Patent Publication Number: US-11383379-B2

Title: Artificial intelligence server for controlling plurality of robots and method for the same

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     Pursuant to 35 U.S.C. § 119(a), this application claims the benefit of earlier filing date and right of priority to Korean Patent Application No. 10-2019-0093444, filed on Jul. 31, 2019, the contents of which are hereby incorporated by reference herein in its entirety. 
     BACKGROUND 
     The present invention relates to an artificial intelligence server for controlling a plurality of robots and a method for the same. Specifically, the present invention relates to an artificial intelligence server for organically controlling at least one of operations or deployment areas of a plurality of robots, and a method for the same. 
     Recently, with the explosive growth of airport users and efforts to leap to smart airports, methods for providing services through artificial intelligence robots in airports or multiplexes are being discussed. 
     In a case in which artificial intelligence robots are introduced at airports or multiplexes, it is expected that robots can take on the unique role of human beings, which traditional computer systems could not replace, thereby contributing to the quantitative and qualitative improvement of provided services. 
     Artificial intelligence robots can perform various operations such as informing the users of directions at airports and various places where a lot of people gather. 
     However, in general, artificial intelligence robots are located only in the area allocated thereto, and thus, there is a problem that cannot actively cope with a situation where guidance services of more robots are needed when the density of people is increased. 
     SUMMARY 
     The present invention is directed to provide an artificial intelligence server for controlling operations or deployment areas of robots so as to reduce the gap of the deployment areas when a specific robot needs to perform an operation for moving out of the deployment area thereof, and a method for the same, and a method for the same. 
     One embodiment of the present invention provides an artificial intelligence server and a method for the same, wherein the artificial intelligence server receives information including an operation and a deployment area corresponding to each of the plurality of robots, determines a first route corresponding to a first operation corresponding to a first robot among the plurality of robots, if the first operation corresponding to the first robot includes an operation for moving out of a first deployment area corresponding to the first robot, determines an area on the determined first route, in which at least one robot is deployed, determines an associated operation between the first robot and a second robot, wherein the second robot is deployed in the determined area, and updates a second operation corresponding to the second robot or a second deployment area corresponding to the second robot, based on the determined associated operation. 
     In addition, one embodiment of the present invention provides an intelligence artificial server for updating an operation of a second robot to take over an operation of a first robot by using a first associated operation for transferring the operations of the two robots, deployed in adjacent areas, to each other, and a method for the same. 
     Furthermore, one embodiment of the present invention provides an artificial intelligence server for exchanging a deployment area of a first robot and a deployment area of a second robot by using a second associated operation for exchanging the deployment areas of the two robots deployed in adjacent areas, and a method for the same. 
     The details of one or more embodiments are set forth in the accompanying drawings and the description below. Other features will be apparent from the description and drawings, and from the claims. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a block diagram illustrating an AI apparatus according to an embodiment of the present invention. 
         FIG. 2  is a block diagram illustrating an AI server according to an embodiment of the present invention. 
         FIG. 3  is a view illustrating an AI system according to an embodiment of the present invention. 
         FIG. 4  is a block diagram illustrating an AI apparatus according to an embodiment of the present invention. 
         FIG. 5  is a flowchart illustrating a method for controlling a plurality of robots according to an embodiment of the present invention. 
         FIG. 6  is a view illustrating a density measured for each unit area according to an embodiment of the present invention. 
         FIGS. 7 to 9  are views illustrating a method for calculating a density for a group area according to an embodiment of the present invention. 
         FIG. 10  is a view illustrating a method for determining priorities among group areas having the same density according to an embodiment of the present invention. 
         FIG. 11  is a view illustrating a method for determining priorities among group areas having the same density according to an embodiment of the present invention. 
         FIG. 12  is a view illustrating a situation in which an operation of a specific robot includes an operation for moving out of its deployment area. 
         FIG. 13  is a view illustrating a method for controlling robots in the situation of  FIG. 12  based on a first associated operation according to an embodiment of the present invention. 
         FIG. 14  is a view illustrating a method for controlling robots in the situation of  FIG. 13  based on a first associated operation according to an embodiment of the present invention. 
         FIG. 15  is a view illustrating a method for controlling robots in the situation of  FIG. 13  based on a second associated operation according to an embodiment of the present invention. 
         FIG. 16  is a view illustrating a method for controlling robots in the situation of FIG.  12  based on a second associated operation according to an embodiment of the present invention. 
         FIG. 17  is a flowchart illustrating a method for controlling a plurality of robots according to an embodiment of the present invention. 
     
    
    
     DETAILED DESCRIPTION OF THE EMBODIMENTS 
     Hereinafter, embodiments of the present disclosure are described in more detail with reference to accompanying drawings and regardless of the drawings symbols, same or similar components are assigned with the same reference numerals and thus overlapping descriptions for those are omitted. The suffixes “module” and “unit” for components used in the description below are assigned or mixed in consideration of easiness in writing the specification and do not have distinctive meanings or roles by themselves. In the following description, detailed descriptions of well-known functions or constructions will be omitted since they would obscure the invention in unnecessary detail. Additionally, the accompanying drawings are used to help easily understanding embodiments disclosed herein but the technical idea of the present disclosure is not limited thereto. It should be understood that all of variations, equivalents or substitutes contained in the concept and technical scope of the present disclosure are also included. 
     It will be understood that the terms “first” and “second” are used herein to describe various components but these components should not be limited by these terms. These terms are used only to distinguish one component from other components. 
     In this disclosure below, when one part (or element, device, etc.) is referred to as being ‘connected’ to another part (or element, device, etc.), it should be understood that the former can be ‘directly connected’ to the latter, or ‘electrically connected’ to the latter via an intervening part (or element, device, etc.). It will be further understood that when one component is referred to as being ‘directly connected’ or ‘directly linked’ to another component, it means that no intervening component is present. 
     &lt;Artificial Intelligence (AI)&gt; 
     Artificial intelligence refers to the field of studying artificial intelligence or methodology for making artificial intelligence, and machine learning refers to the field of defining various issues dealt with in the field of artificial intelligence and studying methodology for solving the various issues. Machine learning is defined as an algorithm that enhances the performance of a certain task through a steady experience with the certain task. 
     An artificial neural network (ANN) is a model used in machine learning and may mean a whole model of problem-solving ability which is composed of artificial neurons (nodes) that form a network by synaptic connections. The artificial neural network can be defined by a connection pattern between neurons in different layers, a learning process for updating model parameters, and an activation function for generating an output value. 
     The artificial neural network may include an input layer, an output layer, and optionally one or more hidden layers. Each layer includes one or more neurons, and the artificial neural network may include a synapse that links neurons to neurons. In the artificial neural network, each neuron may output the function value of the activation function for input signals, weights, and deflections input through the synapse. 
     Model parameters refer to parameters determined through learning and include a weight value of synaptic connection and deflection of neurons. A hyperparameter means a parameter to be set in the machine learning algorithm before learning, and includes a learning rate, a repetition number, a mini batch size, and an initialization function. 
     The purpose of the learning of the artificial neural network may be to determine the model parameters that minimize a loss function. The loss function may be used as an index to determine optimal model parameters in the learning process of the artificial neural network. 
     Machine learning may be classified into supervised learning, unsupervised learning, and reinforcement learning according to a learning method. 
     The supervised learning may refer to a method of learning an artificial neural network in a state in which a label for training data is given, and the label may mean the correct answer (or result value) that the artificial neural network must infer when the training data is input to the artificial neural network. The unsupervised learning may refer to a method of learning an artificial neural network in a state in which a label for training data is not given. The reinforcement learning may refer to a learning method in which an agent defined in a certain environment learns to select a behavior or a behavior sequence that maximizes cumulative compensation in each state. 
     Machine learning, which is implemented as a deep neural network (DNN) including a plurality of hidden layers among artificial neural networks, is also referred to as deep learning, and the deep learning is part of machine learning. In the following, machine learning is used to mean deep learning. 
     &lt;Robot&gt; 
     A robot may refer to a machine that automatically processes or operates a given task by its own ability. In particular, a robot having a function of recognizing an environment and performing a self-determination operation may be referred to as an intelligent robot. 
     Robots may be classified into industrial robots, medical robots, home robots, military robots, and the like according to the use purpose or field. 
     The robot includes a driving unit may include an actuator or a motor and may perform various physical operations such as moving a robot joint. In addition, a movable robot may include a wheel, a brake, a propeller, and the like in a driving unit, and may travel on the ground through the driving unit or fly in the air. 
     &lt;Self-Driving&gt; 
     Self-driving refers to a technique of driving for oneself, and a self-driving vehicle refers to a vehicle that travels without an operation of a user or with a minimum operation of a user. 
     For example, the self-driving may include a technology for maintaining a lane while driving, a technology for automatically adjusting a speed, such as adaptive cruise control, a technique for automatically traveling along a predetermined route, and a technology for automatically setting and traveling a route when a destination is set. 
     The vehicle may include a vehicle having only an internal combustion engine, a hybrid vehicle having an internal combustion engine and an electric motor together, and an electric vehicle having only an electric motor, and may include not only an automobile but also a train, a motorcycle, and the like. 
     Here, the self-driving vehicle may be regarded as a robot having a self-driving function. 
     &lt;eXtended Reality (XR)&gt; 
     Extended reality is collectively referred to as virtual reality (VR), augmented reality (AR), and mixed reality (MR). The VR technology provides a real-world object and background only as a CG image, the AR technology provides a virtual CG image on a real object image, and the MR technology is a computer graphic technology that mixes and combines virtual objects into the real world. 
     The MR technology is similar to the AR technology in that the real object and the virtual object are shown together. However, in the AR technology, the virtual object is used in the form that complements the real object, whereas in the MR technology, the virtual object and the real object are used in an equal manner. 
     The XR technology may be applied to a head-mount display (HMD), a head-up display (HUD), a mobile phone, a tablet PC, a laptop, a desktop, a TV, a digital signage, and the like. A device to which the XR technology is applied may be referred to as an XR device. 
       FIG. 1  is a block diagram illustrating an AI apparatus  100  according to an embodiment of the present invention. 
     The AI apparatus (or an AI device)  100  may be implemented by a stationary device or a mobile device, such as a TV, a projector, a mobile phone, a smartphone, a desktop computer, a notebook, a digital broadcasting terminal, a personal digital assistant (PDA), a portable multimedia player (PMP), a navigation device, a tablet PC, a wearable device, a set-top box (STB), a DMB receiver, a radio, a washing machine, a refrigerator, a desktop computer, a digital signage, a robot, a vehicle, and the like. 
     Referring to  FIG. 1 , the AI apparatus  100  may include a communication unit  110 , an input unit  120 , a learning processor  130 , a sensing unit  140 , an output unit  150 , a memory  170 , and a processor  180 . 
     The communication unit  110  may transmit and receive data to and from external devices such as other  100   a  to  100   e  and the AI server  200  by using wire/wireless communication technology. For example, the communication unit  110  may transmit and receive sensor information, a user input, a learning model, and a control signal to and from external devices. 
     The communication technology used by the communication unit  110  includes GSM (Global System for Mobile communication), CDMA (Code Division Multi Access), LTE (Long Term Evolution), 5G, WLAN (Wireless LAN), Wi-Fi (Wireless-Fidelity), Bluetooth™ RFID (Radio Frequency Identification), Infrared Data Association (IrDA), ZigBee, NFC (Near Field Communication), and the like. 
     The input unit  120  may acquire various kinds of data. 
     Here, the input unit  120  may include a camera for inputting a video signal, a microphone for receiving an audio signal, and a user input unit for receiving information from a user. The camera or the microphone may be treated as a sensor, and the signal acquired from the camera or the microphone may be referred to as sensing data or sensor information. 
     The input unit  120  may acquire a training data for model learning and an input data to be used when an output is acquired by using learning model. The input unit  120  may acquire raw input data. Here, the processor  180  or the learning processor  130  may extract an input feature by preprocessing the input data. 
     The learning processor  130  may learn a model composed of an artificial neural network by using training data. The learned artificial neural network may be referred to as a learning model. The learning model may be used to an infer result value for new input data rather than training data, and the inferred value may be used as a basis for determination to perform a certain operation. 
     Here, the learning processor  130  may perform AI processing together with the learning processor  240  of the AI server  200 . 
     Here, the learning processor  130  may include a memory integrated or implemented in the AI apparatus  100 . Alternatively, the learning processor  130  may be implemented by using the memory  170 , an external memory directly connected to the AI apparatus  100 , or a memory held in an external device. 
     The sensing unit  140  may acquire at least one of internal information about the AI apparatus  100 , ambient environment information about the AI apparatus  100 , and user information by using various sensors. 
     Examples of the sensors included in the sensing unit  140  may include a proximity sensor, an illuminance sensor, an acceleration sensor, a magnetic sensor, a gyro sensor, an inertial sensor, an RGB sensor, an IR sensor, a fingerprint recognition sensor, an ultrasonic sensor, an optical sensor, a microphone, a lidar, and a radar. 
     The output unit  150  may generate an output related to a visual sense, an auditory sense, or a haptic sense. 
     Here, the output unit  150  may include a display unit for outputting time information, a speaker for outputting auditory information, and a haptic module for outputting haptic information. 
     The memory  170  may store data that supports various functions of the AI apparatus  100 . For example, the memory  170  may store input data acquired by the input unit  120 , training data, a learning model, a learning history, and the like. 
     The processor  180  may determine at least one executable operation of the AI apparatus  100  based on information determined or generated by using a data analysis algorithm or a machine learning algorithm. The processor  180  may control the components of the AI apparatus  100  to execute the determined operation. 
     To this end, the processor  180  may request, search, receive, or utilize data of the learning processor  130  or the memory  170 . The processor  180  may control the components of the AI apparatus  100  to execute the predicted operation or the operation determined to be desirable among the at least one executable operation. 
     When the connection of an external device is required to perform the determined operation, the processor  180  may generate a control signal for controlling the external device and may transmit the generated control signal to the external device. 
     The processor  180  may acquire intention information for the user input and may determine the user&#39;s requirements based on the acquired intention information. 
     The processor  180  may acquire the intention information corresponding to the user input by using at least one of a speech to text (STT) engine for converting speech input into a text string or a natural language processing (NLP) engine for acquiring intention information of a natural language. 
     At least one of the STT engine or the NLP engine may be configured as an artificial neural network, at least part of which is learned according to the machine learning algorithm. At least one of the STT engine or the NLP engine may be learned by the learning processor  130 , may be learned by the learning processor  240  of the AI server  200 , or may be learned by their distributed processing. 
     The processor  180  may collect history information including the operation contents of the AI apparatus  100  or the user&#39;s feedback on the operation and may store the collected history information in the memory  170  or the learning processor  130  or transmit the collected history information to the external device such as the AI server  200 . The collected history information may be used to update the learning model. 
     The processor  180  may control at least part of the components of AI apparatus  100  so as to drive an application program stored in memory  170 . Furthermore, the processor  180  may operate two or more of the components included in the AI apparatus  100  in combination so as to drive the application program. 
       FIG. 2  is a block diagram illustrating an AI server  200  according to an embodiment of the present invention. 
     Referring to  FIG. 2 , the AI server  200  may refer to a device that learns an artificial neural network by using a machine learning algorithm or uses a learned artificial neural network. The AI server  200  may include a plurality of servers to perform distributed processing, or may be defined as a 5G network. Here, the AI server  200  may be included as a partial configuration of the AI apparatus  100 , and may perform at least part of the AI processing together. 
     The AI server  200  may include a communication unit  210 , a memory  230 , a learning processor  240 , a processor  260 , and the like. 
     The communication unit  210  can transmit and receive data to and from an external device such as the AI apparatus  100 . 
     The memory  230  may include a model storage unit  231 . The model storage unit  231  may store a learning or learned model (or an artificial neural network  231   a ) through the learning processor  240 . 
     The learning processor  240  may learn the artificial neural network  231   a  by using the training data. The learning model may be used in a state of being mounted on the AI server  200  of the artificial neural network, or may be used in a state of being mounted on an external device such as the AI apparatus  100 . 
     The learning model may be implemented in hardware, software, or a combination of hardware and software. If all or part of the learning models are implemented in software, one or more instructions that constitute the learning model may be stored in memory  230 . 
     The processor  260  may infer the result value for new input data by using the learning model and may generate a response or a control command based on the inferred result value. 
       FIG. 3  is a view illustrating an AI system  1  according to an embodiment of the present invention. 
     Referring to  FIG. 3 , in the AI system  1 , at least one of an AI server  200 , a robot  100   a , a self-driving vehicle  100   b , an XR device  100   c , a smartphone  100   d , or a home appliance  100   e  is connected to a cloud network  10 . The robot  100   a , the self-driving vehicle  100   b , the XR device  100   c , the smartphone  100   d , or the home appliance  100   e , to which the AI technology is applied, may be referred to as AI apparatuses  100   a  to  100   e.    
     The cloud network  10  may refer to a network that forms part of a cloud computing infrastructure or exists in a cloud computing infrastructure. The cloud network  10  may be configured by using a 3G network, a 4G or LTE network, or a 5G network. 
     That is, the devices  100   a  to  100   e  and  200  configuring the AI system  1  may be connected to each other through the cloud network  10 . In particular, each of the devices  100   a  to  100   e  and  200  may communicate with each other through a base station, but may directly communicate with each other without using a base station. 
     The AI server  200  may include a server that performs AI processing and a server that performs operations on big data. 
     The AI server  200  may be connected to at least one of the AI apparatuses constituting the AI system  1 , that is, the robot  100   a , the self-driving vehicle  100   b , the XR device  100   c , the smartphone  100   d , or the home appliance  100   e  through the cloud network  10 , and may assist at least part of AI processing of the connected AI apparatuses  100   a  to  100   e.    
     Here, the AI server  200  may learn the artificial neural network according to the machine learning algorithm instead of the AI apparatuses  100   a  to  100   e , and may directly store the learning model or transmit the learning model to the AI apparatuses  100   a  to  100   e.    
     Here, the AI server  200  may receive input data from the AI apparatuses  100   a  to  100   e , may infer the result value for the received input data by using the learning model, may generate a response or a control command based on the inferred result value, and may transmit the response or the control command to the AI apparatuses  100   a  to  100   e.    
     Alternatively, the AI apparatuses  100   a  to  100   e  may infer the result value for the input data by directly using the learning model, and may generate the response or the control command based on the inference result. 
     Hereinafter, various embodiments of the AI apparatuses  100   a  to  100   e  to which the above-described technology is applied will be described. The AI apparatuses  100   a  to  100   e  illustrated in  FIG. 3  may be regarded as a specific embodiment of the AI apparatus  100  illustrated in  FIG. 1 . 
     &lt;AI+Robot&gt; 
     The robot  100   a , to which the AI technology is applied, may be implemented as a guide robot, a carrying robot, a cleaning robot, a wearable robot, an entertainment robot, a pet robot, an unmanned flying robot, or the like. 
     The robot  100   a  may include a robot control module for controlling the operation, and the robot control module may refer to a software module or a chip implementing the software module by hardware. 
     The robot  100   a  may acquire state information about the robot  100   a  by using sensor information acquired from various kinds of sensors, may detect (recognize) surrounding environment and objects, may generate map data, may determine the route and the travel plan, may determine the response to user interaction, or may determine the operation. 
     The robot  100   a  may use the sensor information acquired from at least one sensor among the lidar, the radar, and the camera so as to determine the travel route and the travel plan. 
     The robot  100   a  may perform the above-described operations by using the learning model composed of at least one artificial neural network. For example, the robot  100   a  may recognize the surrounding environment and the objects by using the learning model, and may determine the operation by using the recognized surrounding information or object information. The learning model may be learned directly from the robot  100   a  or may be learned from an external device such as the AI server  200 . 
     Here, the robot  100   a  may perform the operation by generating the result by directly using the learning model, but the sensor information may be transmitted to the external device such as the AI server  200  and the generated result may be received to perform the operation. 
     The robot  100   a  may use at least one of the map data, the object information detected from the sensor information, or the object information acquired from the external device to determine the travel route and the travel plan, and may control the driving unit such that the robot  100   a  travels along the determined travel route and travel plan. 
     The map data may include object identification information about various objects arranged in the space in which the robot  100   a  moves. For example, the map data may include object identification information about fixed objects such as walls and doors and movable objects such as pollen and desks. The object identification information may include a name, a type, a distance, and a position. 
     In addition, the robot  100   a  may perform the operation or travel by controlling the driving unit based on the control/interaction of the user. Here, the robot  100   a  may acquire the intention information of the interaction due to the user&#39;s operation or speech utterance, and may determine the response based on the acquired intention information, and may perform the operation. 
     &lt;AI+Self-Driving&gt; 
     The self-driving vehicle  100   b , to which the AI technology is applied, may be implemented as a mobile robot, a vehicle, an unmanned flying vehicle, or the like. 
     The self-driving vehicle  100   b  may include a self-driving control module for controlling a self-driving function, and the self-driving control module may refer to a software module or a chip implementing the software module by hardware. The self-driving control module may be included in the self-driving vehicle  100   b  as a component thereof, but may be implemented with separate hardware and connected to the outside of the self-driving vehicle  100   b.    
     The self-driving vehicle  100   b  may acquire state information about the self-driving vehicle  100   b  by using sensor information acquired from various kinds of sensors, may detect (recognize) surrounding environment and objects, may generate map data, may determine the route and the travel plan, or may determine the operation. 
     Like the robot  100   a , the self-driving vehicle  100   b  may use the sensor information acquired from at least one sensor among the lidar, the radar, and the camera so as to determine the travel route and the travel plan. 
     In particular, the self-driving vehicle  100   b  may recognize the environment or objects for an area covered by a field of view or an area over a certain distance by receiving the sensor information from external devices, or may receive directly recognized information from the external devices. 
     The self-driving vehicle  100   b  may perform the above-described operations by using the learning model composed of at least one artificial neural network. For example, the self-driving vehicle  100   b  may recognize the surrounding environment and the objects by using the learning model, and may determine the traveling route by using the recognized surrounding information or object information. The learning model may be learned directly from the self-driving vehicle  100   a  or may be learned from an external device such as the AI server  200 . 
     Here, the self-driving vehicle  100   b  may perform the operation by generating the result by directly using the learning model, but the sensor information may be transmitted to the external device such as the AI server  200  and the generated result may be received to perform the operation. 
     The self-driving vehicle  100   b  may use at least one of the map data, the object information detected from the sensor information, or the object information acquired from the external device to determine the travel route and the travel plan, and may control the driving unit such that the self-driving vehicle  100   b  travels along the determined travel route and travel plan. 
     The map data may include object identification information about various objects arranged in the space (for example, road) in which the self-driving vehicle  100   b  travels. For example, the map data may include object identification information about fixed objects such as street lamps, rocks, and buildings and movable objects such as vehicles and pedestrians. The object identification information may include a name, a type, a distance, and a position. 
     In addition, the self-driving vehicle  100   b  may perform the operation or travel by controlling the driving unit based on the control/interaction of the user. Here, the self-driving vehicle  100   b  may acquire the intention information of the interaction due to the user&#39;s operation or speech utterance, and may determine the response based on the acquired intention information, and may perform the operation. 
     &lt;AI+XR&gt; 
     The XR device  100   c , to which the AI technology is applied, may be implemented by a head-mount display (HMD), a head-up display (HUD) provided in the vehicle, a television, a mobile phone, a smartphone, a computer, a wearable device, a home appliance, a digital signage, a vehicle, a fixed robot, a mobile robot, or the like. 
     The XR device  100   c  may analyzes three-dimensional point cloud data or image data acquired from various sensors or the external devices, generate position data and attribute data for the three-dimensional points, acquire information about the surrounding space or the real object, and render to output the XR object to be output. For example, the XR device  100   c  may output an XR object including the additional information about the recognized object in correspondence to the recognized object. 
     The XR device  100   c  may perform the above-described operations by using the learning model composed of at least one artificial neural network. For example, the XR device  100   c  may recognize the real object from the three-dimensional point cloud data or the image data by using the learning model, and may provide information corresponding to the recognized real object. The learning model may be directly learned from the XR device  100   c , or may be learned from the external device such as the AI server  200 . 
     Here, the XR device  100   c  may perform the operation by generating the result by directly using the learning model, but the sensor information may be transmitted to the external device such as the AI server  200  and the generated result may be received to perform the operation. 
     &lt;AI+Robot+Self-Driving&gt; 
     The robot  100   a , to which the AI technology and the self-driving technology are applied, may be implemented as a guide robot, a carrying robot, a cleaning robot, a wearable robot, an entertainment robot, a pet robot, an unmanned flying robot, or the like. 
     The robot  100   a , to which the AI technology and the self-driving technology are applied, may refer to the robot itself having the self-driving function or the robot  100   a  interacting with the self-driving vehicle  100   b.    
     The robot  100   a  having the self-driving function may collectively refer to a device that moves for itself along the given route without the user&#39;s control or moves for itself by determining the route by itself. 
     The robot  100   a  and the self-driving vehicle  100   b  having the self-driving function may use a common sensing method so as to determine at least one of the travel route or the travel plan. For example, the robot  100   a  and the self-driving vehicle  100   b  having the self-driving function may determine at least one of the travel route or the travel plan by using the information sensed through the lidar, the radar, and the camera. 
     The robot  100   a  that interacts with the self-driving vehicle  100   b  exists separately from the self-driving vehicle  100   b  and may perform operations interworking with the self-driving function of the self-driving vehicle  100   b  or interworking with the user who rides on the self-driving vehicle  100   b.    
     Here, the robot  100   a  interacting with the self-driving vehicle  100   b  may control or assist the self-driving function of the self-driving vehicle  100   b  by acquiring sensor information on behalf of the self-driving vehicle  100   b  and providing the sensor information to the self-driving vehicle  100   b , or by acquiring sensor information, generating environment information or object information, and providing the information to the self-driving vehicle  100   b.    
     Alternatively, the robot  100   a  interacting with the self-driving vehicle  100   b  may monitor the user boarding the self-driving vehicle  100   b , or may control the function of the self-driving vehicle  100   b  through the interaction with the user. For example, when it is determined that the driver is in a drowsy state, the robot  100   a  may activate the self-driving function of the self-driving vehicle  100   b  or assist the control of the driving unit of the self-driving vehicle  100   b . The function of the self-driving vehicle  100   b  controlled by the robot  100   a  may include not only the self-driving function but also the function provided by the navigation system or the audio system provided in the self-driving vehicle  100   b.    
     Alternatively, the robot  100   a  that interacts with the self-driving vehicle  100   b  may provide information or assist the function to the self-driving vehicle  100   b  outside the self-driving vehicle  100   b . For example, the robot  100   a  may provide traffic information including signal information and the like, such as a smart signal, to the self-driving vehicle  100   b , and automatically connect an electric charger to a charging port by interacting with the self-driving vehicle  100   b  like an automatic electric charger of an electric vehicle. 
     &lt;AI+Robot+XR&gt; 
     The robot  100   a , to which the AI technology and the XR technology are applied, may be implemented as a guide robot, a carrying robot, a cleaning robot, a wearable robot, an entertainment robot, a pet robot, an unmanned flying robot, a drone, or the like. 
     The robot  100   a , to which the XR technology is applied, may refer to a robot that is subjected to control/interaction in an XR image. In this case, the robot  100   a  may be separated from the XR device  100   c  and interwork with each other. 
     When the robot  100   a , which is subjected to control/interaction in the XR image, may acquire the sensor information from the sensors including the camera, the robot  100   a  or the XR device  100   c  may generate the XR image based on the sensor information, and the XR device  100   c  may output the generated XR image. The robot  100   a  may operate based on the control signal input through the XR device  100   c  or the user&#39;s interaction. 
     For example, the user can confirm the XR image corresponding to the time point of the robot  100   a  interworking remotely through the external device such as the XR device  100   c , adjust the self-driving travel path of the robot  100   a  through interaction, control the operation or driving, or confirm the information about the surrounding object. 
     &lt;AI+Self-Driving+XR&gt; 
     The self-driving vehicle  100   b , to which the AI technology and the XR technology are applied, may be implemented as a mobile robot, a vehicle, an unmanned flying vehicle, or the like. 
     The self-driving driving vehicle  100   b , to which the XR technology is applied, may refer to a self-driving vehicle having a means for providing an XR image or a self-driving vehicle that is subjected to control/interaction in an XR image. Particularly, the self-driving vehicle  100   b  that is subjected to control/interaction in the XR image may be distinguished from the XR device  100   c  and interwork with each other. 
     The self-driving vehicle  100   b  having the means for providing the XR image may acquire the sensor information from the sensors including the camera and output the generated XR image based on the acquired sensor information. For example, the self-driving vehicle  100   b  may include an HUD to output an XR image, thereby providing a passenger with a real object or an XR object corresponding to an object in the screen. 
     Here, when the XR object is output to the HUD, at least part of the XR object may be outputted so as to overlap the actual object to which the passenger&#39;s gaze is directed. Meanwhile, when the XR object is output to the display provided in the self-driving vehicle  100   b , at least part of the XR object may be output so as to overlap the object in the screen. For example, the self-driving vehicle  100   b  may output XR objects corresponding to objects such as a lane, another vehicle, a traffic light, a traffic sign, a two-wheeled vehicle, a pedestrian, a building, and the like. 
     When the self-driving vehicle  100   b , which is subjected to control/interaction in the XR image, may acquire the sensor information from the sensors including the camera, the self-driving vehicle  100   b  or the XR device  100   c  may generate the XR image based on the sensor information, and the XR device  100   c  may output the generated XR image. The self-driving vehicle  100   b  may operate based on the control signal input through the external device such as the XR device  100   c  or the user&#39;s interaction. 
       FIG. 4  is a block diagram illustrating an AI apparatus  100  according to an embodiment of the present invention. 
     The redundant repeat of  FIG. 1  will be omitted below. 
     Hereinafter, the AI apparatus  100  may be referred to as an AI robot  100 , and the terms “AI apparatus” and “AI robot” may be used as the same meaning unless otherwise distinguished. 
     Referring to  FIG. 4 , the AI robot  100  may further include a driving unit  160 . 
     The input unit  120  may include a camera  121  for image signal input, a microphone  122  for receiving audio signal input, and a user input unit  123  for receiving information from a user. 
     Voice data or image data collected by the input unit  120  are analyzed and processed as a user&#39;s control command. 
     Then, the input unit  120  is used for inputting image information (or signal), audio information (or signal), data, or information inputted from a user and the AI apparatus  100  may include at least one camera  121  in order for inputting image information. 
     The camera  121  processes image frames such as a still image or a video obtained by an image sensor in a video call mode or a capturing mode. The processed image frame may be displayed on the display unit  151  or stored in the memory  170 . 
     The microphone  122  processes external sound signals as electrical voice data. The processed voice data may be utilized variously according to a function (or an application program being executed) being performed in the AI apparatus  100 . Moreover, various noise canceling algorithms for removing noise occurring during the reception of external sound signals may be implemented in the microphone  122 . 
     The user input unit  123  is to receive information from a user and when information is inputted through the user input unit  123 , the processor  180  may control an operation of the AI apparatus  100  to correspond to the inputted information. 
     The user input unit  123  may include a mechanical input means (or a mechanical key, for example, a button, a dome switch, a jog wheel, and a jog switch at the front, back or side of the AI apparatus  100 ) and a touch type input means. As one example, a touch type input means may include a virtual key, a soft key, or a visual key, which is displayed on a touch screen through software processing or may include a touch key disposed at a portion other than the touch screen. 
     The sensing unit  140  may be referred to as a sensor unit. 
     The output unit  150  may include at least one of a display unit  151 , a sound output module  152 , a haptic module  153 , or an optical output module  154 . 
     The display unit  151  may display (output) information processed in the AI apparatus  100 . For example, the display unit  151  may display execution screen information of an application program running on the AI apparatus  100  or user interface (UI) and graphic user interface (GUI) information according to such execution screen information. 
     The display unit  151  may be formed with a mutual layer structure with a touch sensor or formed integrally, so that a touch screen may be implemented. Such a touch screen may serve as the user input unit  123  providing an input interface between the AI apparatus  100  and a user, and an output interface between the AI apparatus  100  and a user at the same time. 
     The sound output module  152  may output audio data received from the wireless communication unit  110  or stored in the memory  170  in a call signal reception or call mode, a recording mode, a voice recognition mode, or a broadcast reception mode. 
     The sound output module  152  may include a receiver, a speaker, and a buzzer. 
     The haptic module  153  generates various haptic effects that a user can feel. A representative example of a haptic effect that the haptic module  153  generates is vibration. 
     The optical output module  154  outputs a signal for notifying event occurrence by using light of a light source of the AI apparatus  100 . An example of an event occurring in the AI apparatus  100  includes message reception, call signal reception, missed calls, alarm, schedule notification, e-mail reception, and information reception through an application. 
     The driving unit  160  may move the AI robot  100  in a specific direction or by a specific distance. 
     The driving unit  160  may include a left wheel driving unit  161  for driving a left wheel of the AI robot  100  and a right wheel driving unit  162  for driving a right wheel of the AI robot  100 . 
     The left wheel driving unit  161  may include a motor for driving the left wheel, and the right wheel driving unit  162  may include a motor for driving the right wheel. 
     In  FIG. 4 , an example in which the driving unit  160  includes the left wheel driving unit  161  and the right wheel driving unit  162  has been described, but the present invention is not limited thereto. That is, in one embodiment, the driving unit  160  may include only one wheel, or may include three or more wheels. 
       FIG. 5  is a flowchart illustrating a method for controlling a plurality of robots according to an embodiment of the present invention. 
     The AI server  200  may communicate with at least one robot or AI robot  100 , and the AI server  200  may determine the route of the robot or AI robot  100 . 
     That is, the AI server  200  may determine at least one route from a general robot having no AI function and the AI robot  100  having the AI function. 
     Hereinafter, unless otherwise specified, the robot includes an AI robot  100 . 
     Here, the AI server  200  may be a server constituting a control system for controlling at least one robot disposed in an airport or a building. The AI server  200  may control at least one robot. 
     Referring to  FIG. 5 , the processor  260  of the AI server  200  receives information including an operation and a deployment area corresponding to each of the plurality of robots included in the control area (S 501 ). 
     The control area may mean an entire area in which the AI server  200  provides a guidance service using robots, or may be referred to as an entire area. That is, the AI server  200  may determine the operation, the route, and the deployment area of the robot within the control area. 
     That is, the control area may mean the maximum activity range of the robot. 
     The control area may include a plurality of unit areas, and each of the unit areas may be set to a predetermined shape and size. 
     Here, each unit area may have a rectangular or square shape, but the present invention is not limited thereto. 
     Here, the area of each of the unit areas may be the same, but the present invention is not limited thereto. 
     The memory  230  of the AI server  200  may store location information indicating the location of each of the plurality of unit areas. The location information may be coordinates of the unit area. 
     The deployment area corresponding to the robot may mean a range in which the robot performs an operation. That is, each of the robots may move within its own deployment area and provide a service to users. 
     The deployment area may be composed of at least one unit area. 
     Here, if the information about the route and the deployment area for each of the plurality of robots is stored in the memory  230 , the processor  260  may receive the stored information about the route and the deployment area from the memory  230 . 
     Here, if the information about the route and the deployment area for each of the plurality of robots is not stored in the memory  230 , the processor  260  may receive the route and the deployment area for each of the robots through the communication unit  210 . 
     If the first operation corresponding to the first robot includes an operation for moving out of a first deployment area corresponding to the first robot, the processor  260  of the AI server  200  determines a first route corresponding to the first operation corresponding to the first robot (S 503 ). 
     The first robot may refer to a robot having an operation (first operation) for moving out of the deployment area (first placement area) set to the robot itself. 
     The first operation may refer to an operation corresponding to the first robot, and may be referred to as the first operation even if the operation corresponding to the first robot is changed to another operation. Similarly, the first deployment area may refer to the deployment area corresponding to the first robot, and may be referred to as the first deployment area, even if the deployment area corresponding to the first robot is changed to another area. 
     Here, if the information obtained from the robots includes first route information for the first operation, the processor  260  may determine the first route by extracting the first route information from the obtained information. 
     Here, if the information obtained from the robots does not include the first route information for the first operation, the processor  260  may determine the first route corresponding to the first operation. 
     Since the first operation includes the operation for moving out of the first deployment area, the determined first route includes a route for moving out of the first deployment area. 
     The processor  260  of the AI server  200  determines an area on the determined first route, in which at least one robot is deployed (S 505 ). 
     The determined area may refer to an area that is not the same as the first deployment area corresponding to the first robot. 
     Here, the determined area may refer to an area that does not overlap the first deployment area. 
     Here, the determined area may refer to an area adjacent to the first deployment area. That two areas are adjacent to each other may mean that at least some of the boundary lines of the two areas contact or meet each other. 
     The processor  260  of the AI server  200  determines the associated operation between the second robot and the first robot deployed in the determined area (S 507 ). 
     The second robot may refer to one of one or more robots deployed in the determined area. 
     Here, the second robot may be an idle robot that does not perform an operation. 
     Here, the second deployment area corresponding to the second robot may be adjacent to the second deployment area. 
     The second operation may refer to an operation corresponding to the second robot, and may be referred to as the second operation even if the operation corresponding to the second robot is changed to another operation. Similarly, the second deployment area may refer to the deployment area corresponding to the second robot, and may be referred to as the second deployment area, even if the deployment area corresponding to the second robot is changed to another area. 
     The associated operation refers to an operation in which the first robot and the second robot interwork with each other. 
     Here, the associated operation may include a first associated operation for transferring an operation between two robots or a second associated operation for switching a deployment area between two robots. 
     The first associated operation may mean that the second operation corresponding to the second robot moves the second robot to the first robot and takes over the first operation when the second robot encounters the first robot. 
     Here, according to the first associated operation, the second robot may take over the operation of the first robot when the second robot encounters the first robot, and the first robot may transfer the operation to the second robot and switch to an idle state. 
     Here, according to the first associated operation, the second robot may encounter the first robot and take over the operation of the first robot, and the first robot may return to the first deployment area. Returning to the first deployment area may refer to a state of moving to a predetermined point of the first deployment area, or moving or randomly moving according to a predetermined route within the first deployment area, and waiting for interaction with the user. 
     Here, according to the first associated operation, the first robot and the second robot may encounter at the boundary line between the first deployment area corresponding to the first robot and the second deployment area corresponding to the second robot, and may transfer their operations to each other at the boundary line. 
     In addition, according to the first associated operation, the second robot may take over the first operation corresponding to the first robot, and the processor  260  of the AI server  200  may determine whether the second operation corresponding to the second robot is out of the second deployment area, based on the operation taken over by the second robot. If the second operation (second operation after the takeover) corresponding to the second robot includes the operation for moving out of the second deployment area, the processor  260  may perform an operation for determining the associated operation (S 503  to S 509 ). 
     The second associated operation may refer to an operation for exchanging the second deployment area corresponding to the second robot and the first deployment area corresponding to the first robot. 
     That is, according to the second associated operation, the second robot is redeployed to the deployment area (first deployment area) corresponding to the first robot, and the first robot is redeployed to the deployment area (second deployment area) corresponding to the second robot. Accordingly, even if the first robot moves out of the first deployment area according to the first operation, the second robot is redeployed to the first deployment area corresponding to the first robot, thereby maintaining the number of robots deployed in the first deployment area. 
     Furthermore, according to the second associated operation, the first robot may be redeployed to the deployment area (second deployment area) corresponding to the second robot, and the processor  260  of the AI server  200  may determine again whether the first operation corresponding to the first robot is out of the redeployed deployment area (second deployment area), based on the redeployed deployment area. If the first operation corresponding to the first robot includes the operation for moving out of the redeployed deployment area, the processor  260  may perform an operation for determining the associated operation (S 503  to S 509 ). 
     The processor  260  may calculate the density for the first deployment area corresponding to the first robot, and determine the associated operation based on the calculated density. 
     Here, if the density calculated for the first deployment area exceeds a first reference value, the processor  260  may determine the associated operation as the first associated operation, and if the density calculated for the first deployment area does not exceed the first reference value, the processor  260  may determine the associated operation as the second associated operation. 
     This is because if the density for the first deployment area is high, the first robot can quickly provide a service to other users located in the first area when the first robot does not move out of its own deployment area (first deployment area). 
     Alternatively, the processor  260  may divide the density calculated for the first deployment area by the number of robots deployed in the first deployment area, and determine the associated operation based on whether the divided value exceeds a second reference value. 
     This is because even if the density for the first deployment area is high, when the density allocated to every robot deployed in the first deployment area is low, it is possible to quickly provide a serve to users even when the first robot moves out of its own deployment area (first deployment area). 
     Alternatively, even if the first robot is out of its deployment area (first deployment area), the processor  260  may calculate the time it takes for the second robot to fill the empty space, and determine the associated operation based on whether the calculated time exceeds a third reference value. 
     This is because if it does not take a long time for the second robot to be redeployed to the first deployment area even if the first robot moves out of the first deployment area, the first robot can quickly provide a service to users even if the first robot moves out of the deployment area (first deployment area). 
     The processor  260  may collect image data and speech data for the control area and calculate the density for each unit area included in the control area using the collected data. The density for the first deployment area may be calculated based on the density calculated for each unit area. 
     Here, the processor  260  may receive image data photographed for the control area from robots disposed in the control area or the camera looking at the control area, and may receive speech data uttered by the users, which is collected for the control area, from the robots disposed in the control area or the microphone installed in the control area. 
     The robots may include a camera or a microphone, and the camera looking at the control area may include a closed-circuit television (CCTV). 
     The processor  260  may calculate the number of users included in each unit area by using at least one of the received image data or the received speech data, and determine the calculated number of users as the density for each unit area. In addition, the processor  260  may calculate the density for the first deployment area based on the calculated density for each unit area. 
     Here, the processor  260  may extract the faces of the users included in the received image data, and determine the number of users included in each unit area based on the extracted number of faces. 
     Here, the processor  260  may extract the faces of the users from the image data using a image recognition model. The image recognition model may be an artificial neural network based model learned by using a deep learning algorithm or a machine learning algorithm. 
     Here, the processor  260  may determine the number of users included in each unit area by using the received speech data. 
     For example, if the received speech data is present, the processor  260  may extract a plurality of frequency bands from the received speech data, and determine the number of users included in each unit area based on the extracted number of frequency bands. 
     Here, the processor  260  may determine the number of users included in each unit area based on the sound volume of the received speech data. 
     The processor  260  of the AI server  200  updates the second operation corresponding to the second robot or the second deployment area corresponding to the second robot based on the determined associated operation (S 509 ). 
     As described above, if the determined associated operation is the first associated operation, the processor  260  may determine the second operation corresponding to the second robot as moving the second robot to the first robot and taking over the first operation corresponding to the first robot when the second robot encounters the first robot. 
     As described above, if the determined associated operation is the second associated operation, the processor  260  may exchange the first deployment area corresponding to the first robot and the second deployment area corresponding to the second robot. 
     In  FIG. 5 , the process for updating the operation or the deployment area corresponding to the second robot is illustrated as one cycle, and operations S 501  to S 509  illustrated in  FIG. 5  may be repeatedly performed. That is, operations S 501  to S 509  illustrated in  FIG. 5  may be repeatedly performed whenever the operations or the deployment areas of the plurality of robots included in the control area are changed. 
     For example, if the second operation corresponding to the second robot or the second deployment area corresponding to the second robot is updated according to operation S 509 , the AI server  200  may perform again operations S 501  to S 509  illustrated in  FIG. 5 . 
       FIG. 6  is a view illustrating the density measured for each unit area according to an embodiment of the present invention. 
     Referring to  FIG. 6 , the entire area  600  may include a plurality of unit areas. 
     In  FIG. 6 , the entire area  600  includes 25 unit areas  601  arranged in the form of 5×5, but the present invention is not limited thereto. 
     Each unit area  601  corresponds to a density measured for each unit area. 
     For example, the unit areas  601  constituting the first row of the entire area  600  have a measured density of [ 1 ,  3 ,  0 ,  2 ,  5 ]. 
     The memory  230  may store location information indicating the location of each unit area  601 . The location information of each unit area  601  may be center coordinates of each unit area. 
     The processor  260  may obtain location information of each unit area  601  by using a location measurement module such as a GPS module. 
       FIGS. 7 to 9  are views illustrating a method for calculating a density for a group area according to an embodiment of the present invention. 
       FIGS. 7 to 9  illustrate a method for calculating densities for group areas including a plurality of unit areas  601  using the example of the entire area  600  shown in  FIG. 6 . 
     Referring to  FIG. 7 , the entire area  600  having the form of 5×5 may be divided into four 4×4 group areas  710 ,  730 ,  750 , and  770 . 
     Each of the group areas  710  to  770  may include 16 unit areas in the form of 4×4. 
     The memory  230  may store location information indicating the location of each group area. The location information of each group area may be center coordinates of each group area. 
     The processor  260  may calculate an average value of the densities for the unit areas included in each group area, and determine the average value as the density for the corresponding group area. 
     The density for the first group area  710  may be an average value of the densities measured for the plurality of unit areas included in the first group area  710 . That is, the density for the first group area  710  may be calculated as (1+3+0+2+4+3+2+1+0+8+2+1+5+3+2+5)/16=2.625. 
     The density for the second group area  730  may be an average value of the densities measured for the plurality of unit areas included in the second group area  730 . That is, the density for the second group area  730  may be calculated as 3.375. 
     The density for the third group area  750  may be an average value of the densities measured for the plurality of unit areas included in the third group area  750 . That is, the density for the third group area  750  may be calculated as 2.813. 
     The density for the fourth group area  770  may be an average value of the densities measured for the plurality of unit areas included in the fourth group area  770 . That is, the density for the fourth group area  770  may be calculated as 3.250. 
     The calculated densities for the first to fourth group areas  710  to  770  may be used when the processor  260  determines the area or the route of the robot to which the robot should move preferentially within the entire area  600 . 
     In addition, the calculated densities for the first to fourth group areas  710  to  770  may be used when the processor  260  determines the priority between the respective group areas  710  to  770 . The priority may mean priority as a destination to which the robot should move. 
     For example, as the calculated density is greater, the higher priority may be given to the group area. 
     That is, within the entire area  600 , the second group area  730  may be determined as the first priority, the fourth group area  770  may be determined as the second priority, the third group area  750  may be determined as the third priority, and the first group area  710  may be determined as the fourth priority. 
     Furthermore, referring to  FIG. 8 , the 4×4 second group area  730  may be divided into four 3×3 subgroup areas  810 ,  830 ,  850 , and  870 . 
     In  FIG. 8 , only an example of dividing the second group area  730  into subgroup areas is illustrated, but not only the second group area  730  but also other first, third, and fourth group areas  710 ,  750 , and  770  may be divided into four 3×3 subgroup areas. 
     Each of the subgroup areas  810  to  870  may include 9 unit areas in the form of 3×3. 
     The memory  230  may store location information of each subgroup area. The location information of each subgroup area may be center coordinates of each subgroup area. 
     The processor  260  may calculate the density for each of the plurality of subgroup areas  810  to  870  constituting the second group area  730  so as to identify a more dense area within the second group area  730 . 
     The processor  260  may calculate an average value of the densities for the unit areas included in each subgroup area, and determine the average value as the density for the corresponding subgroup area. 
     The density for the first subgroup area  810  may be an average value of the densities measured for the plurality of unit areas included in the first subgroup area  810 . That is, the density for the first subgroup area  810  may be calculated as (3+0+2+3+2+1+8+2+1)/9=2.444. 
     The density for the second subgroup area  830  may be an average value of the densities measured for the plurality of unit areas included in the second subgroup area  830 . That is, the density for the second subgroup area  830  may be calculated as 3.111. 
     The density for the third subgroup area  850  may be an average value of the densities measured for the plurality of unit areas included in the third subgroup area  850 . That is, the density for the third subgroup area  850  may be calculated as 3.0. 
     The density for the fourth subgroup area  870  may be an average value of the densities measured for the plurality of unit areas included in the fourth subgroup area  870 . That is, the density for the fourth subgroup area  870  may be calculated as 3.333. 
     The calculated densities for the first to fourth subgroup areas  810  to  870  may be used when the processor  260  determines the area or the route of the robot to which the robot should move preferentially within the second group area  730 . 
     In addition, the calculated densities for the first to fourth subgroup areas  810  to  870  may be used when the processor  260  determines the priority between the respective subgroup areas  810  to  870 . The priority may mean priority as a destination to which the robot should move. 
     For example, as the calculated density is greater, the higher priority may be given to the subgroup area. 
     That is, within the second group area  730 , the fourth subgroup area  870  may be determined as the first priority, the second subgroup area  830  may be determined as the second priority, the third subgroup area  850  may be determined as the third priority, and the first subgroup area  810  may be determined as the fourth priority. 
     Further, referring to  FIG. 9 , the 3×3 fourth subgroup area  870  may be divided into four 2×2 lowest group areas  910 ,  930 ,  950 , and  970 . 
     In  FIG. 9 , only an example of dividing the fourth subgroup area  870  into the lowest group areas is illustrated, but not only the fourth subgroup area  870  but also the other first to third subgroup areas  810 ,  830 , and  850  may be divided into four 2×2 lowest group areas. 
     Each of the lowest group areas  910  to  970  may include four 2×2 unit areas. 
     The memory  230  may store location information of each lowest group area. The location information of each lowest group area may be center coordinates of each lowest group area. 
     The processor  260  may calculate the density for each of the plurality of lowest group areas  910  to  970  constituting the fourth subgroup area  870  so as to identify a more dense area within the fourth subgroup area  870 . 
     The processor  260  may calculate an average value of the densities of the unit areas included in each lowest group area, and determine the average value as the density for the lowest group area. 
     The density for the first lowest group area  910  may be an average value of the densities measured for the plurality of unit areas included in the first lowest group area  910 . That is, the density for the first lowest group area  910  may be calculated as (2+1+2+5)/4=2.5. 
     The density for the second lowest group area  930  may be an average value of the densities measured for the plurality of unit areas included in the second lowest group area  930 . That is, the density for the second lowest group area  930  may be calculated as 3.75. 
     The density for the third lowest group area  950  may be an average value of the densities measured for the plurality of unit areas included in the third lowest group area  950 . That is, the density for the third lowest group area  950  may be calculated as 1.5. 
     The density for the fourth lowest group area  970  may be an average value of the densities measured for the plurality of unit areas included in the fourth lowest group area  970 . That is, the density for the fourth lowest group area  970  may be calculated as 4.25. 
     The calculated densities for the first to fourth lowest group areas  910  to  970  may be used when the processor  260  determines the area or the route of the robot to which the robot should move preferentially within the fourth subgroup area  870 . 
     In addition, the calculated densities for the first to fourth lowest group areas  910  to  970  may be used when the processor  260  determines the priority between the respective lowest group areas  910  to  970 . The priority may mean priority as a destination to which the robot should move. 
     For example, as the calculated density is greater, the higher priority may be given to the lowest group area. 
     That is, within the fourth subgroup area  870 , the fourth lowest group area  970  may be determined as the first priority, the second lowest group area  930  may be determined as the second priority, the first lowest group area  910  may be determined as the third priority, and the third lowest group area  950  may be determined as the fourth priority. 
     As such, by dividing the area where the users are concentrated in the entire area  600 , it is possible to determine the routes of the robots that can effectively cope with a situation in which the user needs guidance. 
     Accordingly, the robots may move to areas where the users are concentrated, and provide the guidance services desired by the users. 
       FIG. 10  is a view illustrating a method for determining priorities among group areas having the same density according to an embodiment of the present invention. 
     Referring to  FIG. 10 , the calculated densities for the 3×3 fifth group area  1010  and the sixth lowest group area  1030  included in the entire area  600  are equal to 3.000, respectively. 
     As such, when the calculated densities for the group areas are the same, the processor  260  may calculate a sub-density average value for the group areas and compare the calculated sub-density average values to determine priority. 
     Here, the sub-density average value means the average value of the densities calculated for the immediately lower group areas included in the group area. The immediately lower group area may refer to group areas having a size smaller by one unit. 
     For example, when the sub-density for the 4×4 group area is calculated, the processor  260  may calculate the densities for the four 3×3 subgroup areas included in the group area, and calculate the sub-density average value by calculating an average value of the calculated densities. 
     In the example of  FIG. 10 , the processor  260  may determine the sub-density average values of the fifth group area  1010  and the sixth group area  1030  so as to determine the priority between the fifth group area  1010  and the sixth group area  1030 . 
     That is, the processor  260  may compare the average value ( 2 . 875 ) of the densities for the subgroup areas included in the fifth group area  1010  with the average value ( 3 . 438 ) of the densities for the subgroup areas included in the sixth group area  1030 . 
     Since the sub-density average value ( 3 . 438 ) of the sixth group area  1030  is larger than the sub-density average value ( 2 . 875 ) of the fifth group area  1010 , the processor  260  may give a higher priority to the sixth group area  1030  than to the fifth group area  1010 . 
     Alternatively, in a situation where the densities are the same among the group areas other than the lowest group area, the processor  260  may determine the priority among the group areas by using the densities for the adjacent unit areas as in a method of  FIG. 11  described below. 
       FIG. 11  is a view illustrating a method for determining priorities among group areas having the same density according to an embodiment of the present invention. 
       FIG. 11  illustrates a method for determining the priority among group areas, and may be applied when determining the priority among the lowest group areas in which the method for calculating the sub-density average value described with reference to  FIG. 10  is not applicable. It is apparent that the method for determining the priority in  FIG. 11  may also be applied when determining the priority among group areas other than the lowest group area. 
     Referring to  FIG. 11 , the calculated densities are equal to 4.000 for the 2×2 seventh group area  1110  and the 2×2 eighth group area  1130  included in the entire are 600, respectively. 
     As such, when the calculated densities for the group areas are the same, the processor  260  may calculate an adjacent density average value for the group areas and compare the calculated adjacent density average values to determine priority. 
     The adjacent density average value means the average value of the densities calculated for the unit areas adjacent to the group area. 
     Here, the unit areas adjacent to the group area may mean adjacent unit areas of up, down, left, and right positions adjacent to the group area, or may mean only adjacent unit areas of adjacent up, down, left, and right positions from the group area. 
     In the example of  FIG. 11 , the processor  260  may determine the adjacent density average values of the seventh group area  1110  and the eighth group area  1130  so as to determine the priority between the seventh group area  1110  and the eighth group area  1130 . 
     In one embodiment, the adjacent density average value of the seventh group area  1110  may be calculated as (0+2+1+7)/4=2.5, which is the average value of the densities for the unit areas adjacent in up, down, left, and right directions of the seventh group area  1110 . The adjacent density average value of the eighth group area  1130  may be calculated as (4+3+2+2+2+4)/6=2.833, which is the average value of the densities for the unit areas adjacent in up, down, left, and right directions of the eighth group area  1130 . 
     That is, the processor  260  may compare the average value ( 2 . 5 ) of the densities for the unit areas adjacent to the seventh group area  1110  and the average value ( 2 . 833 ) of the densities for the unit areas adjacent to the eighth group area  1130 . 
     Since the adjacent density average value ( 2 . 833 ) of the eighth group area  1130  is larger than the adjacent density average value ( 2 . 5 ) of the seventh group area  1110 , the processor  260  may give a higher priority to the eighth group area  1130  than to the seventh group area  1110 . 
       FIG. 12  is a view illustrating a situation in which an operation of a specific robot includes an operation of moving out of its deployment area. 
     Referring to  FIG. 12 , the control area may be divided into a plurality of areas  1211 ,  1212 , and  1213 . 
     A first robot  1201  is deployed in a first area  1211 , a second robot  1202  is deployed in a second area  1212 , and a third robot  1203  is deployed in a third area. 
     That is, the first deployment area corresponding to the first robot  1201  is the first area  1211 , the second deployment area corresponding to the second robot  1202  is the second area  1212 , and the third deployment area corresponding to the third robot  1203  is the third area  1213 . 
     The first robot  1201  may stop or move within the first area  1211  and may provide various services to users. Similarly, the second robot  1202  may stop or move within the second area  1212  and may provide various services to users, and the third robot  1203  may stop or move within the third area  1213  and may provide various services to users. 
     If a specific user  1220  utters to the first robot  1201  as follows: “Take me to destination  1230 ” ( 1221 ), the first operation corresponding to the first robot  1201  may be determined as an operation for moving to the destination  1230  together with the user  1220 . 
     Accordingly, a first route  1240  corresponding to the first operation may be determined as a route for starting from the first area  1211 , passing through the second area  1212 , and moving to the destination  1230  included in the third area  1213 . 
     If the first robot  1201  moves to the destination  1230  according to the first operation, the number of robots deployed in the first deployment area  1211  where the first robot  1201  has been deployed is reduced by one. 
     In particular, in the situation as illustrated in  FIG. 12 , there is no robot deployed in the first deployment area  1211 . Thus, an empty period that cannot provide a service to users may occur. 
       FIG. 13  is a view illustrating a method for controlling robots in the situation of  FIG. 12  based on a first associated operation according to an embodiment of the present invention. 
     Referring to  FIG. 13 , the processor  260  of the AI server  200  may determine the second operation corresponding to the second robot  1202  as moving the first robot  1201  to the second robot  1202  and taking over the first operation corresponding to the first robot  1201  when the second robot  1202  encounters the first robot  1201 , according to the first associated operation. 
     The first robot  1201  and the second robot  1202  may encounter at a boundary point  1350  of the first area  1211  and the second area  1212  where the first robot  1201  and the second robot  1202  are respectively deployed. To this end, the processor  260  may determine a first route of the first robot  1201  as a route  1341  for moving to the boundary point  1350 , and may determine a second route of the second robot  1202  as a route  1342  for moving to the boundary point  1350 . 
     If the second robot  1202  encounters the first robot  1201  at the boundary point  1350 , the processor  260  may update the second operation of the second robot  1202  with an operation for taking over the first operation of the first robot  1201  and moving to the destination  1230  together with the user  1220 . 
     Therefore, the first robot  1201  may transfer its own operation to the second robot  1202  without moving out of the first area  1211  where the first robot  1201  is deployed, and thus, the first robot  1201  may provide a service for the first area  1211  without interruption. 
     Although not illustrated in  FIG. 13 , if there is a crowded area in the first area  1211 , after the first robot  1201  transfers the operation to the second robot  1202 , the processor  260  may determine the first operation of the first robot  1201  as an operation for moving to the area where the crowd is concentrated in the first area  1211 . 
     According to  FIG. 13 , the second robot  1202  may take over the operation of the first robot  1201  and move to the destination  1230  located in the third area  1203  together with the user  1220 . Since the second operation corresponding to the second robot  1202  includes an operation for moving out of the second area  1212  that is the deployment area of the second robot  1202 , the processor  260  of the AI server  200  may determine the associated operation between the second robot  1202  and the third robot  1203  and control the robots as illustrated in  FIG. 14 or 15 . 
     In this case, the associated operation between the second robot  1202  and the third robot  1203  is not limited to the first associated operation. 
       FIG. 14  is a view illustrating a method for controlling robots in the situation of  FIG. 13  based on a first associated operation according to an embodiment of the present invention. 
     Referring to  FIG. 14 , the processor  260  of the AI server  200  may determine the third operation corresponding to the third robot  1203  as moving the third robot  1203  to the second robot  1202  and taking over the second operation corresponding to the second robot  1201  when the third robot  1203  encounters the second robot  1202 , according to the first associated operation. 
     The second robot  1202  and the third robot  1203  may encounter at a boundary point  1450  of the second area  1212  and the third area  1213  where the second robot  1202  and the third robot  1203  are respectively deployed. To this end, the processor  260  may determine a second route of the second robot  1201  as a route  1441  for moving to the boundary point  1450 , and may determine a third route of the third robot  1203  as a route  1442  for moving to the boundary point  1450 . 
     If the third robot  1203  encounters the second robot  1202  at the boundary point  1450 , the processor  260  may update the third operation of the third robot  1203  with an operation for taking over the second operation of the second robot  1202  and moving to the destination  1230  together with the user  1220 . 
     Therefore, the second robot  1202  may transfer its own operation to the third robot  1203  without moving out of the second area  1212  where the second robot  1202  is deployed, and thus, the second robot  1202  may provide a service for the second area  1212  without interruption. 
     Although not illustrated in  FIG. 14 , if there is a crowded area in the second area  1212 , after the second robot  1202  transfers the operation to the third robot  1203 , the processor  260  may determine the second operation of the second robot  1202  as an operation for moving to the area where the crowd is concentrated in the second area  1212 . 
       FIG. 15  is a view illustrating a method for controlling robots in the situation of  FIG. 13  based on a second associated operation according to an embodiment of the present invention. 
     Referring to  FIG. 15 , the processor  260  of the AI server  200  may exchange the second deployment area corresponding to the second robot  1202  and the third deployment area corresponding to the third robot  1203  according to the second associated operation. 
     That is, the processor  260  may set the second deployment area corresponding to the second robot  1202  as the third area  1213 , and may set the third deployment area corresponding to the third robot  1203  as the second area  1212 . 
     The processor  260  may determine the second route corresponding to the second robot  1202  as the route  1541  for moving to the third area  1213  and the route  1543  for moving from the third area  1213  to the destination  1230 , and may determine the third route corresponding to the third robot  1203  as the route  1542  for moving to the second area  1212 . 
     Therefore, since the third robot  1203  fills the empty space even if the second robot  1202  moves out of the second area  1212 , the third robot  1203  may provide a service while minimizing the empty space for the second area  1212 . 
       FIG. 16  is a view illustrating a method for controlling robots in the situation of  FIG. 12  based on a second associated operation according to an embodiment of the present invention. 
     Referring to  FIG. 16 , the processor  260  of the AI server  200  may exchange the first deployment area corresponding to the first robot  1201  and the second deployment area corresponding to the second robot  1202  according to the second associated operation. 
     That is, the processor  260  may set the first deployment area corresponding to the first robot  1201  as the second area  1212 , and may set the second deployment area corresponding to the second robot  1202  as the first area  1211 . 
     The processor  260  may determine the first route corresponding to the first robot  1201  as the route  1641  for moving to the second area  1212  and the route  1643  for moving from the second area  1212  to the destination  1230 , and may determine the second route corresponding to the second robot  1202  as the route  1642  for moving to the first area  1211 . 
     Therefore, since the second robot  1202  fills the empty space even if the first robot  1201  moves out of the first area  1211 , the second robot  1202  may provide a service while minimizing the empty space for the first area  1211 . 
     According to  FIG. 16 , the first robot  1201  and the second robot  1202  may replace the deployment areas with each other, and may move to the destination  1230  located in the third area  1203  together with the user  1220 . Since the first operation corresponding to the first robot  1201  includes an operation for moving out of the second area  1212  that is the deployment area of the first robot  1201 , the processor  260  of the AI server  200  may determine the associated operation between the first robot  1201  and the third robot  1203  and control the robots. 
       FIG. 17  is a flowchart illustrating a method for controlling a plurality of robots according to an embodiment of the present invention. 
     A description redundant to that provided above with reference to  FIG. 5  will be omitted. 
     Referring to  FIG. 17 , the processor  260  of the AI server  200  receives information including an operation and a deployment area corresponding to each of a plurality of robots included in a control area (S 1701 ). 
     This corresponds to operation S 501  of  FIG. 5 . 
     If a first operation corresponding to a first robot includes an operation for moving out of a first deployment area corresponding to the first robot, the processor  260  of the AI server  200  determines whether to perform the first operation by itself (S 1703 ). 
     The processor  260  may determine whether the first robot performs the first operation by itself, based on at least one of the time taken for the first robot to perform the first operation or the density for the first deployment area where the first robot is deployed. 
     For example, if the time taken for the first robot to perform the first operation exceeds a fourth reference value, or if the density for the first deployment area where the first robot is deployed exceeds a fifth reference value, the processor  260  may determine that the first robot does not perform the first operation by itself. 
     The processor  260  of the AI server  200  determines whether the first robot performs the first operation autonomously (S 1705 ). 
     The processor  260  may determine whether the first robot performs the first operation by itself, based on the result determined in operation S 1703 . 
     If it is determined in operation S 1705  that the first robot performs the first operation by itself, the processor  260  of the AI server  200  controls the first robot to perform the first operation (S 1707 ). 
     If it is determined in operation S 1705  that the first robot does not perform the first operation by itself, the processor  260  of the AI server  200  determines a first route corresponding to the first operation (S 1709 ). 
     This corresponds to operation S 503  of  FIG. 5 . 
     The processor  260  of the AI server  200  determines an area on the determined first route, in which at least one robot is deployed (S 1711 ). 
     This corresponds to operation S 505  of  FIG. 5 . 
     The processor  260  of the AI server  200  determines the associated operation between the second robot and the first robot deployed in the determined area (S 1713 ). 
     This corresponds to operation S 507  of  FIG. 5 . 
     The processor  260  of the AI server  200  updates the second operation corresponding to the second robot or the second deployment area corresponding to the second robot based on the determined associated operation (S 1715 ). 
     This corresponds to operation S 509  of  FIG. 5 . 
     In  FIG. 17 , the process for updating the operation or the deployment area corresponding to the second robot is illustrated as one cycle, and operations S 1701  to S 1715  illustrated in  FIG. 17  may be repeatedly performed. That is, operations S 1701  to S 1715  illustrated in  FIG. 17  may be repeatedly performed whenever the operations or the deployment areas of the plurality of robots included in the control area are changed. 
     For example, if the second operation corresponding to the second robot or the second deployment area corresponding to the second robot is updated according to operation S 1715 , the AI server  200  may perform again operations S 1701  to S 1715  illustrated in  FIG. 17 . 
     Meanwhile, in the above embodiment, although operations of  FIG. 5  and operations of  FIG. 17  are described as being performed by the AI server  200 , but may be performed by any one AI robot  100  or  100   a  among a plurality of robots. 
     In this case, any one AI robot  100  may be a master robot that can control other robots, the master robot may be a preset AI robot. 
     According to various embodiments of the present invention, even if the specific robot needs to perform an operation for moving out of the deployment area, it is possible to stably provide a service to users by removing a blank area in connection with other robots. 
     In addition, according to various embodiments of the present invention, it is possible to effectively distribute the load between the respective robots by transferring the operations or deployment areas between the robots in consideration of the density for the control area. 
     According to an embodiment of the present invention, the above-described method may be implemented as a processor-readable code in a medium where a program is recorded. Examples of a processor-readable medium may include read-only memory (ROM), random access memory (RAM), CD-ROM, a magnetic tape, a floppy disk, and an optical data storage device.