Patent Publication Number: US-11378966-B2

Title: Robot cleaner for recognizing stuck situation through artificial intelligence and method of operating the same

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application claims priority to Korean Patent Application No. 10-2019-0105364, filed on 27 Aug. 2019, in Korea, the entire contents of which is hereby incorporated by reference in its entirety. 
     BACKGROUND 
     The present disclosure relates to a robot cleaner for recognizing a stuck situation through artificial intelligence (AI). 
     A robot cleaner is an AI device to self-drive in a cleaning area without an operation of a user to suction foreign substances, such as dust, from the floor, thereby automatically performing cleaning. 
     Such a robot cleaner sets a cleaning route by recognizing the structure of a space and performs a cleaning operation along the set cleaning route. In addition, the robot cleaner performs cleaning according to a preset schedule or a user command. 
     In general, such a robot cleaner detects distances to obstacles such as furniture or office supplies, walls, etc. in a cleaning area, maps the cleaning area according to the distances, and controls driving of left and right wheels to perform obstacle avoidance operation. 
     A conventional robot cleaner stores the location of a detected stuck area and recognizes and avoids the stuck area based on the stored location of the stuck area. 
     However, when the environment of the stuck area is changed, for example, when an obstacle is removed from the stuck area, the robot cleaner based on location recognition may determine a cleaning area as a stuck area. 
     Therefore, there is a need for ability to actively cope with change in environment of the stuck area. 
     SUMMARY 
     The present disclosure is to provide a robot cleaner capable of detecting change in surrounding environment and accurately determining a stuck situation. 
     The present disclosure is to provide a robot cleaner capable of continuously training a model for inferring a stuck situation through self-validation. 
     A robot cleaner according to an embodiment of the present disclosure may convert 3D image data and a bumper event into surrounding map image data, infer the stuck situation of the robot cleaner from the 3D image data and the bumper event using the stuck situation recognition model, and control driving of the robot cleaner according to an inference result. 
     A robot cleaner according to an embodiment of the present disclosure may determine whether an error is detected in the inference result of the stuck situation recognition model, re-labels training data according to determination, and continuously update the stuck situation recognition model. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The present disclosure will become more fully understood from the detailed description given herein below and the accompanying drawings, which are given by illustration only, and thus are not limitative of the present disclosure, and wherein: 
         FIG. 1  illustrates an AI device  100  according to an embodiment of the present disclosure. 
         FIG. 2  illustrates an AI server  200  according to an embodiment of the present disclosure. 
         FIG. 3  illustrates an AI system  1  according to an embodiment of the present disclosure. 
         FIG. 4  illustrates an AI device  100  according to an embodiment of the present disclosure. 
         FIG. 5  a perspective view of an AI device  100  according to an embodiment of the present disclosure. 
         FIG. 6  a bottom view of an AI device  100  according to an embodiment of the present disclosure. 
         FIG. 7 a    is a side view of an artificial intelligence device according to another embodiment of the present disclosure, and  FIG. 7 b    is a bottom view of the artificial intelligence device. 
         FIG. 8  is a flowchart illustrating a method of operating a robot cleaner for avoiding a stuck situation according to an embodiment of the present disclosure. 
         FIGS. 9 a  to 9 e    are views illustrating a process of recognizing and avoiding a stuck situation at a robot cleaner according to an embodiment of the present disclosure. 
         FIGS. 10 and 11  are views illustrating a process of determining a rotation angle when a robot cleaner recognizes a stuck situation according to an embodiment of the present disclosure. 
         FIG. 12  is a view illustrating a method of operating an artificial intelligence device according to an embodiment of the present disclosure. 
         FIG. 13  is a view illustrating operation performed by an artificial intelligence device before evolution according to an embodiment of the present disclosure. 
         FIGS. 14 and 15  are views illustrating a training process performed during evolution of an artificial intelligence device according to an embodiment of the present disclosure. 
         FIG. 16  is a view illustrating a process performed after evolution of an artificial intelligence device according to an embodiment of the present disclosure. 
         FIG. 17  is a view illustrating a process of continuously training a context recognition model through error detection and self-validation at an artificial intelligence device according to an embodiment of the present disclosure, and  FIG. 18  is a view illustrating a detailed process of self-validation. 
         FIG. 19  is a view illustrating a method of operating a robot cleaner according to another embodiment of the present disclosure. 
         FIG. 20  is a view illustrating a generated surrounding map using surrounding map data according to an embodiment of the present disclosure. 
         FIG. 21  is a view illustrating a process of determining a time point when a robot cleaner labels surrounding map data with a stuck situation according to an embodiment of the present disclosure. 
         FIG. 22  is a view illustrating a process of training a stuck situation recognition model according to an embodiment of the present disclosure. 
         FIG. 23  is a view showing a process of performing self-validation with respect to a stuck situation recognition model at a robot cleaner according to an embodiment of the present disclosure, and  FIG. 24  is a view illustrating a detailed method of self-validation. 
         FIG. 25 a    is a view illustrating a coping method of a robot cleaner when reentering a stuck area based on the location of a stuck area according to the conventional technology, and  FIG. 25 b    is a view illustrating a coping method of a robot cleaner when reentering a stuck area based on recognition of a surrounding situation according to an embodiment of the present disclosure. 
     
    
    
     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 disclosure 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 learning data is given, and the label may mean the correct answer (or result value) that the artificial neural network must infer when the learning 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 learning 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. 
     At this time, 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  illustrates an AI device  100  according to an embodiment of the present disclosure. 
     The AI device (or an AI apparatus)  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 device  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 AI devices  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. 
     At this time, 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 learning 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. In this case, 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 learning 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 learning data, and the inferred value may be used as a basis for determination to perform a certain operation. 
     At this time, the learning processor  130  may perform AI processing together with the learning processor  240  of the AI server  200 . 
     At this time, the learning processor  130  may include a memory integrated or implemented in the AI device  100 . Alternatively, the learning processor  130  may be implemented by using the memory  170 , an external memory directly connected to the AI device  100 , or a memory held in an external device. 
     The sensing unit  140  may acquire at least one of internal information about the AI device  100 , ambient environment information about the AI device  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. 
     At this time, 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 device  100 . For example, the memory  170  may store input data acquired by the input unit  120 , learning data, a learning model, a learning history, and the like. 
     The processor  180  may determine at least one executable operation of the AI device  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 device  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 device  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 device  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 device  100  in combination so as to drive the application program. 
       FIG. 2  illustrates an AI server  200  according to an embodiment of the present disclosure. 
     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. At this time, the AI server  200  may be included as a partial configuration of the AI device  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 device  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 learning 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 device  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  illustrates an AI system  1  according to an embodiment of the present disclosure. 
     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 devices  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 devices 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 devices  100   a  to  100   e.    
     At this time, the AI server  200  may learn the artificial neural network according to the machine learning algorithm instead of the AI devices  100   a  to  100   e , and may directly store the learning model or transmit the learning model to the AI devices  100   a  to  100   e.    
     At this time, the AI server  200  may receive input data from the AI devices  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 devices  100   a  to  100   e.    
     Alternatively, the AI devices  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 devices  100   a  to  100   e  to which the above-described technology is applied will be described. The AI devices  100   a  to  100   e  illustrated in  FIG. 3  may be regarded as a specific embodiment of the AI device  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 . 
     At this time, 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 apparatus 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. At this time, 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 movement line 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 . 
     At this time, 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 apparatus 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. At this time, 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 . 
     At this time, 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 movement line without the user&#39;s control or moves for itself by determining the movement line 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.    
     At this time, 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 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. 
     At this time, 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  illustrates an AI device  100  according to an embodiment of the present disclosure. 
     The redundant repeat of  FIG. 1  will be omitted below. 
     Referring to  FIG. 4 , the AI device  100  may further include a driving unit  160  and a cleaning unit  190 . 
     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 mobile terminal  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 mobile terminal  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 mobile terminal  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 mobile terminal  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 called a sensor unit. 
     The sensing unit  140  may include at least one of a depth sensor (not illustrated) or an RGB sensor (not illustrated) to acquire image data for a surrounding of the AI robot  100 . 
     The depth sensor may sense that light irradiated from the light emitting unit (not illustrated) is reflected and return. The depth sensor may measure the difference between times at which the returning light is transmitted, an amount of the returning light, and a distance from an object. 
     The depth sensor may acquire information on a two dimensional image or a three dimensional image of the surrounding of the AI robot  100 , based on the distance from the object. 
     The RGB sensor may obtain information on a color image for an object or a user around the AI robot  100 . The information on the color image may be an image obtained by photographing an object. The RGB sensor may be named an RGB camera. 
     In this case, the camera  121  may refer to the RGB sensor. 
     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 mobile terminal  100 . For example, the display unit  151  may display execution screen information of an application program running on the mobile terminal  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 mobile terminal  100  and a user, and an output interface between the mobile terminal  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 mobile terminal  100 . An example of an event occurring in the mobile terminal  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 certain distance. 
     The driving unit  160  may include a left wheel driving unit  161  to drive the left wheel of the AI robot  100  and a right wheel driving unit  162  to drive the right wheel. 
     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. 
     Although the driving unit  160  includes the left wheel driving unit  161  and the right wheel driving unit  162  by way of example as in  FIG. 4 , but the present disclosure is not limited thereto. In other words, according to an embodiment, the driving unit  160  may include only one wheel. 
     The cleaning unit  190  may include at least one of a suction unit  191  or a mopping unit  192  to clean the floor around the AI device  100 . 
     The suction unit  191  may be referred to as a vacuum cleaning unit. 
     The suction unit  191  may suction air to suction foreign matters such as dust and garbage around the AI device  100 . 
     In this case, the suction unit  191  may include a brush or the like to collect foreign matters. 
     The mopping unit  192  may wipe the floor in the state that a mop is at least partially in contact with the bottom surface of the AI device  100 . 
     In this case, the mopping unit  192  may include a mop and a mop driving unit to move the mop. 
     In this case, the mopping unit  192  may adjust the distance from the ground surface through the mop driving unit. In other words, the mop driving unit may operate such that the mop makes contact with the ground surface when the mopping is necessary. 
       FIG. 5  a perspective view of the AI device  100  according to an embodiment of the present disclosure. 
     Referring to  FIG. 5 , the AI robot  100  may include a cleaner body  50  and a camera  121  or a sensing unit  140 . 
     The camera  121  or the sensing unit  140  may irradiate a light forward and receive the reflected light. 
     The camera  121  or the sensing unit  140  may acquire the depth information using the difference between times at which the received lights are returned. 
     The cleaner body  50  may include remaining components except the camera  121  and the sensing unit  140  described with reference to  FIG. 4 . 
       FIG. 6  is a bottom view of the AI device  100  according to an embodiment of the present disclosure. 
     Referring to  6 , the AI device  100  may further include a cleaner body  50 , a left wheel  61   a , a right wheel  61   b , and a suction unit  70  in addition to the components of  FIG. 4 . 
     The left wheel  61   a  and the right wheel  61   b  may allow the cleaner body  50  to travel. 
     The left wheel driving unit  161  may drive the left wheel  61   a  and the right wheel driving unit  162  may drive the right wheel  61   b.    
     As the left wheel  61   a  and the right wheel  61   b  are rotated by the driving unit  160 , the AI robot  100  may suction foreign matters such as dust and garbage through the suction unit  70 . 
     The suction unit  70  is provided in the cleaner body  50  to suction dust on the floor surface. 
     The suction unit  70  may further include a filter (not illustrate) to collect foreign matters from the sucked air stream and a foreign matter receiver (not illustrated) to accumulate foreign matters collected through the filter. 
     In addition to the components of  FIG. 4 , the AI robot  100  may further include a mopping unit (not illustrated). 
     The mopping unit (not illustrated) may include a damp cloth (not illustrated) and a motor (not illustrated) to rotate the damp cloth in contact with the floor and to move the damp cloth along a set pattern. 
     The AI device  100  may wipe the floor with the mopping unit (not illustrated). 
       FIG. 7 a    is a side view of an artificial intelligence device according to another embodiment of the present disclosure, and  FIG. 7 b    is a bottom view of the artificial intelligence device. 
     Hereinafter, the artificial intelligence device  100  may be referred to as a robot cleaner. 
     Referring to  FIGS. 7 a  and 7 b   , the robot cleaner  100  may further include a bumper  190  in addition to the components of  FIG. 4 . 
     The bumper  190  may be provided at the lower end of the main body of the robot cleaner  100 . The bumper  190  may include a cleaning unit  190  including the suction unit  191  and the mopping unit  192  shown in  FIG. 4 . 
     The bumper  190  may mitigate impact applied to the main body due to collision with an obstacle or another object while the robot cleaner  100  travels. 
     The bumper  190  may include one or more bumper sensors (not shown). The bumper sensor may measure the amount of impact applied to the bumper  190 . 
     The bumper sensor may generate a bumper event when a predetermined amount or more of impact is detected. The bumper event may be used to detect a stuck situation of the robot cleaner  100 . 
     In addition, each of the left wheel  61   a  and the right wheel  61   b  may include a wheel sensor. The wheel sensor may be an optical sensor for measuring the amount of rotation of the left wheel or the right wheel. The amount of rotation of the left wheel or the right wheel measured through the wheel sensor may be used to calculate the movement distance of the robot cleaner  100 . 
     One or more cliff sensors  193  may be provided at the lower surface of the bumper  190 . The cliff sensor  193  measures a distance between the floor and the cliff sensor  193  using a transmitted infrared signal and a reflected infrared signal. 
     The processor  180  may determine that the robot cleaner  100  reaches a staircase or a cliff when the measured distance is equal to or greater than a certain distance or when the reflected infrared signal is not detected for a certain time. 
       FIG. 8  is a flowchart illustrating a method of operating a robot cleaner for avoiding a stuck situation according to an embodiment of the present disclosure. 
     The processor  180  of the robot cleaner  100  controls the driving unit  160  such that the robot cleaner travels along a cleaning route (S 801 ). 
     The processor  180  determines whether a stuck situation is recognized while the robot cleaner travels (S 803 ). 
     In one embodiment, the processor  180  may recognize the stuck situation through the bumper sensor (not shown) provided in the bumper  190 . 
     The stuck situation may refer to a situation in which the robot cleaner  100  is stuck by an obstacle. 
     When the number of bumper events measured through the bumper sensor is equal to or greater than a predetermined number during a predetermined time, the processor  180  may determine that the robot cleaner  100  is in the stuck situation. 
     When the amount of impact applied to the bumper is equal to or greater than a predetermined amount of impact, the bumper sensor may generate the bumper event. The processor  180  may recognize the stuck situation based on the number of generated bumper events. 
     Upon determining that the stuck situation is recognized, the processor  180  determines a rotation angle of the robot cleaner  100  (S 805 ). 
     When the stuck situation of the robot cleaner  100  is recognized, the processor  180  may determine the rotation angle of the robot cleaner  100 , in order to avoid the robot cleaner  100  from the stuck situation. 
     The processor  180  may determine the rotation angle to avoid the stuck situation based on traveling angles and traveling speeds at time points before the stuck situation is recognized. 
     The processor  180  may determine the rotation angle to avoid the stuck situation, based on a plurality of traveling angles and a plurality of traveling speeds measured at a plurality of unit time intervals before the stuck situation is recognized. 
     A process of determining the rotation angle of the robot cleaner  100  when the stuck situation is recognized will be described below in detail. 
     The processor  180  controls the driving unit  160  such that the robot cleaner rotates by the determined rotation angle (S 807 ). 
     The processor  180  may control operation of the left wheel driving unit  161  and the right wheel driving unit  162  such that the robot cleaner rotates by the determined rotation angle. 
     The processor  180  may rotate the left wheel  61   a  and the right wheel  61   b  such that the robot cleaner rotates by the determined rotation angle. 
     The processor  180  may control operation of a left wheel motor for controlling the left wheel  61   a  and a right wheel motor for controlling the right wheel  61   b  such that the robot cleaner  100  rotates by the determined rotation angle. 
     The processor  180  may control the driving unit  160  such that the robot cleaner reversely rotates by the determined rotation angle, in order to reverse in a direction before recognizing the stuck situation. 
     Thereafter, the processor  180  controls the driving unit  160  such that the robot cleaner reverses by a certain distance (S 809 ). 
     The processor  180  may control the driving unit  160  such that the robot cleaner  100  moves backward by the certain distance after rotating by the determined rotation angle. 
     The robot cleaner  100  reverses by the certain distance in order to deviate from the stuck situation to avoid an obstacle. 
     The processor  180  generates a virtual wall at a front position of the robot cleaner  100  (S 811 ), after reversing the robot cleaner  100  by the certain distance. 
     The virtual wall may be a virtual wall on a cleaning map used to prevent the robot cleaner  100  from re-entering in the future. The virtual wall is not invisible to the human eyes and visible only in the field of view of the robot cleaner  100 . 
     The processor  180  may acquire the front position of the robot cleaner  100  and insert the virtual wall at the acquired front position, after the robot cleaner  100  reverses by the certain distance. 
     Thereafter, the processor  180  controls the driving unit  160  such that the robot cleaner travels along the changed cleaning route (S 813 ). 
     In one embodiment, the changed cleaning route may be a route excluding a route included in an area, in which the stuck situation is recognized, from a predetermined cleaning route. 
     That is, the processor  180  may change an existing cleaning route to a new cleaning route, in order to prevent the robot cleaner  100  from being in the stuck situation. 
     According to the embodiment of the present disclosure, the robot cleaner  100  may automatically recognize the stuck situation and rapidly avoid the stuck situation. Therefore, it is possible to decrease power consumption of the robot cleaner  100  and to increase cleaning performance. 
       FIGS. 9 a  to 9 e    are views illustrating a process of recognizing and avoiding a stuck situation at a robot cleaner according to an embodiment of the present disclosure. 
     Referring to  FIGS. 9 a  to 9 e   , a cleaning map  900  created by simultaneous localization and mapping (SLAM) is shown. 
     A cleaning route  910  of a robot cleaner identifier  901  for identifying the robot cleaner  100  on the cleaning map  900  is shown on the cleaning map  900 . 
       FIG. 9 a    shows recognition of the stuck situation by an obstacle  903  while the robot cleaner  100  travels along the cleaning route  910 . 
     The processor  180  may recognize the stuck situation using the bumper sensor provided in the bumper  190  of the robot cleaner  100 . 
     When the number of bumper events detected through the bumper sensor is equal to or greater than the predetermined number during the predetermined time, the processor  180  may recognize that the robot cleaner  100  is in the stuck situation. 
     The processor  180  may determine the rotation angle (a degrees) of the robot cleaner  100  when the stuck situation is recognized. 
     The processor  180  may determine an angle between a first traveling direction  911  before the stuck situation is recognized and a second traveling direction  913  when the stuck situation is recognized as the rotation angle. The method of determining the rotation angle will be described below in detail. 
     The processor  180  may rotate the robot cleaner  100  by the determined rotation angle (a degrees). Referring to  FIG. 9 b   , the robot cleaner identifier  901  rotates by the determined rotation angle (a degrees) in a direction before the stuck situation is recognized. 
     Thereafter, the processor  180  may control the driving unit  160  to reverse the robot cleaner  100  by the certain distance. Referring to  FIG. 9 c   , the robot cleaner identifier  901  reverses by the certain distance. 
     Here, the certain distance may be a distance at which the robot cleaner deviates from an obstacle. 
     After the robot cleaner  100  reverses by the certain distance, the processor  180  may insert a virtual wall  930  on the cleaning map  900 , as shown in  FIG. 9 d   . The processor  180  may insert the virtual wall  930  at the front position of the robot cleaner  100  on the cleaning map  900 , after reversing the robot cleaner  100  by the certain distance. 
     In another example, the processor  180  may insert the virtual wall  930  into an area including a line connecting the center of the obstacle  903  and the traveling route of the robot cleaner  100 , after reversing the robot cleaner  100  by the certain distance. 
     The virtual wall  930  may be inserted in order to prevent the robot cleaner  100  from being in the stuck situation in the future. 
     It is possible to prevent the entry route of the robot cleaner  100  from being blocked due to the virtual wall  930  reflected on the cleaning map  900 , thereby preventing the robot cleaner  100  from being in the stuck situation in the future. 
     Meanwhile, the processor  180  may drive the robot cleaner  100  along the new traveling route after reflecting the virtual wall  930  on the cleaning map  900 . 
     That is, as shown in  FIG. 9 e   , the robot cleaner identifier  901  may travel in the opposite direction of the virtual wall  930 . 
     Next, a process of determining the rotation angle in order to avoid the stuck situation when the robot cleaner recognizes the stuck situation will be described. 
       FIGS. 10 and 11  are views illustrating a process of determining a rotation angle when a robot cleaner recognizes a stuck situation according to an embodiment of the present disclosure. 
     Referring to  FIG. 11 , a first graph  1110  indicating the traveling angle of the robot cleaner  100  over time and a second graph  1130  indicating the traveling speed of the robot cleaner  100  over time are shown. 
     The robot cleaner  100  may measure the traveling angle and traveling speed of the robot cleaner  100  per unit time. The robot cleaner  100  may measure the traveling angle using a reference point and the rotation direction of the left wheel or the right wheel. The reference point may be a point of a ceiling or a point of the floor, but this is merely an example. 
     The robot cleaner  100  may measure the traveling speed of the robot cleaner  100  using the amount of rotation of the left wheel or the right wheel measured through the wheel sensor per unit time. 
     The unit time may be one second but this is merely an example. 
     Referring to  FIG. 10 , assume that the stuck situation of the robot cleaner  100  is recognized at a tenth time point t 10  among first time point t 1  to tenth time point t 10  while the robot cleaner identifier  901  travels along the traveling route  910  on the cleaning map  900 . 
     The processor  180  may acquire the traveling angle (the current traveling angle) of the robot cleaner  100  at the tenth time point t 10  when the stuck situation is recognized. 
     At the same time, the processor  180  may acquire the traveling angles of past time points before the tenth time point t 10 . 
     The processor  180  may calculate the rotation angle to avoid the stuck situation using the current traveling angle and an average value of the traveling angles of the past time points. 
     The processor  180  may calculate the rotation angles as shown in Equation 1 below. 
     
       
         
           
             
               
                 
                   
                     θ 
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                       θ 
                       n 
                     
                     - 
                     
                       
                         1 
                         N 
                       
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                           ∑ 
                           
                             k 
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                             1 
                           
                           N 
                         
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                           θ 
                           
                             n 
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                             k 
                           
                         
                       
                     
                   
                 
               
               
                 
                   [ 
                   
                     Equation 
                     ⁢ 
                     
                         
                     
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                     1 
                   
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     That is, the rotation angle θdiff may be obtained by subtracting the average angle of traveling angles θn-k measured at the past N time points from the current traveling angle θ. 
     Here, N may be 5, but this is merely an example. 
     However, at each of the sampled N time points, the traveling speed of the robot cleaner  100  should be equal to or greater than a threshold speed. 
     The reason why the traveling speed of the robot cleaner  100  is equal to or greater than the threshold speed at the sampled past time point is because, when the traveling speed of the robot cleaner  100  is less than the threshold speed, the robot cleaner  100  is already in the stuck situation or stays at a fixed position and thus it is difficult to use the rotation angles at the time points to determine the rotation angle for avoidance. 
     For example, when the number of sampled time points is 5, the fifth time point t 5  to the ninth time point t 9  before the tenth time point t 10  in which the stuck situation is recognized are checked. 
     Referring to the second graph  1130 , the traveling speeds of the robot cleaner  100  from the fifth time point t 5  to the eighth time point t 8  are equal to or greater than the threshold speed, but the traveling speed at the ninth time point t 9  is less than the threshold speed. 
     Accordingly, the ninth time point t 9  is not used as a time point used to calculate the rotation angle. 
     Since the traveling speed of the robot cleaner  100  is less than the threshold speed even at the third time point t 3  and the fourth time point t 4 , the third time point t 3  and the fourth time point t 4  are not used as samples for calculating the rotation angle. 
     As a result, since the traveling speed at the second time point is equal to or greater than the threshold speed, the processor  180  may determine the traveling angles at the second time point t 2  and the fifth time point t 5  to the eighth time point t 8  as a sampling target. 
     The processor  180  may determine a value obtained by subtracting the average angle of the traveling angles at the second time point t 2  and the fifth time point t 5  to the eighth time point t 8  from the current traveling angle, as the rotation angle. 
     The processor  180  may control the driving unit  160  to reversely rotate the robot cleaner by the determined rotation angle. 
     For example, when the current traveling angle is 45 degrees and the average angle of the traveling angles at the sampled time points is 0 degrees, the processor  180  may control the driving unit  160  to reversely rotate the robot cleaner  100  by 45 degrees (45-0). 
     According to the embodiment of the present disclosure, when the stuck situation of the robot cleaner  100  is detected, it is possible to change the rotation angle of the robot cleaner  100  to rapidly avoid the stuck situation. 
     Therefore, it is possible to prevent unnecessary waste of power of the robot cleaner  100  and to greatly improve the ability to cope with the stuck situation. 
       FIG. 12  is a view illustrating a method of operating an artificial intelligence device according to an embodiment of the present disclosure. 
     In particular,  FIG. 12  relates to a method of recognizing a situation through self-training at the artificial intelligence device  100 . 
     Referring to  FIG. 12 , the processor  180  of the artificial intelligence device  100  acquires sensing data through the sensing unit  140  (S 1201 ). 
     The processor  180  converts the acquired sensing data into context data (S 1203 ). 
     In one embodiment, the context data may indicate a surrounding situation related to the artificial intelligence device  100 . That is, the context data may be used to determine the surrounding situation of the artificial intelligence device  100 . 
     The surrounding situation of the artificial intelligence device  100  may be any one of a normal situation or an abnormal situation. 
     The abnormal situation may refer to a situation in which operation of the artificial intelligence device  100  is not normally performed and the normal situation may refer to a situation in which operation of the artificial intelligence device  100  is normally performed. 
     This will be described with reference to  FIG. 13 . 
       FIG. 13  is a view illustrating operation performed by an artificial intelligence device before evolution according to an embodiment of the present disclosure. 
     In  FIG. 13 , since a context recognition model is not trained, the artificial intelligence device  100  may not be evolved yet. 
     Referring to  FIG. 13 , the sensing unit  140  of the artificial intelligence device  100  may collect sensing data  1310 . 
     The processor  180  of the artificial intelligence device  100  may convert the collected sensing data  1310  into context data  1330 . 
     The converted context data  1330  may be stored in the memory  170 . 
       FIG. 12  will be described again. 
     The processor  180  determines whether the artificial intelligence device  100  has experienced the abnormal situation based on the converted context data (S 1205 ). 
     Upon determining that the artificial intelligence device  100  has experienced the abnormal situation, the processor  180  labels the converted context situation with the abnormal situation (S 1207 ). 
     That is, the context data may be labeled with the abnormal situation, and the context data and the labeling data labeled therewith may be used for training of the context recognition model. 
     That is, the context data and the abnormal situation may configure a training data set. 
     The processor  180  learns the context recognition model based on the context data and the labeling data (S 1209 ). 
     The context recognition model may be an artificial neural network based model subjected to supervised learning using a deep learning algorithm or a machine learning algorithm. 
     Here, as the artificial neural network of the context recognition model, any one of support vector machine (SVM) or a convolutional neural network (CNN) may be used. 
     The processor  180  or the learning processor  130  may perform supervised learning with respect to the context recognition model based on the context data and the labeling data configuring the training data set. 
     The context recognition model may be trained with the aim of accurately inferring the labeled abnormal or normal situation from given context data. 
     The process of training the context recognition model will be described with reference to the following drawings. 
       FIGS. 14 and 15  are views illustrating a training process performed during evolution of an artificial intelligence device according to an embodiment of the present disclosure. 
     In  FIGS. 14 and 15 , since the context recognition model  1430  is trained to autonomously grasp the context, the artificial intelligence device  100  may be being evolved. 
     Referring to  FIG. 14 , an auto labeler  1410  may label the context data stored in the memory  170 . The auto labeler  1410  may be included in any one of the processor  180  or the learning processor  130 . 
     The auto labeler  1410  may label the context data with the abnormal situation when the abnormal situation of the artificial intelligence device  100  is detected. 
     The context data and the abnormal situation labeled therewith may configure a training data set and may be used for supervised learning of the context recognition model  1430 . 
     Referring to  FIG. 15 , the context recognition model  1430  including an artificial neural network is shown. 
     The context recognition model  1430  may be subjected to supervised learning using context data and labeling data. 
     The processor  180  or the learning processor  130  may extract an input feature vector from the context data. The extracted input feature vector may be input to the context recognition model  1430 . 
     The processor  180  or the learning processor  130  may be trained to minimize a cost function indicating a difference between a target feature vector (or a target feature point) which is an inference result of the context recognition model  1430  and a current situation which is labeling data. 
     The cost function of the context recognition model  1430  may be expressed by a squared mean of a difference between a label for an operation situation of the artificial intelligence device  100  corresponding to training data and an operation situation inferred from each training data. 
     In the context recognition model  1430 , model parameters included in the artificial neural network may be determined to minimize the cost function through training. 
     The target feature point of the context recognition model  1430  may include an output layer of a single node indicating the normal or abnormal situation of the artificial intelligence device  100 . The target feature point may have a value of 1 when the target feature point indicates the normal situation and have a value of 0 when the target feature point indicates the abnormal situation. In this case, the output layer of the context recognition model  1430  may use sigmoid, hyperbolic tangent, etc. as an activation function. 
     In another example, the target feature point of the context recognition model  1430  may include output layers of two output nodes indicating the normal or abnormal situation of the artificial intelligence device  100 . 
     That is, the target feature point (target feature vector) may include a normal situation and an abnormal situation, may have a value “(1, 0)” when the target feature point indicates the normal situation, and have a value “(0, 1)” when the target feature point indicates the abnormal situation. In this case, the output layer of the context recognition model  1430  may use Softmax as an activation function. 
       FIG. 12  will be described again. 
     Thereafter, the processor  180  acquires new sensing data (S 1211 ), and infers whether a current situation is a normal situation or an abnormal situation from the acquired new sensing data using the trained context recognition model (S 1213 ). 
     The processor  180  performs action according to an inference result (S 1215 ). 
     The processor  180  may control one or more components configuring the artificial intelligence device  100  to perform operation corresponding to the normal situation, when the inference result is a normal situation. 
     The processor  180  may control one or more components configuring the artificial intelligence device  100  to perform operation corresponding to the abnormal situation, when the inference result is an abnormal situation. 
       FIG. 16  is a view illustrating a process performed after evolution of an artificial intelligence device according to an embodiment of the present disclosure. 
     In  FIG. 16 , the artificial intelligence device  100  may determine whether a current situation is a normal situation or an abnormal situation using the context recognition model  1430  in a state in which training of the context recognition model  1430  is completed. 
     Referring to  FIG. 16 , the artificial intelligence device  100  may acquire sensing data  1610  through the sensing unit  140 . The artificial intelligence device  100  may convert the acquired sensing data into context data. 
     The artificial intelligence device  100  may output an inference result  1650  indicating that the current situation is a normal situation or an abnormal situation from the context data using the context recognition model  1430 . 
     The artificial intelligence device  100  may perform an action  1670  according to the inference result  1650 . 
     Meanwhile, the artificial intelligence device  100  may perform error detection and self-validation with respect to the inference result. The artificial intelligence device  100  may continuously train the context recognition model  1430  through error detection and self-validation. 
     This will be described with reference to  FIGS. 17 and 18 . 
       FIG. 17  is a view illustrating a process of continuously training a context recognition model through error detection and self-validation at an artificial intelligence device according to an embodiment of the present disclosure, and  FIG. 18  is a view illustrating a detailed process of self-validation. 
     Referring to  FIG. 17 , the artificial intelligence device  100  may perform action  1670  according to the inference result  1650  and perform error correction  1730  for determining whether an error is detected with respect to the performed action  1670 . 
     Error detection  1730  may be included in self-validation  1710 . 
     The artificial intelligence device  100  may re-label the context data  1630  through the auto labeler  1410  when an error is detected with respect to the inference result. 
     For example, the artificial intelligence device  100  performed an existing action because the inference result is a normal situation, but an actual situation may be detected as an abnormal situation. 
     The artificial intelligence device  100  may label the context data  1630  with the abnormal situation to re-train the context recognition model  1430 . 
     The artificial intelligence device  100  performs an existing action performed before inference, when the inference result is a normal situation. Thereafter, the artificial intelligence device  100  may determine whether there is an error in determination of the normal situation. 
     That is, the artificial intelligence device  100  may not take further action when the error is not detected in the determined situation while the existing action is performed, after determining the normal situation. 
     The artificial intelligence device  100  may determine that an error occurs in determination of the normal situation, after determining the normal situation. That is, even though the artificial intelligence device  100  determines the normal situation, the abnormal situation may be detected when performing the existing situation. In this case, the artificial intelligence device  100  may re-label the context data used to determinate the normal situation with the abnormal situation and re-train the context recognition model  1430 . 
     Meanwhile, the artificial intelligence device  100  may perform the existing action as before the determination of the abnormal situation even upon determining that the inference result is an abnormal situation. The artificial intelligence device  100  may periodically perform the existing action as before the determination of the abnormal situation, even upon determining that the inference result is an abnormal situation. 
     The artificial intelligence device  100  may determine that an error is detected when a normal situation is detected while the existing action is performed under the abnormal situation. In this case, the artificial intelligence device  100  may re-label the context data used to determine the abnormal situation with the normal situation and re-train the context recognition model  1430 . 
     The artificial intelligence device  100  may determine that an error is not detected and correct inference is performed when the abnormal situation is detected again while the existing action is performed under the abnormal situation. In this case, the artificial intelligence device  100  may perform an action suitable for the abnormal situation. 
     According to the embodiment of the present disclosure, it is possible to perform self-validation with respect to the inference result of the context recognition model  1430  and to evolve the model in real time as re-training is performed. 
     Therefore, it is possible to improve the ability of the artificial intelligence device  100  to actively cope with the changed surrounding environment without user intervention. 
     Hereinafter, the embodiment of the present disclosure will be described in greater detail on the assumption that the artificial intelligence device  100  is a robot cleaner. 
       FIG. 19  is a view illustrating a method of operating a robot cleaner according to another embodiment of the present disclosure. 
     The processor  180  of the robot cleaner  100  acquires a three-dimensional data (hereinafter referred to as 3D data) and bumper event data through the sensing unit  140  while the robot cleaner travels along a cleaning route (S 1901 ). 
     The 3D sensor may be the depth sensor described with reference to  FIG. 4 . The 3D sensor may be provided on a front surface of the main body of the robot cleaner  100 . 
     When the 3D sensor is a depth sensor, light emitted from a light emitting unit (not shown) and reflected from an object may be detected. The depth sensor may measure a distance from the object based on a difference in time when the returned light is detected and the amount of returned light. 
     The depth sensor may acquire two-dimensional (2D) image data or 3D image data of the periphery of the robot cleaner  100  based on a measured distance between the objects. 
     A plurality of 3D sensors may be disposed on the front surface of the main body of the robot cleaner  100 . 
     Meanwhile, the robot cleaner  100  may acquire a bumper event through a bumper sensor provided in a bumper  190 . The bumper sensor may measure the amount of impact applied to the bumper and generate the bumper event data when the measured amount of impact is equal to or greater than a predetermined amount of impact. 
     The processor  180  of the robot cleaner  100  converts the acquired 3D data and bumper event into surrounding map data and generates a surrounding map (S 1903 ). 
     The surrounding map data may be used to create the surrounding map of the robot cleaner  100  on a cleaning map based on the current location of the robot cleaner  100 . 
     The processor  180  may store the converted surrounding map data and the surrounding map in the memory  170 . 
     Steps S 1901  and S 1903  may be performed after the user purchases the robot cleaner  100  and before the robot cleaner  100  experiences a stuck situation. 
     The surrounding map will be described with reference to  FIG. 20 . 
       FIG. 20  is a view illustrating a generated surrounding map using surrounding map data according to an embodiment of the present disclosure. 
     Referring to  FIG. 20 , the surrounding map  2000  generated by the surrounding map data is shown. 
     The surrounding map  2000  may be created by the surrounding map data on the cleaning map based on the current location of the robot cleaner  100 . 
     The processor  180  may convert the 3D data into first-color dots  2001  and  2003  indicating objects. 
     The processor  180  may convert the bumper event into second-color dots  2010  indicating an obstacle. 
     The processor  180  may generate the surrounding map  2000  including the converted first-color dots  2001  and  2003  and second-color dots  2010 . 
       FIG. 19  will be described again. 
     The processor  180  of the robot cleaner  100  determines whether the robot cleaner  100  has experienced a stuck situation based on the converted surrounding map data (S 1905 ). 
     The processor  180  may determine that the robot cleaner  100  has experienced the stuck situation, when the number of times of occurrence of the bumper event is equal to or greater than a predetermined number on the surrounding map  2000  shown in  FIG. 20 . 
     In another example, the processor  180  may determine that the robot cleaner  100  has experienced the stuck situation, when the robot cleaner  100  is located for a predetermined time on the surrounding map  2000  and the number of times of occurrence of the bumper event is equal to or greater than the predetermined number. 
     In another example, the processor  180  may determine that the robot cleaner  100  has experienced the stuck situation, when the number of times that the bumper event occurs for a certain time is equal to or greater than the predetermined number. 
     The processor  180  may store the surrounding map at a time point when the robot cleaner experiences the stuck situation in the memory  170  as image data. 
     The processor  180  of the robot cleaner  100  labels the surrounding map data with the stuck situation (S 1907 ), when the robot cleaner  100  has experienced the stuck situation. 
     The processor  180  may label the surrounding map data corresponding to the time point when the stuck situation is detected with the stuck situation in order to train the stuck situation recognition model. 
     That is, the surrounding map data or the surrounding map obtained in step S 1903  may be data for training the stuck situation recognition model. 
       FIG. 21  is a view illustrating a process of determining a time point when a robot cleaner labels surrounding map data with a stuck situation according to an embodiment of the present disclosure. 
     Referring to  FIG. 21 , a graph  2100  showing change in bumper event over time is shown. 
     When the number of times of occurrence of the bumper event measured for a certain time is less than the predetermined number, the processor  180  may label the surrounding map image data corresponding to the certain time with a non-stuck (normal) situation. 
     When the number of times of occurrence of the bumper event measured for a certain time is equal to or greater than the predetermined number, the processor  180  may label the surrounding map image data corresponding to the certain time with a stuck (wander) situation. 
     The processor  180  may divide the certain time into a plurality of unit times and label the surrounding map image data corresponding to each divided unit time with the stuck or non-stuck situation. Labeling may be performed by the auto labeler  1410  of  FIG. 14 . 
       FIG. 19  will be described again. 
     The processor  180  of the robot cleaner  100  trains the stuck situation recognition model using the surrounding map data and the labeling data labeled therewith (S 1909 ). 
     The stuck situation recognition model may be an example of the context recognition model of  FIG. 12 . 
     The stuck situation recognition model may refer to a model for inferring the stuck or non-stuck situation of the robot cleaner  100  from the surrounding map data. 
     The stuck situation recognition model may be an artificial neural network based model subjected to supervised learning using a deep learning algorithm or a machine learning algorithm. 
     Here, as the artificial neural network of the stuck situation recognition model, any one of support vector machine (SVM) or a convolutional neural network (CNN) may be used. 
     Steps S 1905  to S 1909  may be a process of self-training the stuck situation recognition model based on the robot cleaner  100  experiencing the stuck situation. 
     The process of training the stuck situation recognition model will be described with reference to the following drawing. 
       FIG. 22  is a view illustrating a process of training a stuck situation recognition model according to an embodiment of the present disclosure. 
     Referring to  FIG. 22 , an artificial neural network based stuck situation recognition model  2200  is shown. 
     The surrounding map data acquired by the robot cleaner  100  may be labeled with labeling data indicating the stuck situation. Labeling may be performed by the auto labeler shown in  FIG. 14 . 
     The surrounding map data and the stuck situation labeled therewith may configure a training set and may be used for supervised learning of the stuck situation recognition model  2200 . 
     The processor  180  may extract an input feature vector from the surrounding map data. The extracted input feature vector may be input to the stuck situation recognition model  2200 . 
     The processor  180  may be trained to minimize a cost function indicating a difference between a target feature vector (or a target feature point) which is an inference result of the stuck situation recognition model  2200  and a stuck situation which is labeling data. 
     The cost function of the stuck situation recognition model  2200  may be expressed by a squared mean of a difference between a label for the stuck situation of the robot cleaner  100  corresponding to training data and a situation inferred from each training data. 
     In the stuck situation recognition model  2200 , model parameters included in the artificial neural network may be determined to minimize the cost function through training. 
     The target feature point of the stuck situation recognition model  2200  may include an output layer of a single node indicating the normal or abnormal situation of the robot cleaner  100 . The target feature point may have a value of 1 when the target feature point indicates the stuck situation and have a value of 0 when the target feature point indicates non-stuck situation. In this case, the output layer of the stuck situation recognition model  2200  may use sigmoid, hyperbolic tangent, etc. as an activation function. 
     In another example, the target feature point of the stuck situation recognition model  2200  may include output layers of two output nodes indicating the stuck or non-stuck situation of the robot cleaner  100 . 
     That is, the target feature point (target feature vector) may include a stuck situation and a non-stuck situation, may have a value “(1, 0)” when the target feature point indicates the stuck situation, and have a value “(0, 1)” when the target feature point indicates the non-stuck situation. In this case, the output layer of the stuck situation recognition model  2200  may use Softmax as an activation function. 
       FIG. 19  will be described again. 
     The subsequent steps may be a process performed by the robot cleaner  100  after first training of the stuck situation recognition model  2200  is completed. 
     Thereafter, the processor  180  of the robot cleaner  100  acquires new 3D data and a new bumper event through the sensing unit  140  while the robot cleaner travels along the cleaning route (S 1911 ). 
     The processor  180  converts the acquired 3D data and bumper event into surrounding map data and generates a surrounding map (S 1913 ). 
     The processor  180  of the robot cleaner  100  infers whether the robot cleaner  100  is in the stuck situation from the generated surrounding map using the trained stuck situation recognition model (S 1913 ). 
     The processor  180  may determine whether the robot cleaner  100  is in the stuck situation from image data corresponding to the surrounding map using the stuck situation recognition model. 
     The processor  180  of the robot cleaner  100  performs S 805  of  FIG. 8  which is a step of avoiding the stuck situation and the subsequent steps thereof, when the stuck situation is inferred by the stuck situation recognition model (S 1915 ). 
     That is, the processor  180  may change the traveling angle of the robot cleaner  100  in order to prevent the stuck situation, when it is inferred that the robot cleaner  100  is in the stuck situation. For this, refer to the embodiment of  FIG. 8 . 
     The processor  180  of the robot cleaner  100  drives the robot cleaner along the existing cleaning route (SS 801  of  FIG. 8 ), when the non-stuck situation is inferred by the stuck situation recognition model (S 1915 ). 
     Meanwhile, the robot cleaner  100  may perform self-validation in order to reduce the error of the stuck situation recognition model  2200  or in order to cope with change in surrounding environment. As self-validation is performed, the stuck situation recognition model  2200  may be continuously updated. 
       FIG. 23  is a view showing a process of performing self-validation with respect to a stuck situation recognition model at a robot cleaner according to an embodiment of the present disclosure, and  FIG. 24  is a view illustrating a detailed method of self-validation. 
     Referring to  FIG. 23 , the processor  180  of the robot cleaner  100  acquires sensing data  2310  including 3D image data and a bumper event through the sensing unit  140 . 
     The processor  180  converts the acquired sensing data  2310  into surrounding map image data  2320 . 
     The converted surrounding map image data  2320  may be stored in the memory  170 . The surrounding map image data  2320  may be acquired at an interval of 1 ms but this is merely an example. 
     The processor  180  may infer the stuck situation or the normal situation from the surrounding map image data  2320  using the stuck situation recognition model  2200 , the first training of which is completed. 
     The processor  180  may perform self-validation  2350  with respect to the inferred stuck situation or normal situation (or non-stuck situation). 
     Self-validation  2350  may be performed in order to cope with change in surrounding environment or in order to reduce the error of the stuck situation recognition model  2200 . 
     Self-validation  2350  will be described with reference to  FIG. 24 . 
     First, the case where the inference result of the stuck situation recognition model  2200  is a normal situation (or a non-stuck situation) will be described. 
     When it is inferred that the robot cleaner  100  is in the stuck situation, the processor  180  may drive the robot cleaner  100  along the existing cleaning route. Thereafter, the processor  180  may determine whether the stuck situation of the robot cleaner  100  has occurred. 
     The processor  180  may determine that the stuck situation has occurred when the number of times of occurrence of the bumper event is equal to or greater than a predetermined number during a certain time. 
     The processor  180  may determine that the stuck situation has not occurred when the number of times of occurrence of the bumper event is less than the predetermined number during the certain time. 
     The processor  180  may not take further action when the stuck situation has not occurred. 
     The processor  180  may re-train the stuck situation recognition model  2200  when the stuck situation has occurred. 
     The processor  180  or the auto labeler  2330  may label the surrounding map image data used to determine the inference result of the non-stuck situation with the stuck situation. 
     The surrounding map image data and the labeling data may configure a training data set and may be used to re-train the stuck situation recognition model  2200 . 
     Meanwhile, the case where the inference result of the stuck situation recognition model  2200  is the stuck situation will be described. 
     The processor  180  may drive the robot cleaner  100  along the existing cleaning route when it is inferred that the robot cleaner  100  is in the struck situation. 
     Thereafter, the processor  180  may determine whether the stuck situation of the robot cleaner  100  has occurred. 
     The processor  180  may determine that an error is detected when the stuck situation has not occurred. In this case, the processor  180  may re-train the stuck situation recognition model  2200 . 
     The processor  180  or the auto labeler  2330  may label the surrounding map image data used to infer the stuck situation with the non-stuck situation. The surrounding map image data and the labeling data may configure a training data set and may be used to re-train the stuck situation recognition model  2200 . 
     Meanwhile, the processor  180  may determine that the inference result is correct when the stuck situation has occurred. In this case, the processor  180  may control the driving unit  160  in order to avoid the stuck situation. 
     Specifically, the processor  180  may determine the rotation angle of the robot cleaner  100  as in step S 805  of  FIG. 8  and reverse the robot cleaner after rotating by the determined rotation angle, thereby avoiding the stuck situation. For this, refer to the description of  FIG. 8 . 
     The robot cleaner  100  according to the embodiment of the present disclosure may perform self-validation with respect to the stuck situation recognition model  2200 , thereby continuously updating the stuck situation recognition model  2200 . 
     Therefore, the robot cleaner  100  may actively cope with change in surrounding situation. In addition, it is possible to increase accuracy of training model, by improving training errors of the stuck situation recognition model  2200 . 
     Next, the coping abilities of the robot cleaner  100  when the stuck area is determined based on the location and when the stuck area is determined based on surrounding situation recognition are compared. 
       FIG. 25 a    is a view illustrating a coping method of a robot cleaner when reentering a stuck area based on the location of a stuck area according to the conventional technology, and  FIG. 25 b    is a view illustrating a coping method of a robot cleaner when reentering a stuck area based on recognition of a surrounding situation according to an embodiment of the present disclosure. 
     First,  FIG. 25 a    will be described. The conventional robot cleaner  10  experiences a stuck situation through a wall  2510 , a first obstacle  2530  and a second obstacle  2550 . The robot cleaner  10  stores the location of the stuck area when experiencing the stuck situation. 
     When the robot cleaner  10  enters the stuck area in the future, the robot cleaner  10  determines that the stuck area is recognized if the second obstacle  2550  is maintained or even if the second obstacle  2550  is removed. That is, the robot cleaner  10  travels while avoiding the obstacle according to recognition of the stuck area. In this case, when the second obstacle  2550  is removed, the robot cleaner  10  recognizes the stuck area based on the location and travels for avoiding the stuck area. 
     Even though avoidance is unnecessary due to change in surrounding environment, the conventional robot cleaner  10  performs avoidance. Therefore, the stuck area cannot be cleaned. 
     Next,  FIG. 25 b    will be described. 
     The robot cleaner  100  according to the embodiment of the present disclosure experiences the stuck situation through a wall  2510 , a first obstacle  2530  and a second obstacle  2550 . 
     The robot cleaner  100  may label the surrounding map image data indicating the surrounding situation with the stuck situation when experiencing the stuck situation and train the stuck situation recognition model  2200 . 
     Thereafter, when the second obstacle  2550  is removed, the robot cleaner  100  enters the stuck area in which the robot cleaner has experienced the stuck situation. The robot cleaner  100  may infer the stuck situation from the re-acquired surrounding map image data using the stuck situation recognition model  2200 . 
     The robot cleaner  100  may recognize change in surrounding environment such as removal of the second obstacle  2550  and may not determine the stuck situation when entering the existing stuck area. 
     That is, the robot cleaner  100  may infer the current situation as the non-stuck situation from the re-acquired surrounding map image data using the stuck situation recognition model  2200  even when entering the existing stuck area. Therefore, unlike the location based method of  FIG. 25 a   , it is possible to more efficiently perform cleaning, by recognizing change in surrounding environment. 
     It may be determined that the robot cleaner  100  is in the stuck situation when entering the stuck area, even if the second obstacle  2550  has been removed. 
     The robot cleaner  100  may detect errors of determination of the stuck situation through the self-validation method described with reference to  FIG. 24  and re-train the stuck situation recognition model  2200 . 
     The robot cleaner  100  may not determine the stuck region when entering the stuck area in consideration of removal of the second obstacle  2550  according to re-training of the stuck situation recognition model  2200 . 
     According to the embodiment of the present disclosure, the robot cleaner  100  may recognize change in surrounding environment and determine the stuck area in consideration of change in surrounding environment. Accordingly, it is possible to greatly improve the ability to actively cope with change in surrounding environment and to more efficiently perform cleaning. 
     According to the embodiment of the present disclosure, since the robot cleaner travels while detecting change in the environment of the stuck area, the robot cleaner can actively cope with change in surrounding environment. 
     According to the embodiment of the present disclosure, since the robot cleaner continuously updates the stuck situation recognition mode, it is possible to accurately confirm the stuck situation and efficiently perform cleaning. 
     The present disclosure mentioned in the foregoing description can also be embodied as computer readable codes on a computer-readable recording medium. Examples of possible computer-readable mediums include HDD (Hard Disk Drive), SSD (Solid State Disk), SDD (Silicon Disk Drive), ROM, RAM, CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.