words, the processor (P) can prioritize the delivery of a takeoff control signal to a mobile body (10) that is located at a smaller distance from the door (D) of the storage facility (20) among the plurality of mobile bodies (10). For instance, the processor (P) can prioritize the delivery of a takeoff control signal to an adjacent mobile body (10_N) over a separated mobile body (10_F). That is, if an adjacent mobile body (10_N), which is a mobile body (10) placed adjacent to the door (D), is delivered first, interference between the plurality of mobile bodies (10) can be reduced and the takeoff efficiency improved. Thus, the processor (P) can efficiently control the takeoff of the entire cluster of mobile bodies by prioritizing the delivery of such an adjacent mobile body (10_N).

[0175] As another example, the processor (P) can prioritize the delivery of a takeoff control signal to a vehicle that lands in the storage facility (20) later among the vehicles in the cluster. In other words, the processor (P) can prioritize the delivery of a takeoff control signal to a vehicle (10) that lands in the storage facility (20) later. That is, as shown in FIG. 9, which will be described later, when a plurality of vehicles (10) land in the storage facility (20), a vehicle (10) that lands in the storage facility (20) earlier becomes a separated vehicle (10_F), and a vehicle (10) that lands in the storage facility (20) later becomes an adjacent vehicle (10_N). By prioritizing the takeoff of this adjacent vehicle (10_N), the processor (P) can efficiently control the takeoff of the entire cluster of vehicles.

[0176] Meanwhile, the processor (P) can determine the density of the swarm of mobiles within the storage facility (20) and control the takeoff of each mobile (10) within the storage facility (20) based on the determined density. For example, the processor (P) can determine the density of the swarm of mobiles within the storage facility (20) and apply different takeoff methods for each zone based on the determined density.

[0177] To explain in more detail, first, the processor (P) can determine the density of moving objects (10) in a specific area within the storage facility (20). At this time, the density can be calculated based on at least one of the weight information per area measured by the weight sensor of the storage facility (20), distance information between moving objects (10), or number information of moving objects (10) per area.

[0178] Next, the processor (P) can control the takeoff of each mobile body (10) within the storage (20) based on the determined density.

[0179] For example, the processor (P) may provide a step-by-step takeoff control signal to a moving body (10) in a high-density zone and a simultaneous takeoff control signal to a moving body (10) in a low-density zone. For example, in the case of a high-density zone where the density exceeds a predefined threshold, the processor (P) may apply a step-by-step takeoff method that sequentially provides a first takeoff control signal and a second takeoff control signal as described above. On the other hand, in the case of a low-density zone where the density is below the threshold, the processor (P) may provide a simultaneous takeoff control signal to all moving bodies (10) in the zone.

[0180] As another example, the processor (P) can control the takeoff to begin first from the high-density zone. This is because the takeoff efficiency of the entire group of vehicles can be improved by having the vehicles (10) in the high-density zone take off first to secure space inside the storage facility (20).

[0181]

[0182] FIG. 9 is a diagram illustrating the process of a control server controlling the landing of a mobile body according to some embodiments of the present invention.

[0183] Referring to FIGS. 1, FIGS. 7 and FIGS. 9, a processor (P) according to some embodiments of the present invention can control a mobile body (10) landing in a storage facility (20).

[0184] At this time, as described above, the mobile body (10) may not land so as to be aligned with a predefined set coordinate in the storage (20). That is, the processor (P) according to some embodiments of the present invention may not control the mobile body to land at a predefined set coordinate within the storage (20). Accordingly, the mobile body (10) may land randomly at any location within the storage (20).

[0185] However, unlike such random landings, the processor (P) can dynamically adjust the landing position by taking into account the density inside the storage (20) during the landing process of the mobile body (10).

[0186] For example, the processor (P) can monitor the location information of the first landed vehicle (10) in real time and determine the landing area of ​​the next vehicle (10) to land based on this.

[0187] As another example, the processor (P) can calculate the density of zones within the storage (20) and designate zones with low density as priority landing zones. For example, the processor (P) can measure the density of the mobile body (10) in zones in real time through at least one of the weight sensor, pressure sensor, or image sensor of the storage (20), identify the zone with the lowest measured density, and then transmit a landing control signal to the mobile body (10) scheduled for the next landing to guide it to land in that zone. Through this dynamic landing position adjustment, the processor (P) can control the mobile body (10) to be evenly distributed within the storage (20), which can improve efficiency during the next takeoff.

[0188] Meanwhile, the processor (P) can transmit a control signal to the storage facility (20) to close the door (D) of the storage facility (20) under predetermined conditions during the landing process of the mobile body (10).

[0189] For example, the processor (P) can close the door (D) of the storage facility (20) according to the recovery rate of the swarm of mobiles. In other words, the processor (P) can transmit a control signal to the storage facility (20) to close the door (D) of the storage facility (20) based on the ratio of multiple mobiles (10) landing in the storage facility (20), i.e., the recovery rate. As an example, the processor (P) can provide a control signal to the storage facility (20) to close the door (D) when the recovery rate reaches 100% or reaches a predefined value.

[0190] To this end, the processor (P) can determine the recovery rate of the swarm of moving bodies through various methods.

[0191] For example, the processor (P) can determine the recovery rate based on the location information of each of the plurality of mobile bodies (10). At this time, the location information may include GPS information of each of the plurality of mobile bodies (10), but the embodiments of the present invention are not limited thereto. For example, the processor (P) can determine the recovery rate as the ratio of mobile bodies (10) having location information that differs from the location information of the storage facility (20) by less than or equal to a predefined threshold, but the embodiments of the present invention are not limited thereto.

[0192] As another example, the processor (P) may determine the recovery rate based on the communication information of each of the plurality of mobile bodies (10). In this case, the communication information may include information related to notifying the processor (P) that each of the plurality of mobile bodies (10) has landed at the storage facility (20), but the embodiments of the present invention are not limited thereto. For example, the processor (P) may determine the recovery rate based on the ratio of mobile bodies (10) that have transmitted communication information, but the embodiments of the present invention are not limited thereto.

[0193] As another example, the processor (P) can determine the recovery rate based on weight information of the storage facility (20). In this case, the weight information may include information regarding the total weight of the storage facility (20), including the weight of the embedded mobile body (10), but the embodiments of the present invention are not limited thereto. For example, the processor (P) may calculate the recovery rate by inputting weight information regarding the storage facility (20) transmitted from the storage facility (20) into a predefined relationship, but the embodiments of the present invention are not limited thereto.

[0194]

[0195] Referring again to FIGS. 1 and FIGS. 7, as another example, the processor (P) can generate reconstruction data based on the captured data captured by the mobile body (10) through the camera. For example, the processor (P) can generate reconstruction data by receiving the captured data from each mobile body (10) included in the group of mobile bodies in a compressed form, i.e., in the form of compressed data, and by restoring the received compressed data.

[0196] Hereinafter, with reference to FIG. 10, the process of a processor (P) of a control server (CS) according to some embodiments of the present invention generating reconstruction data through captured data taken by a mobile body (10) will be described in detail.

[0197]

[0198] FIG. 10 is a diagram illustrating the process of a control server according to some embodiment of the present invention reconstructing shooting data generated by each of a plurality of moving bodies.

[0199] Referring to FIGS. 1, FIGS. 7 and FIGS. 10, a processor (P) according to some embodiments of the present invention can generate reconstructed data for the captured data by receiving captured data taken by a plurality of mobile bodies (10) in a compressed form, i.e., in the form of compressed data, and then restoring the compressed data. In other words, the processor (P) can generate reconstructed data by restoring the captured data taken by the first mobile body (11), the second mobile body (12), etc., through a camera (120 in FIG. 3) in the form of compressed data.

[0200] At this time, each of the plurality of moving bodies (10) may include an encoder (hereinafter referred to as “EN”) that encodes the captured data into a vector (hereinafter referred to as “V”), and the processor (P) of the control server (CS) may include a decoder (hereinafter referred to as “DE”) that decodes the vector (V) corresponding to each of the plurality of moving bodies (10) to generate reconstructed data.

[0201] In this case, the vector (V) is data in the form of a vector in which the captured data is compressed by the encoder (EN), and may be referred to by terms such as latent representation or latent vector.

[0202] Accordingly, the encoder (EN), which is part of the Transformer structure that is an example of a Neural Network, is processed by the on-boarding computer of the mobile body (10), and the decoder (DE), which is the remaining part, is processed through the high-spec GPU (Graphic Processing Unit) of the processor (P) of the control server (CS). In other words, in the case of a Transformer network that is an example of Generative Artificial Intelligence, it includes an encoder structure and a decoder structure. In the case of the present invention, the encoder structure of such a Transformer network, i.e., the encoder (EN), can be placed in the mobile body (10), and the decoder structure of the Transformer network, i.e., the decoder (DE), can be placed in the processor (P) of the control server (CS).

[0203] However, embodiments of the present invention are not limited thereto, and the encoder (EN) and decoder (DE) can be implemented using various neural network structures. For example, the encoder (EN) and decoder (DE) in the present invention may utilize an auto-encoder, a Variational Autoencoder (VAE), or an encoder-decoder structure based on other compression algorithms. In such structures, the encoder (EN) can convert high-dimensional captured data into a low-dimensional compressed vector, and the decoder (DE) can restore it back to its original form or a post-processed form. Through this compression-restoration method, the present invention can efficiently collect and process large volumes of captured data from swarms of moving objects even in environments with limited bandwidth.

[0204] For example, as illustrated in FIG. 10, the first moving body (11) and the second moving body (12) may each include a first encoder (EN1) and a second encoder (EN2) that encode captured data, and the processor (P) may include a decoder (DE) that decodes each of the first vector (V1) and the second vector (V2) received from each of the first encoder (EN1) and the second encoder (EN2) to generate reconstructed data.

[0205] The decoder (DE) can generate reconstructed data by decoding the vector (V) received from each moving body (10).

[0206] For example, the decoder (DE) can restore the original image data itself. In other words, the decoder (DE) can decode the vector (V) to restore the image data, which is the input data of the encoder (EN), to its original form. That is, the decoder (DE) can restore the image data captured by each moving body (10) exactly as it is through the decoding of the vector (V).

[0207] As another example, the decoder (DE) can restore the captured data in the form of a post-processed version. In other words, the decoder (DE) can restore the vector (V) not in its original form, but in the form of a post-processed version of the original. In this case, the post-processed version may be an image in which the drawing style is simplified compared to the original, or only the outline of the original, or a color image of the original is post-processed into a black-and-white image, but the embodiments of the present invention are not limited thereto.

[0208] The processor (P) of the control server (CS) can reconstruct the scene captured in 3D (3-Dimension) based on the reconstruction data.

[0209] Meanwhile, a control server (CS) according to some embodiments of the present invention may adopt a local communication method for efficiently controlling a large-scale swarm of thousands of mobiles. In some examples, a processor (P) may efficiently control a large-scale swarm of mobiles by transmitting a simplified local control command to only some of the mobiles (10) among the large-scale swarm of mobiles. For example, the processor (P) may transmit a simple command, such as "maintain a distance of at least 1 meter from surrounding mobiles," to some of the mobiles (10) among the swarm of mobiles. In this manner, the mobile (10) that receives the command can adjust its position and behavior by considering only the distance to a small number of adjacent mobiles (10) without needing to determine the state of the entire swarm, and accordingly, the movement, behavior, and flight of the entire large-scale swarm of mobiles can be controlled.

[0210]

[0211] Meanwhile, with reference to FIG. 10, the data processing process for the captured data taken by the aforementioned mobile body (10) may be performed by a separate analysis device rather than a control server (CS). In other words, the control system (1) illustrated in FIG. 1 according to some embodiments of the present invention may further include a separate analysis device for analyzing captured data.

[0212] Hereinafter, an analysis device according to several embodiments of the present invention will be described in detail with reference to FIG. 11.

[0213]

[0214] FIG. 11 is a block diagram of an analysis device according to some embodiments of the present invention.

[0215] Referring to FIGS. 1, FIGS. 2, FIGS. 7 and FIGS. 11, an analysis device (60) according to some embodiments of the present invention is a device for analyzing captured data captured by a plurality of mobile bodies (10), and may include a communication device (610), a memory (620), and a processor (630).

[0216] The communication device (610) performs data communication between the analysis device (60) and an external device. For example, the communication device (610) may perform data communication between the analysis device (60) and the mobile body (10) and / or between the analysis device (60) and the control center (hereinafter referred to as “CC”). For example, the control center (CC) may include the server (30), controller (40), and / or station (50) described in FIG. 2. In this case, the control center (CC) may be referred to as a Ground Control System (GCS).

[0217] At this time, the communication device (610) can communicate with the mobile body (10), controller (40), server (30) and / or station (50) using various communication methods such as infrared communication, RF (Radio Frequency) communication, Wi-Fi communication, ZigBee communication, Bluetooth communication, laser communication, UWB (Ultra-Wideband) communication, LTE, 5G, 6G, and Wireless LAN. However, the communication methods employed by the communication device (610) are not limited to those described above.

[0218] The memory (620) may include any non-transient computer-readable recording medium. As an example, the memory (620) may include a permanent mass storage device such as random access memory (RAM), read-only memory (ROM), a disk drive, a solid state drive (SSD), or flash memory. As another example, a permanent mass storage device such as ROM, an SSD, flash memory, or a disk drive may be a separate permanent storage device distinct from the memory. Additionally, the memory (620) may store an operating system (OS) and at least one program code.

[0219] These software components may be loaded from a computer-readable recording medium separate from memory (620). This separate computer-readable recording medium may be a recording medium that can be directly connected to a computer and may include, for example, computer-readable recording media such as a floppy drive, disk, tape, DVD / CD-ROM drive, or memory card. Alternatively, the software components may be loaded into memory (620) via a communication device (150) that is not a computer-readable recording medium. For example, at least one program may be loaded into memory (620) based on a computer program installed by files provided through the communication device (150) by developers or a file distribution system that distributes installation files for applications.

[0220] The memory (620) may store commands, information, and / or data related to the operation of each component included in the analysis device (60). For example, the memory (620) may store instructions that enable the processor (630) to perform various operations described in this document during execution. For another example, the memory (620) may store various algorithms or models that can be used when the analysis device (60) performs analysis on captured data taken from the moving object (10).

[0221] In some examples, the memory (620) can store a Generative Artificial Intelligence Model (e.g., ChatGPT) that generates analysis information based on the captured data taken by the mobile body (10), and the processor (630) can use this model to analyze the captured data taken by multiple mobile bodies (10).

[0222] The processor (630) can process instructions of a computer program by performing basic arithmetic, logic, and input / output operations. Here, instructions may be provided from memory (620) or an external device (e.g., a server (30), a controller (40), a station (50), etc.). Instructions may also be referred to by the aforementioned name 'instruction'. At this time, the processor (630) may be operatively connected to memory (620) to perform analysis of captured data captured by the mobile body (10). In addition, the processor (630) can generally control the operation of communication devices (610), etc., in addition to memory (620).

[0223] At this time, the functions performed by each module included in the processor (630) may be performed by a single processor or by each separate processor. The processor (630) may perform operations or data processing regarding the control and / or communication of at least one other component of the analysis device (60). Additionally, the processor (630) may be implemented as an array of multiple logic gates, or as a combination of a general-purpose microprocessor and a memory storing a program that can be executed on the microprocessor. For example, the processor (630) may include a general-purpose processor, a central processing unit (CPU), a microprocessor, a digital signal processor (DSP), a controller, a microcontroller, a state machine, etc. In some environments, the processor (630) may include an application-specific integrated circuit (ASIC), a programmable logic device (PLD), a field programmable gate array (FPGA), etc. For example, the processor (630) may refer to a combination of processing devices such as a combination of a digital signal processor (DSP) and a microprocessor, a combination of multiple microprocessors, a combination of one or more microprocessors combined with a digital signal processor (DSP) core, or any other combination of such configurations.

[0224] Meanwhile, such an analysis device (60) may be placed in at least one of the control system (1), that is, the mobile body (10), storage facility (20), server (30), controller (40), station (50), and control server (CS) of FIGS. 1, FIGS. 2, and FIGS. 7. In other words, at least one component of the control system (1), which includes the mobile body (10), storage facility (20), server (30), controller (40), station (50), and control server (CS), may contain the analysis device (60) as described above. At this time, as described above in FIG. 2, the storage facility (20) may be a component corresponding to the station (50), and the control server (CS) may be a component corresponding to the server (30).

[0225] For example, the analysis device (60) may be placed in a control center (CC). In other words, the analysis device (60) may be placed in at least one of the server (30), controller (40), and station (50) included in the control center (CC). That is, at least one of the server (30), controller (40), and station (50) included in the control center (CC) may house the analysis device (60) described above.

[0226] In another example, the analysis device (60) may be placed in at least one of a plurality of mobile bodies (10). In this embodiment, the analysis device (60) is a component included in an on-board computer mounted on the mobile body (10), and accordingly, the operation of the analysis device (60) may be performed by the processor (160) of the mobile body (10) and the memory (130) operatively connected to the processor (160). In other words, when the analysis device (60) is a component included and embedded in the mobile body (10), the memory (620) of the analysis device (60) may be a component included in the memory (130) of the mobile body (10), and the processor (630) of the analysis device (60) may be a component included in the processor (160) of the mobile body (10).

[0227] The placement location and arrangement form of such analysis device (60) will be described in detail later through FIGS. 18 to 23.

[0228] Meanwhile, the processor (630) described above can generate analysis information by analyzing the captured data taken by the mobile body (10). At this time, the mobile body (10) may include a plurality of mobile bodies, and the processor (630) can generate a plurality of analysis information by analyzing the data taken by each of the plurality of mobile bodies. As an example, the processor (630) can generate analysis information from the captured data through a pre-trained generative artificial intelligence model.

[0229] Hereinafter, with reference to FIG. 12, the process of the processor (630) of the analysis device (60) generating analysis information based on the captured data taken from the moving body (10) will be explained in detail.

[0230]

[0231] FIG. 12 is a flowchart relating to the operation of an analysis device according to some embodiments of the present invention. Each step (S100 to S300) of FIG. 12 can be performed by the processor (630) of FIG. 11.

[0232] Referring to FIGS. 2 to 12, first, the processor (630) can acquire shooting data captured by at least one of the plurality of moving bodies (10) (S100).

[0233] In some examples, the processor (630) may obtain captured data from each of the plurality of moving bodies (10) and from the camera (120) included in each moving body (10). At this time, the captured data may include image data of the object (20 in FIG. 1) described above in FIG. 1, that is, a structure or a predefined coordinate area.

[0234] Hereinafter, with reference to FIG. 13, the image data captured by a plurality of moving bodies (10) according to some embodiments of the present invention will be described.

[0235]

[0236] FIG. 13 is a diagram illustrating how a swarm of moving bodies generates image data according to some embodiments of the present invention.

[0237] Referring to FIGS. 2 to 13, the processor (630) can obtain captured data from each of the plurality of moving bodies (11 to 15) captured from the camera (120) included in each moving body (11 to 15). In FIG. 13, for convenience of explanation, the number of the plurality of moving bodies (11 to 15) is shown as five, but it is obvious that embodiments of the present invention are not limited thereto.

[0238] In some examples, a plurality of moving bodies (11 to 15) can generate image data by capturing a coordinate area (Area, hereinafter referred to as “AR”) where each moving body (11 to 15) is located. For example, as shown in FIG. 13, a first moving body (11) can generate first image data for the A coordinate area (AR_A), a second moving body (12) can generate second image data for the B coordinate area (AR_B), a third moving body (13) can generate third image data for the C coordinate area (AR_C), a fourth moving body (14) can generate fourth image data for the D coordinate area (AR_D), and a fifth moving body (15) can generate fifth image data for the E coordinate area (AR_E).

[0239]

[0240] Referring again to FIGS. 2 to 12, the method by which the processor (630) acquires such image data may differ depending on the location where the analysis device (60) is placed. In other words, the method by which the processor (630) acquires image data may differ depending on whether the analysis device (60) is placed in the control center (CC) or included in the moving body (10).

[0241] For example, when the analysis device (60) is placed in the control center (CC), the processor (630) can receive the corresponding shooting data through communication with the communication device (150) of the mobile body (10).

[0242] As another example, when the analysis device (60) is placed on at least one of the plurality of mobile bodies (10), the processor (630) can acquire the corresponding shooting data through wired or wireless communication with the camera (120) of the mobile body (10).

[0243] In this case, the communication device (610), memory (620), and processor (630) of the analysis device (60) may each be integrated with the communication device (150), memory (130), and processor (160) of FIG. 3. However, the present invention is not limited thereto, and the analysis device (60) may be included as a separate component.

[0244] Next, the processor (630) can generate analysis information, which is data obtained by analyzing the shooting data, by inputting the shooting data into a pre-trained analysis model (S200).

[0245] For example, the analysis model may be a model pre-trained through a generative AI model (e.g., ChatGPT).

[0246] Hereinafter, with further reference to FIGS. 14 to 16, the process of a processor (630) according to some embodiments of the present invention generating analysis information based on captured data will be described in detail.

[0247]

[0248] FIG. 14 is a diagram illustrating a processor according to some embodiment of the present invention generating analysis information from captured data through an analysis model. FIG. 15 is a diagram illustrating the neural network structure of an analysis model according to some embodiment of the present invention. FIG. 16 illustrates one example of analysis information according to some embodiment of the present invention. FIG. 17 illustrates another example of analysis information according to some embodiment of the present invention.

[0249] Referring to FIGS. 2 through 16, the processor (630) can generate analysis information (Analysis Data, hereinafter referred to as “AD”), which is data obtained by analyzing the captured data (Camera Data, hereinafter referred to as “CD”), by inputting the captured data (Camera Data, hereinafter referred to as “CD”) into a pre-trained analysis model (621). At this time, the analysis model (621) may be a model stored in memory (620) as shown in FIG. 14, but the embodiments of the present invention are not limited thereto.

[0250] When the analysis model (621) receives the shooting data (CD), it can generate analysis information (AD) regarding the object and coordinate area captured by the shooting data (CD) by analyzing the shooting data (CD). At this time, the analysis model (621) can generate the analysis information (AD) using AI (Artificial Intelligence) technology.

[0251] To explain in more detail, deep learning, a type of machine learning, involves learning by descending to deep levels in multiple stages based on data. In other words, deep learning represents a set of machine learning algorithms that extract key data from multiple datasets by progressively increasing the levels.

[0252] For example, neural networks can utilize various known deep learning structures. For instance, neural networks can utilize structures such as CNN (Convolutional Neural Network), RNN (Recurrent Neural Network), DBN (Deep Belief Network), GNN (Graph Neural Network), GAN (Generative Adversarial Network), Transformer, and Autoencoder.

[0253] Specifically, a Convolutional Neural Network (CNN) is a model that mimics the function of the human brain, based on the assumption that when humans recognize an object, they extract its basic features, perform complex calculations within the brain, and then recognize the object based on the results. CNNs may include well-known structures such as LeNet, AlexNet, VGGNet, GoogleNet, and ResNet, but are not limited to them.

[0254] Recurrent Neural Networks (RNNs) are widely used in natural language processing and are an effective structure for processing time-series data that changes over time; they can be constructed by stacking layers at every moment.

[0255] A Deep Belief Network (DBN) is a deep learning structure constructed by stacking Restricted Boltzmann Machines (RBMs), a deep learning technique, in multiple layers. When the Restricted Boltzmann Machine (RBM) training is repeated until a certain number of layers are reached, a Deep Belief Network (DBN) with that number of layers can be constructed.

[0256] A Graphic Neural Network (GNN) represents an artificial neural network structure implemented by deriving similarities and feature points between modeling data using modeling data modeled based on data mapped between specific parameters.

[0257] A Generative Adversarial Network (GAN) represents an artificial neural network structure that uses a generative neural network and a discriminative neural network to generate new data in a form similar to input data. GANs may include known DCGAN (Deep Convolutional GAN), CGAN (Conditional GAN), WGAN (Wasserstein GAN), StyleGAN (Style-Based GAN), CycleGAN, etc., but embodiments of the present invention are not limited thereto.

[0258] The Transformer is an artificial neural network with an encoder-decoder structure utilizing attention, capable of grasping the overall meaning between input and output sequences. By employing an attention mechanism, the Transformer ensures that every element of the input sequence influences the output sequence, allowing both the encoder and decoder to consider the entire sequence. The Transformer can use natural language and time-series data, as well as patched images, as input.

[0259] An autoencoder is a deep learning architecture that performs the role of extracting and reconstructing data features. Typically, an autoencoder includes an encoder that compresses input values ​​and a decoder that restores the compressed data. The encoder transforms input values ​​into low-dimensional latent representations, while the decoder restores the latent representations to the same dimension as the input values. In this process, both the encoder and decoder can be composed of Multilayer Perceptrons (MLPs). When training an autoencoder, input data is used, and weights and biases are trained to minimize the difference between the output and input values. An autoencoder trained in this way can effectively extract features from input data and restore noisy input data. Autoencoders are primarily utilized in fields such as data compression, dimensionality reduction, noise removal, and data generation; they can also be applied in areas such as image recognition, natural language processing, and speech recognition.

[0260] Meanwhile, the artificial neural network learning of the neural network model used by the analysis model (621) can be achieved by adjusting the weights of the connections between nodes (and, if necessary, adjusting the bias values) so that a desired output is produced for a given input. Additionally, the artificial neural network can continuously update the weight values ​​through learning. Furthermore, methods such as back propagation can be used for the learning of the artificial neural network. At this time, machine learning methods for the artificial neural network may include unsupervised learning, semi-supervised learning, and supervised learning. Additionally, the neural network model can be controlled to automatically update the artificial neural network structure to output analysis information after learning, depending on the settings.

[0261] A neural network structure included in an analysis model (621) according to some embodiments of the present invention is illustrated in FIG. 15. As illustrated in FIG. 15, an analysis model (621) according to some embodiments of the present invention can generate analysis information (AD) using a neural network structure.

[0262] For example, the analysis model (621) may include an input layer, an output layer, and M hidden layers placed between the input layer and the output layer. Here, weights may be set for the edges connecting the nodes of each layer. The presence or absence of such weights or edges may be added, removed, or updated during the learning process. Thus, through the learning process, the weights of the nodes and edges placed between k input nodes and i output nodes may be updated. Before the analysis model (621) performs learning, all nodes and edges may be set to initial values. However, when information is input cumulatively, the weights of the nodes and edges are changed, and in this process, a matching may be achieved between the parameters input as learning factors (captured data (CD)) and the values ​​assigned to the output nodes (analysis information (AD)). Additionally, if a cloud server is used, the analysis model (621) can receive and process a large number of parameters. Accordingly, the analysis model (621) can perform learning based on vast amounts of data. The weights of the nodes and edges between the input nodes and output nodes constituting the analysis model (621) can be updated through the learning process of the neural network. In addition, the parameters input or output in the analysis model (621) may be further expanded to include various data in addition to the shooting data (CD) and analysis information (AD).

[0263] This analysis model (621) can be pre-trained to generate analysis information (AD) by processing, restoring, reconstructing, and / or processing the captured data (CD) when the captured data (CD) is input. As a few examples, the analysis model (621) can be pre-trained to generate analysis information (AD) based on the extracted features of people, objects (e.g., firearms, fire hydrants, etc.), roads, vehicles, buildings, etc. included in the captured data (CD). At this time, the analysis model (621) can be trained through a learning phase, and in the inferencing phase, it can perform computational operations based on the results of the learning phase.

[0264] Some examples of analysis information (AD) generated by the analysis model (621) are shown in FIGS. 16 and FIGS. 17. As shown in FIGS. 16 and FIGS. 17, the analysis information (AD) may be in the form of text data, but embodiments of the present invention are not limited thereto, and the analysis information (AD) may be transformed into various forms such as audio data, image data, video data, vibration data, etc.

[0265] As illustrated in FIGS. 16 and 17, analysis information (AD) according to some embodiments of the present invention may include situation information that analyzes the situation of the shooting data (CD), control information that controls the behavior of the moving body (10) according to the analyzed situation, and guide information that guides the behavior of the user of the analysis device (60) according to the analyzed situation.

[0266] At this time, Fig. 16 <a1>inside <a5>Analysis information (AD) is shown in the case where a plurality of mobile bodies (10) perform "swarm reconnaissance" for each area (AR_A to AR_E) shown in FIG. 13, and FIG. 17 <b1>and <b2>Analysis information (AD) in the case where multiple mobile bodies (10) perform a "security mission" is illustrated in FIG. 17. <c1>inside <c3>Analysis information (AD) is shown in the case where multiple mobile bodies (10) perform a "fire dispatch (initial fire dispatch)."

[0267] For example, Fig. 16 <a1>As illustrated in [Image], the analysis information (AD) may include situation information that analyzes the situation of multiple coordinate areas (AR_A to AR_E) (e.g., one person is moving east in AR_A (coordinate area A), two tanks are waiting in AR_B (coordinate area B)...).

[0268] As another example, Fig. 16 <a2>As illustrated in the figure, the analysis information (AD) may include control information that controls the behavior of the moving body (10) based on the results of analyzing the situation in multiple coordinate areas (AR_A to AR_E) (e.g., "There is one person moving east in AR_A (coordinate area A), so let the A drone (first drone (11)) track the person") and guide information that guides the actions of the user of the analysis device (60) according to the analyzed situation (e.g., "There are two tanks waiting in AR_B (coordinate area B), so please fire").

[0269] As another example, Fig. 16 <a3>inside <a5>As illustrated in [Image], the analysis information (AD) may include situation information that analyzes the situation of a coordinate area (at least one of AR_A to AR_E) selected by the user of the analysis device (60).

[0270] For example, if a user selects a coordinate area A (AR_A) and / or a first moving body (11) that captures the coordinate area A (AR_A), the analysis information (AD) generated in response is of FIG. 16 <a3>Situation information analyzing the situation of a user-selected coordinate area (coordinate area A (AR_A)) as illustrated in Fig. 16 (e.g., 1 person is moving east in AR_A (coordinate area A)), <a4>Control information for controlling the behavior of the first mobile body (11) corresponding to the coordinate area (coordinate area (AR_A)) based on the result of analyzing the situation of the coordinate area selected by the user (coordinate area A (AR_A)) as illustrated in Fig. 16 (e.g., "Since there is one person moving east in AR_A (coordinate area A), I will have the A drone (first drone (11)) track the person") and / or Fig. 16 <a5>It may include guide information that guides the user's actions of the analysis device (60) based on the results of analyzing the situation of the coordinate area selected by the user (coordinate area A (AR_A)) as described in the figure (e.g., "There is one person moving east in AR_A (coordinate area A), so please fire a warning shot").

[0271] As another example, Fig. 17 <b1>As illustrated in [Image], the analysis information (AD) may include situational information that analyzes the situation of a specific coordinate area that is the target of a security mission (e.g., three armed intruders are approaching).

[0272] As another example, Fig. 17 <b2>As illustrated in [Image], the analysis information (AD) may include guide information that guides the actions of the user of the analysis device (60) based on the results of analyzing the situation in a predetermined coordinate area that is the target of a security mission (e.g., "Three armed intruders are approaching, so one police mobile unit needs to be dispatched").

[0273] As another example, Fig. 17 <c1>inside <c3>As illustrated in [Image], the analysis information (AD) may include situation information that analyzes the situation in a predetermined coordinate area that is the target of the fire dispatch (e.g., there is a chemical plant next to the fire situation and it is dangerous, a fire hydrant is located at location xx of the fire situation, the scale of the fire is about xx).

[0274] Meanwhile, the analysis model (621) may receive multiple shooting data (CD) and generate a single analysis information (AD). For example, the analysis model (631) may combine multiple shooting data (CD) to generate a single reconstruction data for the coordinate area captured by each shooting data (CD). At this time, the reconstruction data may include a 3D map (3-Dimension Map), but the embodiments of the present invention are not limited thereto. As an example, in the case of FIG. 13, the analysis model (621) may analyze multiple coordinate areas (AR_A to AR_E) based on each shooting data (CD) for multiple coordinate areas (AR_A to AR_E), and then generate a 3D map that reconstructs the multiple coordinate areas (AR_A to AR_E) based on each analyzed result as analysis information (AD).

[0275]

[0276] Referring again to FIGS. 2 to 12, the processor (630) can then provide analysis information to the user (S300).

[0277] At this time, the processor (630) can provide analysis information to the user of the analysis device (60) in different ways depending on the placement location of the analysis device (60).

[0278] For example, when the analysis device (60) is placed in a control center (CC), that is, at least one of a server (30), a controller (40), and a station (50), the processor (630) can provide analysis information to the user of the analysis device (60) in a visual or non-visual manner through control of an output interface (e.g., display, speaker, etc.) included in at least one of the server (30), the controller (40), and the station (50) included in the control center (CC). A detailed explanation of this will be provided later with reference to FIG. 18.

[0279] In another example, when the analysis device (60) is placed on at least one of a plurality of mobile bodies (10), the processor (630) can provide the analysis information to the user of the analysis device (60) by transmitting the analysis information to a control center (CC), that is, at least one of a server (30), a controller (40), and a station (50), through a communication device (610). At this time, at least one of the server (30), the controller (40), and the station (50) included in the control center (CC) can output the analysis information received from the communication device (610) to the user in a visual or non-visual manner through an output interface (e.g., a display, a speaker, etc.). A detailed explanation of this will be provided later with reference to FIGS. 19 to 23.

[0280] Hereinafter, various embodiments regarding the operation of the moving body (10), the control center (CC), and the analysis device (60) according to the placement position of the analysis device (60) will be described through FIGS. 18 to 23. Meanwhile, the plurality of moving bodies (10) may include n moving bodies, where n may be a natural number greater than or equal to 2, but for convenience of explanation in FIGS. 18 to 23, it will be described by assuming that n is 5 and accordingly, the plurality of moving bodies (10) include the first moving body (11) to the fifth moving body (15).

[0281]

[0282] FIG. 18 is a drawing for explaining a first embodiment regarding the arrangement of an analysis device according to some embodiments of the present invention.

[0283] Referring to FIGS. 2 through 13 and FIG. 18, an analysis device (60) according to some embodiments of the present invention may be placed in a control center (CC). At this time, as described above in FIG. 2, the control center (CC) may include at least one of a server (30) that communicates with a plurality of mobile bodies (10), a controller (40) that controls the movement of the plurality of mobile bodies (10), and a station (50) that stores and stores the plurality of mobile bodies (10).

[0284] In some examples, the processor (630) of the analysis device (60) can receive first shooting data (CD1) to fifth shooting data (CD5) from each of the first moving body (11) to fifth moving body (15), and generate first analysis information (AD1) to fifth analysis information (AD5) by inputting each of the first shooting data (CD1) to fifth shooting data (CD5) into the analysis model (621).

[0285] In other words, the processor (630) can receive first shooting data (CD1) from the first mobile body (11) and generate first analysis information (AD1) from the first shooting data (CD1), receive second shooting data (CD2) from the second mobile body (12) and generate second analysis information (AD2) from the second shooting data (CD2), receive third shooting data (CD3) from the third mobile body (13) and generate third analysis information (AD3) from the third shooting data (CD3), receive fourth shooting data (CD4) from the fourth mobile body (14) and generate fourth analysis information (AD4) from the fourth shooting data (CD4), receive fifth shooting data (CD5) from the fifth mobile body (15) and generate fifth analysis information (AD5) from the fifth shooting data (CD5).

[0286] Subsequently, the processor (630) can provide analysis information to the user of the analysis device (60) in a visual or non-visual manner through control of an output interface (e.g., display, speaker, etc.) included in at least one of the server (30), controller (40), and station (50) included in the control center (CC).

[0287] In some other examples, the processor (630) may receive first shooting data (CD1) to fifth shooting data (CD5) from each moving body (11 to 15) and generate one analysis information (AD). For example, the processor (630) may combine multiple shooting data (CD1 to CD5) to generate one reconstruction data for the coordinate areas captured by the multiple shooting data (CD1 to CD5). At this time, the reconstruction data may include a 3D map (3-Dimension Map), but the embodiments of the present invention are not limited thereto. As an example, the analysis model (621) may analyze multiple coordinate areas (AR_A to AR_E) based on each shooting data (CD1 to CD5) for each of the multiple coordinate areas (AR_A to AR_E), and generate one 3D map as analysis information (AD) by reconstructing the multiple coordinate areas (AR_A to AR_E) based on the results of analyzing each coordinate area (AR_A to AR_E).

[0288]

[0289] FIG. 19 is a drawing for explaining a second embodiment regarding the arrangement of an analysis device according to some embodiments of the present invention.

[0290] Referring to FIGS. 2 through 13 and FIG. 19, an analysis device (60) according to some embodiments of the present invention may be disposed in each of a plurality of moving bodies (10). In other words, the analysis device (60) may include a first analysis device (61) to a fifth analysis device (65) disposed in each of a first moving body (11) to a fifth moving body (15).

[0291] In some examples, each of the first analysis device (61) to the fifth analysis device (65) can analyze the captured data (CD) taken from the mobile body (10) on which each of the first analysis device (61) to the fifth analysis device (65) is mounted to generate analysis information (AD), and each generated analysis information (AD) can be individually transmitted to the control center (CC).

[0292] In other words, the first analysis device (61) embedded in the first mobile body (11) can generate first analysis information (AD1) by inputting first shooting data (CD1) captured from the first mobile body (11) into the analysis model (621), and can transmit the generated first analysis information (AD1) to the control center (CC) through the communication device (610). Similarly, the second analysis device (62) to the fifth analysis device (65) embedded in each of the second mobile body (12) to the fifth mobile body (15) can also generate second analysis information (AD2) to fifth analysis information (AD5) in a similar manner and transmit them to the control center (CC).

[0293] Subsequently, at least one of the server (30), controller (40), and station (50) included in the control center (CC) can output the analysis information (AD1 to AD5) received from the communication device (610) to the user in a visual or non-visual manner through an output interface (e.g., display, speaker, etc.).

[0294]

[0295] FIG. 20 is a drawing for explaining a third embodiment regarding the arrangement of an analysis device according to some embodiments of the present invention.

[0296] Referring to FIGS. 2 to 13 and FIG. 20, a plurality of moving bodies (10) according to some embodiments of the present invention may include a reference moving body (10_R) that communicates with a control center (CC) and transmits analysis information (AD) to the control center (CC), and other moving bodies (10_E) excluding the reference moving body (10_R).

[0297] At this time, the reference moving body (10_R) can be determined as any one of a plurality of moving bodies (10), namely the first moving body (11) to the fifth moving body (15). For convenience of explanation, FIG. 19 is illustrated as the fifth moving body (15) being the reference moving body (10_R), and the other remaining moving bodies (11 to 14) being other moving bodies (10_E). In some examples, the reference moving body (10_R) can be determined from among the plurality of moving bodies (10) through at least one of the following criteria: random selection, distance from the control center (CC), positional relationship between the plurality of moving bodies (10), and use of the moving body (10).

[0298] For example, the reference moving body (10_R) may be any one of the moving bodies (10) randomly selected. In other words, the reference moving body (10_R) may be randomly selected from the moving bodies (10).

[0299] As another example, the reference moving body (10_R) can be determined based on the distance from the control center (CC) of each of the multiple moving bodies (10). For instance, the moving body closest to the control center (CC) can be determined as the reference moving body (10_R).

[0300] As another example, the reference moving body (10_R) can be determined according to the positional relationship between the multiple moving bodies (10). For example, the moving body located most at the center among the multiple moving bodies (10) can be determined as the reference moving body (10_R). In other words, the reference moving body (10_R) can be determined as the moving body with the minimum distance from the other moving bodies.

[0301] As another example, the reference vehicle (10_R) can be determined according to the use of each of the multiple vehicles (10). For example, each vehicle (10) may be assigned a unique purpose or use (e.g., reconnaissance, management, etc.), and the reference vehicle (10_R) may be determined as a vehicle that performs only management and does not perform reconnaissance.

[0302] This reference mobile body (10_R) can communicate with a control center (CC) to transmit analysis information (AD) to the control center (CC). At this time, the analysis device (60) may include a reference analysis device (60_R) placed on this reference mobile body (10_R).

[0303] More specifically, the processor (630) of the reference analysis device (60_R) included in the reference mobile body (10_R) can receive first to fourth shooting data (CD1 to CD4) from other mobile bodies (10_E), excluding the fifth mobile body (15) which is the reference mobile body (10_R) among the plurality of mobile bodies (10), i.e., the first mobile body (11) to the fourth mobile body (14), through a communication device (610), and can obtain fifth shooting data (CD5) from the fifth mobile body (15) which is the reference mobile body (10_R).

[0304] Next, the processor (630) of the reference analysis device (60_R) included in the reference moving body (10_R) can generate analysis information (AD) based on each shooting data (CD1 to CD5).

[0305] For example, the processor (630) of the reference analysis device (60_R) can generate one analysis information (AD) based on the first to fifth shooting data (CD1) to fifth shooting data (CD5). For example, the processor (630) of the reference analysis device (60_R) can combine the multiple shooting data (CD1 to CD5) to generate one reconstruction data for the coordinate area captured by the multiple shooting data (CD1 to CD5). At this time, the reconstruction data may include a 3D map (3-Dimension Map), but the embodiments of the present invention are not limited thereto. For example, the processor (630) of the reference analysis device (60_R) can analyze the multiple coordinate areas (AR_A to AR_E) based on each of the captured data (CD1 to CD5) for the multiple coordinate areas (AR_A to AR_E), and generate a single 3D map of the multiple coordinate areas (AR_A to AR_E) reconstructed based on the results of analyzing each coordinate area (AR_A to AR_E) as analysis information (AD). For example, the processor (630) of the reference analysis device (60_R) can summarize or integrate the analysis information of the multiple coordinate areas (AR_A to AR_E) (i.e., the first analysis information (AD1) to the fifth analysis information (AD5)), or integrate the analysis information for a selected portion of the multiple coordinate areas (AR_A to AR_E) to generate a single analysis information (AD).

[0306] As another example, the processor (630) of the reference analysis device (60_R) may generate first analysis information (AD1) to fifth analysis information (AD5) by inputting each of the image data (CD) of each of the plurality of moving bodies (10), that is, the image data (CD5) of the fifth moving body (15) which is the reference moving body (10_R) and the image data (CD1 to CD4) of the first moving body (11) to the fourth moving body (14) which is other moving bodies (10_E), into the analysis model (621).

[0307] Next, the processor (630) of the reference analysis device (60_R) included in the reference moving body (10_R) can transmit one analysis information (AD) and / or multiple analysis information (first analysis information (AD1) to fifth analysis information (AD5)) to the control center (CC) through a communication device (610), etc.

[0308]

[0309] FIG. 21 is a drawing for explaining a fourth embodiment regarding the arrangement of an analysis device according to some embodiments of the present invention.

[0310] Referring to FIGS. 2 to 13 and FIG. 21, a plurality of moving bodies (10) according to some embodiments of the present invention may include a reference moving body (10_R) that communicates with a control center (CC) and transmits analysis information (AD) to the control center (CC) as described above in FIG. 20, and other moving bodies (10_E) excluding the reference moving body (10_R). In this case, the reference moving body (10_R) may be determined as any one of the plurality of moving bodies (10), namely the first moving body (11) to the fifth moving body (15). FIG. 21 is illustrated as the fifth moving body (15) being the reference moving body (10_R) and the other remaining moving bodies (11 to 14) being other moving bodies (10_E) for convenience of explanation, similar to FIG. 20. For example, the reference moving body (10_R) can be determined by at least one of the following criteria: random selection among a plurality of moving bodies (10), distance from the control center (CC), positional relationship between the plurality of moving bodies (10), and use of the moving body (10). A detailed explanation of this is omitted here as it has been previously described through FIG. 20.

[0311] This reference mobile body (10_R) can communicate with the control center (CC) to transmit analysis information (AD) to the control center (CC). At this time, the analysis device (60) may include a reference analysis device (60_R) placed in this reference mobile body (10_R) and an other analysis device (60_E) included in the other mobile body (10_E).

[0312] More specifically, first, the other analysis device (60_E) can generate its own analysis information (AD) based on the captured data (CD) taken from the other mobile body (10_E) in which the other analysis device (60_E) is built. That is, the other analysis device (60_E) built into the first mobile body (11) can generate the first analysis information (AD1) based on the first captured data (CD1) generated by the first mobile body (11), and the other analysis devices (60_E) built into the second mobile body (12) to the fourth mobile body (14) can also generate the second analysis information (AD2) to the fourth analysis information (AD4) in a similar manner.

[0313] In addition, similarly, the reference analysis device (60_R) can generate analysis information (AD) based on the captured data (CD) taken from the reference mobile body (10_R) in which the reference analysis device (60_R) is built. That is, the reference analysis device (60_R) built into the fifth mobile body (15) can generate fifth analysis information (AD5) based on the fifth captured data (CD5) generated by the fifth mobile body (15).

[0314] Next, the processor (630) of the reference analysis device (60_R) included in the reference mobile body (10_R) can receive their respective analysis information (AD1 to AD4) from the first mobile body (11) to the fourth mobile body (14), which are other mobile bodies (10_E), through a communication device (610), etc.

[0315] Next, the processor (630) of the reference analysis device (60_R) included in the reference mobile body (10_R) can transmit the first analysis information (AD1) to the fifth analysis information (AD5) to the control center (CC) through a communication device (610), etc. At this time, the processor (630) of the reference analysis device (60_R) may transmit each of the first analysis information (AD1) to the fifth analysis information (AD5) individually to the control center (CC) as shown in FIG. 21, or it may combine the first analysis information (AD1) to the fifth analysis information (AD5) to generate a single reconstruction data (e.g., a 3D map) and then transmit the generated reconstruction data to the control center (CC). As another example, the processor (630) of the reference analysis device (60_R) may summarize or integrate analysis information of multiple coordinate regions (AR_A to AR_E) (i.e., first analysis information (AD1) to fifth analysis information (AD5)), or integrate analysis information for selected parts of the multiple coordinate regions (AR_A to AR_E) to generate a single analysis information, and transmit the single analysis information to the control center (CC).

[0316]

[0317] FIG. 22 is a drawing for explaining a fifth embodiment regarding the arrangement of an analysis device according to some embodiments of the present invention.

[0318] Referring to FIGS. 2 to 13 and FIG. 22, a plurality of moving bodies (10) according to some embodiments of the present invention may include a reference moving body (10_R) that communicates with a control center (CC) and transmits analysis information (AD) to the control center (CC) as described above in FIGS. 20 and 21, and other moving bodies (10_E) excluding the reference moving body (10_R). In this case, the reference moving body (10_R) may be determined as any one of the plurality of moving bodies (10), namely the first moving body (11) to the fifth moving body (15). FIG. 22 is illustrated as the fifth moving body (15) being the reference moving body (10_R) and the other remaining moving bodies (11 to 14) being other moving bodies (10_E), as in FIGS. 20 and 21, for convenience of explanation. For example, the reference moving body (10_R) can be determined by at least one of the following criteria: random selection among a plurality of moving bodies (10), distance from the control center (CC), positional relationship between the plurality of moving bodies (10), and use of the moving body (10). A detailed explanation of this is omitted here as it has been previously described through FIG. 20.

[0319] This reference mobile body (10_R) can communicate with the control center (CC) to transmit analysis information (AD) to the control center (CC). At this time, the analysis device (60) may include a reference analysis device (60_R) placed in this reference mobile body (10_R) and an other analysis device (60_E) included in the other mobile body (10_E). A detailed explanation of this has been previously provided through FIG. 21, so it is omitted here.

[0320] At this time, multiple mobile bodies (10) can perform data transmission and reception through cumulative data transmission. In other words, multiple mobile bodies (10) can perform data transmission and reception through methods such as cumulative data relay, incremental data transmission, cascade data transmission, and chained data transfer.

[0321] More specifically, first, the other analysis device (60_E) can generate its own analysis information (AD) based on the captured data (CD) taken from the other mobile body (10_E) in which the other analysis device (60_E) is built. That is, the other analysis device (60_E) built into the first mobile body (11) can generate the first analysis information (AD1) based on the first captured data (CD1) generated by the first mobile body (11), and the other analysis devices (60_E) built into the second mobile body (12) to the fourth mobile body (14) can also generate the second analysis information (AD2) to the fourth analysis information (AD4) in a similar manner.

[0322] In addition, similarly, the reference analysis device (60_R) can generate analysis information (AD) based on the captured data (CD) taken from the reference mobile body (10_R) in which the reference analysis device (60_R) is built. That is, the reference analysis device (60_R) built into the fifth mobile body (15) can generate fifth analysis information (AD5) based on the fifth captured data (CD5) generated by the fifth mobile body (15).

[0323] Next, the other analysis device (60_E) embedded in the first mobile body (11), which is the other mobile body (10_E), can transmit the first analysis information (AD1) to the second mobile body (12) through a communication device (610), etc.

[0324] Next, the other analysis device (60_E) embedded in the second mobile body (12), which is the other mobile body (10_E), can transmit the second analysis information (AD2) generated through the first analysis information (AD1) received from the first mobile body (11) and the second shooting data (CD2) to the third mobile body (13) through a communication device (610), etc.

[0325] Next, the other analysis device (60_E) embedded in the third mobile body (13), which is the other mobile body (10_E), can transmit the third analysis information (AD3) generated through the first and second analysis information (AD1, AD2) received from the second mobile body (12) and the third shooting data (CD3) to the fourth mobile body (14) through a communication device (610), etc.

[0326] Next, the other analysis device (60_E) embedded in the fourth mobile body (14), which is the other mobile body (10_E), can transmit the fourth analysis information (AD4) generated through the first to third analysis information (AD1 to AD3) received from the third mobile body (13) and the fourth shooting data (CD4) to the fourth mobile body (14) through a communication device (610), etc.

[0327] Next, the reference analysis device (60_R) embedded in the fifth mobile body (15), which is the reference mobile body (10_R), can transmit the fifth analysis information (AD5) generated through the first to fourth analysis information (AD1 to AD4) received from the fourth mobile body (14) and the fifth shooting data (CD5) to the control center (CC) via a communication device (610), etc. At this time, the processor (630) of the reference analysis device (60_R) may transmit each of the first analysis information (AD1) to the fifth analysis information (AD5) individually to the control center (CC) as shown in FIG. 22, or it may combine the first analysis information (AD1) to the fifth analysis information (AD5) to generate a single reconstruction data (e.g., 3D map) and then transmit the generated reconstruction data to the control center (CC). As another example, the processor (630) of the reference analysis device (60_R) may summarize or integrate analysis information of multiple coordinate regions (AR_A to AR_E) (i.e., first analysis information (AD1) to fifth analysis information (AD5)), or integrate analysis information for selected parts of the multiple coordinate regions (AR_A to AR_E) to generate a single analysis information, and transmit the single analysis information to the control center (CC).

[0328]

[0329] FIG. 23 is a drawing for explaining a sixth embodiment regarding the arrangement of an analysis device according to some embodiments of the present invention.

[0330] Referring to FIGS. 2 to 13 and FIG. 23, a plurality of moving bodies (10) according to some embodiments of the present invention may include a reference moving body (10_R) that communicates with a control center (CC) and transmits analysis information (AD) to the control center (CC) as described above in FIGS. 20 to 22, and other moving bodies (10_E) excluding the reference moving body (10_R). In this case, the reference moving body (10_R) may be determined as any one of the plurality of moving bodies (10), namely the first moving body (11) to the fifth moving body (15). FIG. 23 is illustrated as the fifth moving body (15) being the reference moving body (10_R) and the other remaining moving bodies (11 to 14) being other moving bodies (10_E), as in FIGS. 20 to 22, for convenience of explanation. For example, the reference moving body (10_R) can be determined by at least one of the following criteria: random selection among a plurality of moving bodies (10), distance from the control center (CC), positional relationship between the plurality of moving bodies (10), and use of the moving body (10). A detailed explanation of this is omitted here as it has been previously described through FIG. 20.

[0331] This reference moving body (10_R) can communicate with a control center (CC) to transmit analysis information (AD) to the control center (CC). At this time, the analysis device (60) may include a reference analysis device (60_R) placed on this reference moving body (10_R). A detailed explanation of this has been previously described through FIGS. 20 to 22, so it is omitted here.

[0332] At this time, multiple mobile bodies (10) can transmit and receive data through cumulative data transmission as described above in FIG. 22. In other words, multiple mobile bodies (10) can transmit and receive data through methods such as cumulative data relay, incremental data transmission, cascade data transmission, and chained data transfer.

[0333] More specifically, first, the first mobile body (11), which is the other mobile body (10_E), can transmit the first captured data (CD1) captured through the camera (120) of the first mobile body (11) to the second mobile body (12) through the communication device (150).

[0334] Next, the second mobile body (12), which is another mobile body (10_E), can transmit the first shooting data (CD1) received from the first mobile body (11) and the second shooting data (CD2) captured through the camera (120) of the second mobile body (12) to the third mobile body (13) through the communication device (150).

[0335] Next, the third mobile body (13), which is another mobile body (10_E), can transmit the first shooting data (CD1) and the second shooting data (CD2) received from the first mobile body (11) and the second mobile body (12), and the third shooting data (CD3) captured through the camera (120) of the third mobile body (13) to the fourth mobile body (14) through the communication device (150).

[0336] Next, the fourth mobile body (14), which is an other mobile body (10_E), can transmit the first shooting data (CD1) to the third shooting data (CD3) received from the first mobile body (11) to the third mobile body (13), and the fourth shooting data (CD4) captured through the camera (120) of the fourth mobile body (14), to the fifth mobile body (15), which is a reference mobile body (10_R), through a communication device (150).

[0337] Next, the reference analysis device (60_R) embedded in the fifth mobile body (15), which is the reference mobile body (10_R), can generate analysis information (AD) based on the first shooting data (CD1) to the fourth shooting data (CD4) received from the fourth mobile body (14) and the fifth shooting data (CD5) captured through the camera (120) of the fifth mobile body (15).

[0338] For example, the processor (630) of the reference analysis device (60_R) can generate one analysis information (AD) based on the first to fifth shooting data (CD1) to fifth shooting data (CD5). For example, the processor (630) of the reference analysis device (60_R) can combine the multiple shooting data (CD1 to CD5) to generate one reconstruction data for the coordinate area captured by the multiple shooting data (CD1 to CD5). At this time, the reconstruction data may include a 3D map (3-Dimension Map), but the embodiments of the present invention are not limited thereto. For example, the processor (630) of the reference analysis device (60_R) can analyze the multiple coordinate areas (AR_A to AR_E) based on each of the captured data (CD1 to CD5) for the multiple coordinate areas (AR_A to AR_E), and generate a single 3D map of the multiple coordinate areas (AR_A to AR_E) reconstructed based on the results of analyzing each coordinate area (AR_A to AR_E) as analysis information (AD). For another example, the processor (630) of the reference analysis device (60_R) can summarize or integrate the analysis information of the multiple coordinate areas (AR_A to AR_E) (i.e., the first analysis information (AD1) to the fifth analysis information (AD5)), or integrate the analysis information for a selected portion of the multiple coordinate areas (AR_A to AR_E) to generate a single analysis information, and transmit the single analysis information to the control center (CC).

[0339] As another example, the processor (630) of the reference analysis device (60_R) may generate first analysis information (AD1) to fifth analysis information (AD5) by inputting each of the image data (CD) of each of the plurality of moving bodies (10), that is, the image data (CD5) of the fifth moving body (15) which is the reference moving body (10_R) and the image data (CD1 to CD4) of the first moving body (11) to the fourth moving body (14) which is other moving bodies (10_E), into the analysis model (621).

[0340] Next, the processor (630) of the reference analysis device (60_R) included in the reference moving body (10_R) can transmit one analysis information (AD) and / or multiple analysis information (first analysis information (AD1) to fifth analysis information (AD5)) to the control center (CC) through a communication device (610), etc.

[0341] The above description is merely an illustrative explanation of the technical concept of the present embodiment, and a person skilled in the art to which the present embodiment belongs would be able to make various modifications and variations within the scope of the essential characteristics of the present embodiment. Accordingly, the present embodiments are intended to explain, not limit, the technical concept of the present embodiment, and the scope of the technical concept of the present embodiment is not limited by these embodiments. The scope of protection of the present embodiment shall be interpreted by the claims below, and all technical concepts within an equivalent scope shall be interpreted as being included within the scope of rights of the present embodiment.

Claims

1. A control server that manages a cluster of mobile bodies including multiple mobile bodies and a storage facility that stores the cluster of mobile bodies, Memory for storing at least one instruction; and It includes at least one processor that executes the above at least one instruction, The processor controls the swarm of mobiles to land in any area within the storage facility. Control server.

2. In Paragraph 1, The processor transmits a takeoff control signal to the swarm of mobiles to control the takeoff of the swarm of mobiles. Control server.

3. In Paragraph 2, The above processor is, The above takeoff control signal is provided simultaneously to a plurality of mobile bodies included in the above swarm of mobile bodies, or The above takeoff control signal is provided to each of the plurality of mobile bodies included in the swarm of mobile bodies at different times. Control server.

4. In Paragraph 3, The above processor is, When the above takeoff control signal is provided simultaneously to a plurality of mobile bodies included in the above swarm of mobile bodies, At a first time point, a first takeoff control signal is provided to the plurality of mobile bodies, and At a second time point that is later in time series than the first time point, the takeoff parameters for each of the plurality of moving bodies are determined, and Based on the above takeoff parameters, determine the ready moving body that is ready for takeoff, and Providing a second takeoff control signal to the determined ready mobile body Control server.

5. In Paragraph 4, The thrust provided by the second takeoff control signal to the moving body has a larger value compared to the thrust provided by the first takeoff control signal to the moving body. Control server.

6. In Paragraph 4, The above takeoff parameters include position information of the moving body, and The processor determines a moving object whose location information differs from the location of the storage by more than a predefined threshold as the ready moving object. Control server.

7. In Paragraph 4, The above takeoff parameters include acceleration information of the moving body, and The processor determines a moving body whose acceleration information is greater than or equal to a predefined threshold as the ready moving body. Control server.

8. In Paragraph 3, The above processor is, When the above takeoff control signal is provided to each of the plurality of moving bodies included in the swarm of moving bodies at different times, Among the above cluster of mobile bodies, the takeoff control signal is preferentially provided to the mobile body with a small distance from the door of the storage facility. Control server.

9. In Paragraph 3, The above processor is, When the above takeoff control signal is provided to each of the plurality of moving bodies included in the swarm of moving bodies at different times, Among the above-mentioned swarm of mobiles, the takeoff control signal is provided preferentially to the mobiles that landed at the storage facility later in priority. Control server.

10. In Paragraph 1, The processor controls the swarm of mobiles to land in the storage without being pre-aligned to predefined coordinates within the storage. Control server.

11. In Paragraph 1, The processor closes the door of the storage facility according to the recovery rate of the swarm of mobiles when controlling the landing of the swarm of mobiles. Control server.

12. In Paragraph 11, The above processor is, Determining the recovery rate of the swarm of mobile bodies based on at least one of the location information of each of the plurality of mobile bodies, communication information with the plurality of mobile bodies, and weight information of the storage facility. Control server.

13. In Paragraph 1, The processor generates reconstruction data for the captured data based on the captured data captured by the moving swarm through a camera. Control server.

14. In Paragraph 13, Each of the above plurality of moving bodies includes an encoder that encodes the above-mentioned shooting data into a vector, and The processor includes a decoder that generates the reconstructed data by decoding a vector corresponding to each of the plurality of moving bodies and restoring the captured data. Control server.

15. In Paragraph 14, The above decoder is, Restore the above shooting data to its original form, or Restoring the above-mentioned shooting data into a post-processed form Control server.

Images