Patent Description:
An artificial intelligence (Al) system is a computer system that implements human-level intelligence, in which, unlike an existing rule-based smart system, the AI system trains by itself, makes decisions, and becomes increasingly smarter. As the Al system is used, the recognition rate is improved and a user's taste can be understood more accurately, and thus, the existing rule-based smart system is gradually being replaced by a deep learning-based Al system.

Al technology is configured by machine learning (deep learning) and element technology utilizing machine learning.

Machine learning is an algorithm technology for classifying/training the characteristics of input data autonomously, and element technology is a technology for simulating functions such as cognition and judgment of the human brain by using machine learning algorithms such as deep learning, and includes technical fields such as linguistic understanding, visual understanding, inference/prediction, knowledge expression, and motion control.

The various fields in which Al technology is applied are as follows. Linguistic understanding is a technology for recognizing and applying/processing human language/characters, and includes natural language processing, machine translation, conversation system, question and answer, and speech recognition/synthesis. Visual understanding is a technology for recognizing and processing objects as human vision, and includes object recognition, object tracking, image search, human recognition, scene understanding, spatial understanding, and image improvement. Inference prediction is a technology for logically inferring and predicting information by determining information, and includes knowledge/probability-based inference, optimization prediction, preference-based planning, and recommendation. Knowledge expression is a technology for automatically processing human experience information into knowledge data, and includes knowledge building (data generation/classification), knowledge management (data utilization), and so on. Motion control is a technology for controlling autonomous driving of a vehicle and movement of a robot, and includes motion control (navigation, collision, traveling), operation control (behavior control), and the like.

With the development of multi-media technology and network technology, users can be provided with various services using mobile robot devices.

However, in the related art, although a safety mechanism has been developed, there is a problem that the possibility of an accident due to a malfunction of the mobile robot device cannot be excluded. Accordingly, there is a need for a technology that allows a user to be provided with a service safely and effectively by controlling a mobile robot device in consideration of the large damage that may be caused by a malfunction of the mobile robot device. <CIT> relates to a trained human-intention classifier for safe and efficient robot navigation.

Provided are a mobile robot device and method for providing a service to a user using a training model trained using an artificial intelligence algorithm.

A mobile robot device that senses the surrounding environment while traveling can safely provide a service to a user.

Hereinafter, embodiments of the disclosure will be described in detail with reference to the accompanying drawings so that those skilled in the art to which the disclosure pertains may easily practice. However, the disclosure can be implemented in many different forms and is not limited to the embodiments described herein. In addition, in order to clearly describe the disclosure in the drawings, parts irrelevant to the description are omitted, and like reference numerals are assigned to similar parts throughout the specification.

Throughout the specification, when a part is "connected" to another part, this includes not only "directly connected" but also "electrically connected" with another element in between. In addition, when a part is described to "include" a certain component, this means that other components may be further included rather than excluding other components unless otherwise specified.

<FIG> is a diagram illustrating an example in which a mobile robot device <NUM> provides a service to a user according to some embodiments.

Referring to <FIG>, a mobile robot device <NUM> may provide a service to a user using an arm device. For example, the mobile robot device <NUM> may deliver coffee to the user. In addition, by sensing the surrounding environment, the mobile robot device <NUM> may change to a safety operation level suitable for the mobile robot device <NUM> to travel safely. For example, in case where there is a risk of an accident occurring around the mobile robot device <NUM> due to traveling of the mobile robot device <NUM>, the mobile robot device <NUM> may increase the safety operation level.

The safety operation level is a level for controlling an operation related to the movement of the mobile robot device <NUM> and the motion of the arm device, and may be expressed as a number, but is not limited thereto.

The mobile robot device <NUM> may change the safety operation level by using a trained model trained using an artificial intelligence algorithm. The trained model may correspond to a currently executed service among a plurality of trained models trained for each of a plurality of services provided by the mobile robot device <NUM>. The mobile robot device <NUM> can provide a safer and more efficient robot-traveling environment by controlling the movement of the mobile robot device <NUM> and the motion of the arm device so that the dangerous situation can be predicted and avoided in advance through the training of the dangerous situation.

In addition, the mobile robot device <NUM> may control the operation of the mobile robot device <NUM> based on the changed safety operation level. For example, the mobile robot device <NUM> may travel by changing a traveling route or change a moving speed based on the safety operation level.

The mobile robot device <NUM> may be a delivery robot, a cleaning robot, a home appliance, and other mobile or non-mobile computing devices. However, the disclosure is not limited thereto, and the mobile robot device <NUM> may include all kinds of devices capable of moving and providing services to users.

In addition, the mobile robot device <NUM> may communicate with an intermediate server <NUM> and other devices (not shown) through a predetermined network in order to change the safety operation level. In this case, the network includes a local area network (LAN), a wide area network (WAN), a value added network (VAN), a mobile radio communication network, a satellite communication network, and the mutual combination thereof, and is a data communication network in a comprehensive sense that enables each network entity to communicate with each other smoothly. In addition, the network may include wired Internet, wireless Internet, and mobile wireless communication networks. Wireless communication is, for example, wireless LAN (Wi-Fi), Bluetooth, Bluetooth low energy, ZigBee, Wi-Fi Direct (WFD), ultra wideband (UWB), infrared data association (IrDA), near field communication (NFC), and the like, but is not limited thereto.

<FIG> is a flowchart of a method of providing, by a mobile robot device <NUM>, a service to a user according to some embodiments.

In operation S200, a mobile robot device <NUM> may obtain sensing information obtained by sensing the surrounding environment of the mobile robot device <NUM> while the mobile robot device <NUM> is traveling. For example, the mobile robot device <NUM> may sense the surrounding environment in real time while traveling.

In an embodiment, the mobile robot device <NUM> may obtain at least one of an image captured around the mobile robot device <NUM>, or temperature information of a surrounding environment of the mobile robot device <NUM>. An example in which the mobile robot device <NUM> captures images of the surrounding will be described later in <FIG>.

In operation S210, the mobile robot device <NUM> may change, based on sensing information, a safety operation level of the mobile robot device <NUM>. The mobile robot device <NUM> may change the safety operation level by applying the obtained sensing information to a training model trained using an artificial intelligence algorithm. For example, the mobile robot device <NUM> is an artificial intelligence algorithm, and may change a safety operation level using a training model trained using at least one of machine learning, neural networks, genes, deep learning, or classification algorithms. For example, the mobile robot device <NUM> may change the safety operation level in a direction to enhance safety around the mobile robot device <NUM>. In addition, when it is determined that there is no dangerous situation around the mobile robot device <NUM>, the mobile robot device <NUM> may optimally provide a service providing operation of the mobile robot device <NUM> without changing the safety operation level.

An example in which the mobile robot device <NUM> changes the safety operation level based on sensing information according to an embodiment will be described later in <FIG> and <FIG>.

In operation S220, the mobile robot device <NUM> may control the operation of the mobile robot device <NUM> based on the changed safety operation level. In an embodiment, the mobile robot device <NUM> may control, based on a safety operation level, a moving speed and a moving direction of the mobile robot device <NUM>, a moving speed and a moving angle of an arm device included in the mobile robot device <NUM>, a moving noise of the mobile robot device <NUM>, a notification output of the mobile robot device <NUM>, and so on. However, it is not limited thereto, and the moving angle of the head portion of the mobile robot device <NUM> may be controlled.

An example of controlling, by the mobile robot device <NUM>, the operation of the mobile robot device <NUM> based on the changed safety operation level according to an embodiment will be described later in <FIG>.

<FIG> is a diagram illustrating an example in which a mobile robot device <NUM> captures surrounding images according to some embodiments.

Referring to <FIG>, while the mobile robot device <NUM> is traveling, the mobile robot device <NUM> may obtain a plurality of images <NUM> of the surrounding in real time. For example, the mobile robot device <NUM> may capture at least one of the front, rear, left, or right sides of the mobile robot device <NUM>.

In an embodiment, the mobile robot device <NUM> may determine in real time whether the surrounding environment is a dangerous situation by obtaining the plurality of images <NUM>. For example, by obtaining a plurality of images <NUM>, the mobile robot device <NUM> may identify the movement of an object located nearby.

In an embodiment, the mobile robot device <NUM> may store, in a memory, a plurality of images <NUM> captured while traveling, and when a preset capacity is exceeded, delete image data from old data.

<FIG> is a diagram illustrating an example in which a mobile robot device <NUM> changes a safety operation level using a single training model according to some embodiments.

Referring to <FIG>, the mobile robot device <NUM> may apply sensing information <NUM> that senses the surrounding environment of the mobile robot device <NUM> as input data of a training model <NUM>.

The training model <NUM> may be generated as a result of training criteria for determining a safety operation level based on the sensing information <NUM>. In this case, the training model <NUM> may be a model pre-built using an artificial intelligence algorithm. For example, the training model <NUM> may be a pre-built model to receive basic training data (e.g., a sample image) and output a safety operation level <NUM>.

The mobile robot device <NUM> may obtain a safety operation level <NUM> output as a result of inputting the sensing information <NUM> to the training model <NUM>. The mobile robot device <NUM> may change the safety operation level of the mobile robot device <NUM> to the safety operation level <NUM> output from the training model <NUM>. For example, if sensing information <NUM> that a person with a walking stick is walking, a temperature is high, and a slope of a traveling route is high is input to the training model <NUM>, the safety operation level <NUM> may be output as <NUM>.

<FIG> is a flowchart of a method of controlling an operation of a mobile robot device <NUM> according to some embodiments.

In operation S500, a mobile robot device <NUM> may obtain sensing information obtained by sensing the surrounding environment of the mobile robot device <NUM> while the mobile robot device <NUM> is traveling.

Because operation S500 corresponds to operation S200 of <FIG>, a detailed description will be omitted.

In operation S510, the mobile robot device <NUM> may obtain, based on sensing information, information about a type of person around the mobile robot device <NUM> and information about a surrounding environment of the mobile robot device <NUM>.

The mobile robot device <NUM> may obtain information about a person type and information about a surrounding environment by applying the obtained sensing information to a training model trained using an artificial intelligence algorithm. For example, the mobile robot device <NUM> is an artificial intelligence algorithm, and may obtain information about a person's type and information about a surrounding environment using a training model trained using at least one of machine learning, neural networks, genes, deep learning, or classification algorithms.

In <FIG>, it has been described that a safety operation level is output by inputting sensing information to a single training model, but is not limited thereto. For example, the safety operation level may be changed from sensing information using a plurality of training models as described later in <FIG>. In this case, by inputting sensing information in a training model for obtaining information about the type of person and information about the surrounding environment, information about the type of person and information about the surrounding environment may be output.

An example in which the mobile robot device <NUM> obtains, based on sensing information, information about the type of person and information about the surrounding environment according to an embodiment will be described later in <FIG>.

In operation S520, the mobile robot device <NUM> may change the safety operation level of the mobile robot device <NUM> based on information on the type of person and information on the surrounding environment.

The mobile robot device <NUM> may change the safety operation level by applying the obtained information about the type of person obtained and the obtained information on the surrounding environment to the training model trained using the artificial intelligence algorithm. For example, the mobile robot device <NUM> is an artificial intelligence algorithm, and may change the safety operation level using a training model trained using at least one of machine learning, neural networks, genes, deep learning, or classification algorithms.

In <FIG>, it has been described that a safety operation level is output by inputting sensing information to a single training model, but is not limited thereto. For example, the safety operation level may be changed from sensing information using a plurality of training models as described later in <FIG>. In this case, a safety operation level may be output by inputting information about a person type and/or information about a surrounding environment to a training model for determining a safety operation level.

In an embodiment, when the information about the type of person around the mobile robot device <NUM> indicates an infant, the mobile robot device <NUM> may be controlled to reduce the moving speed of the mobile robot device <NUM> and not change the angle of the arm device.

In an embodiment, when the information about the type of person around the mobile robot device <NUM> indicates a disabled person, the mobile robot device <NUM> may be controlled to reduce the moving speed of the mobile robot device <NUM> and increase the moving noise of the mobile robot device <NUM>.

In an embodiment, when the information about the surrounding environment of the mobile robot device <NUM> indicates that the congestion around the mobile robot device <NUM> is high, the mobile robot device <NUM> may be controlled to reduce the moving speed of the mobile robot device <NUM> and change the moving direction of the mobile robot device <NUM>.

In an embodiment, when information about the surrounding environment of the mobile robot device <NUM> indicates that the slope of the traveling route of the mobile robot device <NUM> is high, the mobile robot device <NUM> may be controlled to reduce the moving speed of the mobile robot device <NUM>. In addition, the mobile robot device <NUM> may control the operation of the mobile robot device <NUM> in consideration of the material of the floor of the traveling route as well as the slope of the traveling route.

In an embodiment, when the information about the surrounding environment of the mobile robot device <NUM> indicates that the risk of an object located around the mobile robot device <NUM> is high, the mobile robot device <NUM> may reduce the moving speed of the mobile robot device <NUM> and change the moving direction of the mobile robot device <NUM>.

An example in which the mobile robot device <NUM> changes a safety operation level based on the information about the type of person and the information about the surrounding environment according to an embodiment will be described later in <FIG>.

In operation S530, the mobile robot device <NUM> may control the operation of the mobile robot device <NUM> based on the changed safety operation level.

Because operation S530 corresponds to operation S220 of <FIG>, a detailed description will be omitted.

<FIG> is a diagram illustrating an example in which a mobile robot device <NUM> changes a safety operation level using a plurality of training models according to some embodiments.

Referring to <FIG>, unlike the single training model used in <FIG>, the mobile robot device <NUM> may change a safety operation level using a plurality of training models <NUM> and <NUM>.

In an embodiment, the mobile robot device <NUM> may apply sensing information <NUM> for which the surrounding environment of the mobile robot device <NUM> is sensed, as input data of a training model <NUM> for obtaining information about the type of person and information about the surrounding environment <NUM>.

The training model <NUM> to obtain information about the type of person and information about the surrounding environment <NUM> may be generated as a result of training, based on the sensing information <NUM>, the criteria for obtaining information about the type of person and information about the surrounding environment <NUM>. In this case, the training model <NUM> may be a model pre-built using an artificial intelligence algorithm. For example, the training model <NUM> may be a pre-built model for receiving basic training data (e.g., a sample image) and outputting information about the type of person and information about the surrounding environment <NUM>.

The mobile robot device <NUM> may obtain information about the type of person and information about the surrounding environment <NUM> output as a result of inputting sensing information <NUM> to the training model <NUM>. For example, the mobile robot device <NUM> may output, from the training model <NUM>, only information about the type of person or only information about the surrounding environment, and may output both information about the type of person and information about the environment.

In an embodiment, the mobile robot device <NUM> may apply the information about the type of person and information about the surrounding environment <NUM> output from the training model <NUM> as input data of a training model <NUM> for determining a safety operation level <NUM>. For example, the mobile robot device <NUM> may input, to the training model <NUM>, only information about the type of person or only information about the surrounding environment, and may input both information about the type of person and information about the environment.

The training model <NUM> for determining the safety operation level <NUM> may be generated as a result of training the criteria for determining the safety operation level <NUM> based on the information about the type of person and the information about the surrounding environment <NUM>. In this case, the training model <NUM> may be a model pre-built using an artificial intelligence algorithm. For example, the training model <NUM> is a model that is pre-built to receive basic training data (for example, information that an infant is located nearby and information that a slope of a traveling route is high), and output a safety operation level <NUM>.

The mobile robot device <NUM> may obtain a safety operation level <NUM> output as a result of inputting the information about the type of person and the information about the surrounding environment <NUM> to the training model <NUM>. The mobile robot device <NUM> may change the safety operation level of the mobile robot device <NUM> to the safety operation level <NUM> output from the training model <NUM>.

For example, when sensing information that a person with a walking stick is walking around and the temperature is high is input to the training model <NUM>, information about the type of person that a visually impaired person is moving nearby and information about the surrounding environment that congestion is high nearby <NUM> may be output. In addition, for example, when information about the type of person that a visually impaired person is moving nearby and information about the surrounding environment that congestion is high nearby <NUM> are input to the training model <NUM>, the safety operation level <NUM> may be output as <NUM>.

<FIG> is a diagram illustrating an example in which a mobile robot device <NUM> controls the operation of the mobile robot device <NUM> based on information about a person type according to some embodiments.

Referring to <FIG>, a mobile robot device <NUM> may control the operation of the mobile robot device <NUM> based on information about the type of person obtained through sensing information. For example, an image of the front of the mobile robot device <NUM> captured while the mobile robot device <NUM> is traveling may include an infant. Accordingly, when information about the type of person around the mobile robot device <NUM> indicates an infant, the moving speed of the mobile robot device <NUM> may be reduced. In addition, the movement of the mobile robot device <NUM> may be stopped. Moreover, it may be controlled such that the angle of an arm device remains unchanged. Further, it may also be controlled such that the angle of the arm device is not automatically changed, but is changed only by a user input. Alternatively, the angle of the arm device may be controlled to change by only <NUM> degrees, but is not limited thereto. For example, the mobile robot device <NUM> may be controlled to a preset operation according to a safety operation level. Alternatively, for example, the operation of the mobile robot device <NUM> may be controlled according to information about the type of person based on training according to preset criteria without changing the safety operation level.

<FIG> is a diagram illustrating an example in which a mobile robot device <NUM> controls the operation of the mobile robot device <NUM> based on information about the surrounding environment according to some embodiments.

Referring to <FIG>, a mobile robot device <NUM> may control the operation of the mobile robot device <NUM> based on information about the surrounding environment obtained through sensing information. For example, an image of the surroundings captured while the mobile robot device <NUM> traveling may include a glass wall. Accordingly, when information about the surrounding environment around the mobile robot device <NUM> indicates that the wall material is glass, the moving speed of the mobile robot device <NUM> may be reduced. In addition, the movement of the mobile robot device <NUM> may be stopped. In addition, when the mobile robot device <NUM> is traveling to approach the wall, it may be controlled such that the moving direction is changed. For example, the mobile robot device <NUM> may be controlled to a preset operation according to a safety operation level. Alternatively, for example, the operation of the mobile robot device <NUM> may be controlled according to information about the surrounding environment based on training according to preset criteria without changing the safety operation level.

<FIG> is a diagram illustrating a table showing an example of an operation of a mobile robot device according to a safety operation level according to some embodiments.

Referring to <FIG>, Table <NUM> may include a safety operation level field, a moving speed field, a moving direction field, an arm speed field, an arm angle field, a moving noise field, and a notification field.

In the safety operation level field, a level for controlling the movement of the mobile robot device <NUM>, the motion of the arm device, and an operation related to an increase in the safety may be recorded. For example, <NUM>, <NUM>, <NUM>, <NUM>, and N (for example, N is a natural number) may be recorded in the safety operation level field, but it is not limited thereto.

The moving speed field, the moving direction field, the arm speed field, the arm angle field, the moving noise field, and the notification field may include detailed operations of the mobile robot device <NUM> corresponding to a specific safety operation level. For example, when the safety operation level is <NUM>, the moving speed of the mobile robot device <NUM> may be <NUM>/min, the moving direction may be the front, the moving speed of the arm device may be <NUM>/sec, the angle of the arm device may be <NUM> degrees, and the moving noise of the mobile robot device <NUM> may be K dB (for example, K is an integer), and the notification method may be a display, but is not limited thereto. The operation of the mobile robot device <NUM> corresponding to the safety operation level may be set and changed based on training according to preset criteria.

<FIG> is a diagram illustrating an example in which a mobile robot device <NUM> provides a service to a user by interworking with a server <NUM> according to some embodiments.

Referring to <FIG>, a mobile robot device <NUM> may be connected to a server <NUM> through a network, and may provide a service to a user using data trained according to criteria preset by the server <NUM>.

In this case, the server <NUM> may perform at least one of a function of determining whether the vicinity of the mobile robot device <NUM> is a dangerous situation, a function of obtaining information about the type of people around the mobile robot device <NUM> and information about the surrounding environment of the mobile robot device <NUM>, or a function of changing a safety operation level, performed by the mobile robot device <NUM> in <FIG>.

In this case, the mobile robot device <NUM> and the server <NUM> may transmit and receive data necessary for each other in order to perform their own functions. For example, the mobile robot device <NUM> may provide data required for a predetermined function performed by the server <NUM> to the server <NUM>, and the mobile robot device <NUM> may receive result data generated according to a function performed by the server <NUM> from the server <NUM>. In addition, the server <NUM> may provide data required for a predetermined function performed by the mobile robot device <NUM> to the device <NUM>, and the server <NUM> may receive result data generated according to a function performed by the mobile robot device <NUM> from the mobile robot device <NUM>.

In addition, the server <NUM> may manage at least one of data necessary to determine whether the surroundings of the mobile robot device <NUM> are in a dangerous situation, data necessary to obtain information about the type of person around the mobile robot device <NUM> and information about the surrounding environment of the mobile robot device <NUM>, or data necessary to change a safety operation level.

<FIG> and <FIG> are block diagrams of a mobile robot device <NUM> according to some embodiments.

As illustrated in <FIG>, a mobile robot device <NUM> according to some embodiments may include a sensing unit <NUM>, a processor <NUM>, an arm device <NUM>, and a mobile device <NUM>. However, not all of the components shown in <FIG> are essential components of the mobile robot device <NUM>. The mobile robot device <NUM> may be implemented by more components than those illustrated in <FIG>, or the mobile robot device <NUM> may be implemented by fewer components than those illustrated in <FIG>.

For example, as illustrated in <FIG>, the mobile robot device <NUM> according to some embodiments may further include an output unit <NUM>, a memory <NUM>, an input unit <NUM>, and a communication interface <NUM> in addition to the sensing unit <NUM>, the processor <NUM>, the arm device <NUM>, and mobile device <NUM>.

According to an embodiment, the sensing unit <NUM> may obtain sensing information obtained by sensing the surrounding environment of the mobile robot device <NUM> while the mobile robot device <NUM> is traveling. For example, the sensing unit <NUM> may obtain at least one of an image captured around the mobile robot device <NUM> or temperature information of the surrounding environment. In addition, while the mobile robot device <NUM> is traveling, the sensing unit <NUM> may obtain location information of the mobile robot device <NUM>.

The sensing unit <NUM> may include a plurality of sensors configured to sense the surrounding environment of the mobile robot device <NUM>. For example, sensing unit <NUM> may include an image sensor <NUM>, such as a camera, to capture an image of the surroundings of the mobile robot device <NUM>. In addition, the sensing unit <NUM> may include a temperature/humidity sensor <NUM> to obtain temperature information and/or humidity information of the surrounding environment of the mobile robot device <NUM>.

Further, the sensing unit <NUM> may include sensors for obtaining location information of the mobile robot device <NUM>. For example, the sensor <NUM> may include distance sensors, such as an RADAR sensor <NUM>, a LIDAR sensor <NUM>, and an odometery sensor <NUM>.

In addition, the sensor <NUM> may include one or more actuators configured to correct the position and/or orientation of a plurality of sensors, so that images of the direction of each of the front, rear, and sides of the mobile robot device <NUM> may be captured.

The sensor <NUM> may include a plurality of sensors configured to sense information about the surrounding environment in which the mobile robot device <NUM> is located, and may include one or more actuators configured to modify the position and/or orientation of the sensors. For example, the sensing unit <NUM> may include a global positioning system (GPS) <NUM>, an inertial measurement unit (IMU) <NUM>, an RADAR sensor <NUM>, a LIDAR sensor <NUM>, an image sensor <NUM>, and an odometery sensor <NUM>. Further, the sensing unit <NUM> may include at least one of a temperature/humidity sensor <NUM>, an infrared sensor <NUM>, an air pressure sensor <NUM>, a proximity sensor <NUM>, or an RGB sensor (illuminance sensor) <NUM>, but is not limited thereto. Because the function of each sensor can be intuitively deduced, by a person skilled in the art from the name, a detailed description will be omitted.

In addition, the sensing unit <NUM> may include a motion sensing unit <NUM> capable of sensing the motion of the mobile robot device <NUM>. The motion sensing unit <NUM> may include a magnetic sensor <NUM>, an acceleration sensor <NUM>, and a gyroscope sensor <NUM>.

The GPS <NUM> may be a sensor configured to estimate the geographic location of the mobile robot device <NUM>. That is, the GPS <NUM> may include a transceiver configured to estimate the location of the mobile robot device <NUM> relative to the Earth.

The IMU <NUM> may be a combination of sensors configured to sense changes in position and orientation of the mobile robot device <NUM> based on inertial acceleration. For example, a combination of sensors may include accelerometers and gyroscopes.

The RADAR sensor <NUM> may be a sensor configured to detect objects in an environment where the mobile robot device <NUM> is located using a wireless signal. Further, the RADAR sensor <NUM> may be configured to detect the speed and/or direction of objects.

The LIDAR sensor <NUM> may be a sensor configured to detect objects in an environment where the mobile robot device <NUM> is located using a laser. More specifically, the LIDAR sensor <NUM> may include a laser light source configured to emit a laser and/or a laser scanner, and a detector configured to detect reflection of a laser. The LIDAR sensor <NUM> may be configured to operate in a coherent (e.g., using heterodyne detection) or incoherent detection mode.

The image sensor <NUM> may be a still camera or a video camera configured to record the environment outside the mobile robot device <NUM>. For example, the image sensor <NUM> may include a plurality of cameras, and the plurality of cameras may be positioned at a plurality of locations on mobile robot device <NUM>, respectively.

The odometery sensor <NUM> may estimate the location of the mobile robot device <NUM> and measure the moving distance. For example, the odometery sensor <NUM> may measure a position change value of the mobile robot device <NUM> using the number of rotations of a wheel of the mobile robot device <NUM>.

The input unit <NUM> means a means for inputting data for controlling the mobile robot device <NUM>. For example, the input unit <NUM> may include a key pad, a dome switch, a touch pad (contact capacitive type, pressure resistive film type, infrared detect type, surface ultrasonic conduction type, integral tension measurement type, piezo effect type, and so on), a jog wheel, a jog switch, and the like, but is not limited thereto. Further, the input unit <NUM> may include a microphone, and the microphone may be configured to receive audio (e.g., a voice command) from a user.

The output unit <NUM> may output an audio signal or a video signal, and the output unit <NUM> may include a display <NUM> and an audio output unit <NUM>.

The display <NUM> may display and output information processed by the mobile robot device <NUM>. For example, the display <NUM> may display a notification message informing a dangerous situation to a person located around the mobile robot device <NUM> while the mobile robot device <NUM> is traveling. In addition, the display <NUM> may display a user interface for performing an action related to a notification.

The display <NUM> may include at least one of a liquid crystal display, a thin film transistor-liquid crystal display, an organic light-emitting diode, a flexible display, a 3D display, or an electrophoretic display. Depending on the implementation form of the output unit <NUM>, the output unit <NUM> may include two or more displays <NUM>.

The audio output unit <NUM> outputs audio data received from the communication interface <NUM> or stored in the memory <NUM>. For example, the audio output unit <NUM> may output a notification message informing a dangerous situation to a person located around the mobile robot device <NUM> as a sound while the mobile robot device <NUM> is traveling. Further, the audio output unit <NUM> may include a speaker, a buzzer, and the like.

The input unit <NUM> and the output unit <NUM> may include a network interface, and may be implemented as a touch screen.

The communication interface <NUM> may include at least one antenna for wireless communication with other devices. For example, the communication interface <NUM> may be used to wirelessly communicate with a cellular network or other wireless protocols and systems via Wi-Fi or Bluetooth. The communication interface <NUM> controlled by the processor <NUM> may transmit and receive wireless signals. For example, the processor <NUM> may execute a program included in the memory <NUM> in order for the communication interface <NUM> to transmit and receive wireless signals to and from a cellular network.

The processor <NUM> generally controls the overall operation of the mobile robot device <NUM>. For example, the processor <NUM> may overall control the sensing unit <NUM>, the output unit <NUM>, the input unit <NUM>, the communication interface <NUM>, the arm device <NUM>, and the mobile device <NUM> by executing programs stored in the memory <NUM>. Further, the processor <NUM> may perform the functions of the mobile robot device <NUM> described in <FIG> by executing programs stored in the memory <NUM>.

Specifically, the processor <NUM> may obtain, through the sensing unit <NUM>, sensing information obtained by sensing the surrounding environment of the mobile robot device <NUM>. For example, the sensing information may include at least one of an image captured around the mobile robot device <NUM> or temperature information of the surrounding environment.

In an embodiment, the processor <NUM> may change a safety operation level of the mobile robot device <NUM> by applying sensing information to a training model trained using an artificial intelligence algorithm. In addition, the processor <NUM> may control the operation of the mobile robot device <NUM> based on a safety operation level.

In an embodiment, when the information about the type of person around the mobile robot device <NUM> indicates an infant, the processor <NUM> may control such that the moving speed of the mobile robot device <NUM> is reduced and that the angle of the arm device remains unchanged.

In an embodiment, when the information about the type of person around the mobile robot device <NUM> indicates a disabled person, the processor <NUM> may control such that the moving speed of the mobile robot device <NUM> is reduced and that the moving noise of the mobile robot device <NUM> is increased.

In an embodiment, when the information about the surrounding environment of the mobile robot device <NUM> indicates that the congestion around the mobile robot device <NUM> is high, the processor <NUM> may control such that the moving speed of the mobile robot device <NUM> is reduced and that the moving direction of the mobile robot device <NUM> is changed.

In an embodiment, when information about the surrounding environment of the mobile robot device <NUM> indicates that the slope of the traveling route of the mobile robot device <NUM> is high, the processor <NUM> may control such that the moving speed of the mobile robot device <NUM> is reduced. In addition, the mobile robot device <NUM> may control the operation of the mobile robot device <NUM> in consideration of the material of the floor of the traveling route as well as the slope of the traveling route.

In an embodiment, when the information about the surrounding environment of the mobile robot device <NUM> indicates that the risk of an object located near the mobile robot device <NUM> is high, the processor <NUM> may reduce the moving speed of the mobile robot device <NUM> and change the moving direction of the mobile robot device <NUM>.

The memory <NUM> may store a program for processing and controlling the processor <NUM>, and may store data input to or output from the mobile robot device <NUM>.

The memory <NUM> may include at least one type of storage medium from among a flash memory type, a hard disk type, a multimedia card micro type, a card type memory (for example, SD or XD memory, and the like), random access memory(RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, or optical disk. For example, the memory <NUM> may store an image of the surroundings of the mobile robot device <NUM> captured while the mobile robot device <NUM> is traveling.

Programs stored in the memory <NUM> may be classified into a plurality of modules according to their functions, for example, may include a notification module.

The notification module may be configured to output a notification signal in the form of a video signal through the display unit <NUM>, or to output a notification signal in the form of an audio signal through the audio output unit <NUM>, or a notification signal in the form of a vibration signal through a vibration motor.

The arm device <NUM> is an arm portion of the mobile robot device <NUM>, and the moving speed, angle, and direction, etc. of the arm device <NUM> may be controlled by the processor <NUM>. In addition, a head device (not shown) is a head portion of the mobile robot device <NUM>, and the moving angle, direction, etc. of the head device (not shown) may be controlled by the processor <NUM>.

The mobile device <NUM> may include a brake unit <NUM>, a steering unit <NUM>, and a throttle <NUM>.

The steering unit <NUM> may be a combination of mechanisms configured to adjust the direction of the mobile robot device <NUM>.

The brake unit <NUM> may be a combination of mechanisms configured to decelerate the mobile robot device <NUM>. For example, the brake unit <NUM> may use friction to reduce the speed of a wheel of the mobile robot device <NUM>.

<FIG> is a block diagram of a processor <NUM> according to some embodiments.

Referring to <FIG>, a processor <NUM> according to some embodiments may include a data-training unit <NUM> and a data recognition unit <NUM>.

The training model described above in <FIG> may be referred to as a data recognition model in <FIG>.

The data-training unit <NUM> may be configured to train criteria for changing a safety operation level of the mobile robot device <NUM>. The data-training unit <NUM> may be configured to train criteria for determining what data is to be used to determine a situation for changing a predetermined safety operation level, and how to determine a situation using the data. The data-training unit <NUM> may be configured to obtain data to be used for training, and apply the obtained data to a data recognition model to be described later, thereby training criteria for changing a safety operation level.

The data recognition unit <NUM> may be configured to determine whether to change a safety operation level based on the data. The data recognition unit <NUM> may be configured to recognize, whether to change the safety operation level from predetermined data by using the trained data recognition model. The data recognition unit <NUM> may obtain preset data according to preset criteria by training, and use a data recognition model using the obtained data as an input value to thereby determine whether to change a safety operation level based on the preset data. In addition, a result value output by the data recognition model using the obtained data as an input value may be used to update the data recognition model.

At least one of the data-training unit <NUM> or the data recognition unit <NUM> may be manufactured in the form of at least one hardware chip and mounted on an electronic device. For example, at least one of the data-training unit <NUM> or the data recognition unit <NUM> may be manufactured in the form of a dedicated hardware chip for artificial intelligence (Al), or an existing general-purpose processor (for example, a CPU or an application processor), or may be manufactured as part of a graphic-only processor (e.g., a GPU) and mounted on various electronic devices described above.

In this case, the data-training unit <NUM> and the data recognition unit <NUM> may be mounted on one electronic device, or may be mounted on separate electronic devices, respectively. For example, one of the data-training unit <NUM> and the data recognition unit <NUM> may be included in the mobile robot device <NUM>, and the other one may be included in the server <NUM>. In addition, the data-training unit <NUM> and the data recognition unit <NUM> may provide model information constructed by the data-training unit <NUM> to the data recognition unit <NUM> through wired or wireless communication, and the data input to the data recognition unit <NUM> may be provided to the data-training unit <NUM> as additional training data.

Meanwhile, at least one of the data-training unit <NUM> or the data recognition unit <NUM> may be implemented as a software module. When at least one of the data-training unit <NUM> or the data recognition unit <NUM> is implemented as a software module (or a program module including an instruction), the software module may be stored in non-transitory computer-readable media. In addition, in this case, at least one software module may be provided by an operating system (OS) or may be provided by a predetermined application. Alternatively, some of at least one software module may be provided by an operating system (OS), and the other may be provided by a predetermined application.

<FIG> is a block diagram of a data-training unit <NUM> according to some embodiments.

Referring to <FIG>, a data-training unit <NUM> according to some embodiments may include a data acquisition unit <NUM>-<NUM>, a pre-processing unit <NUM>-<NUM>, a training data-selection unit <NUM>-<NUM>, and a model-training unit <NUM>-<NUM>, and a model evaluation unit <NUM>-<NUM>.

The data acquisition unit <NUM>-<NUM> may be configured to obtain data necessary to determine whether to change a safety operation level. The data acquisition unit <NUM>-<NUM> may be configured to obtain data necessary for training to determine whether to change a safety operation level.

According to an embodiment, the data acquisition unit <NUM>-<NUM> may be configured to obtain sensing information obtained by sensing the surrounding environment when the mobile robot device <NUM> is traveling. For example, the data acquisition unit <NUM>-<NUM> may be configured to receive an image captured around in real time when the mobile robot device <NUM> is traveling. In addition, the data acquisition unit <NUM>-<NUM> may be configured to receive data through an input device (e.g., microphone, camera or sensor) of the mobile robot device <NUM>. Alternatively, the data acquisition unit <NUM>-<NUM> may be configured to obtain data through an external device communicating with the mobile robot device <NUM>.

According to an embodiment, an image captured around may be more than one, and may be a video including a plurality of images. For example, the data acquisition unit <NUM>-<NUM> may be configured to receive a video through a camera of a mobile robot device <NUM> including a data-training unit <NUM>, or an external camera (e.g., CCTV, etc.) capable of communicating with a mobile robot device <NUM> including a data-training unit <NUM>.

A camera may include one or more image sensors (e.g., front sensor or rear sensor), a lens, an image signal processor (ISP), or a flash (e.g., LED or xenon lamp, etc.).

The pre-processing unit <NUM>-<NUM> may be configured to preprocess obtained data so that the obtained data may be used for training to change a safety operation level. The pre-processing unit <NUM>-<NUM> may be configured to process obtained data in a preset format so that the model-training unit <NUM>-<NUM>, which will be described later, may use the obtained data for training to change a safety operation level. For example, the pre-processing unit <NUM>-<NUM> may be configured to, based on a common region included in each of a plurality of images (or frames) constituting at least a portion of an input video, overlap at least a portion of the plurality of images and generate a single composite image. In this case, a plurality of composite images may be generated from one video. The common region may be a region that includes the same or similar common object (e.g., an object, a plant or animal, or a person) in each of the plurality of images. Alternatively, the common region may be a region in which colors, shades, RGB values, or CMYK values are the same or similar in each of the plurality of images.

The training data-selection unit <NUM>-<NUM> may be configured to select data necessary for training from pre-processed data. The selected data may be provided to the model-training unit <NUM>-<NUM>. The training data-selection unit <NUM>-<NUM> may be configured to select, according to preset criteria for situation determination, data necessary for training from the pre-processed data. In addition, the training data-selection unit <NUM>-<NUM> may also be configured to select data according to preset criteria by training by the model-training unit <NUM>-<NUM>, which will be described later.

For example, data regarding objects, structures, etc. that may have a dangerous effect on the surroundings of the mobile robot device <NUM> may be selected.

The model-training unit <NUM>-<NUM> may be configured to train a criteria for how to change a safety operation level based on training data. In addition, the model-training unit <NUM>-<NUM> may also be configured to train criteria on what training data to use for changing a safety operation level.

According to an embodiment, the model-training unit <NUM>-<NUM> may be configured to train, based on sensing information, criteria for changing to which safety operation level.

In addition, the model-training unit <NUM>-<NUM> may configured to train a data recognition model used to change a safety operation level using training data. In this case, the data recognition model may be a pre-built model. For example, the data recognition model may be a model pre-built by receiving basic training data (e.g., sample images, etc.).

The data recognition model may be constructed in consideration of application fields of the recognition model, training purpose, or computer performance of the device. The data recognition model may be, for example, a model based on a neural network. For example, a model such as a deep neural network (DNN), a recurrent neural network (RNN), or a bidirectional recurrent deep neural network (BRDNN) may be used as a data recognition model, but it is not limited thereto.

According to various embodiments, when there are a plurality of pre-built data recognition models, the model-training unit <NUM>-<NUM> may be configured to determine a data recognition model of a high relationship between input training data and basic training data as a data recognition model to be trained. In this case, the basic training data may be pre-classified for each type of data, and the data recognition model may be pre-built for each type of data. For example, the basic training data may be pre-classified based on various criteria such as the region where the training data was generated, the time when the training data was generated, the size of the training data, the genre of the training data, the generator of the training data, and the type of object in the training data, and the like.

In addition, the model-training unit <NUM>-<NUM> may be configured to train a data recognition model using, for example, a training algorithm including error back-propagation or gradient descent.

In addition, the model-training unit <NUM>-<NUM> may, for example, train a data recognition model through supervised learning using training data as an input value. In addition, the model-training unit <NUM>-<NUM>, for example, may be configured to train the data recognition model through unsupervised learning that discover criteria for changing a safety operation level by self-training a type of data necessary to change a safety operation level without much supervision. In addition, the model-training unit <NUM>-<NUM> may be configured to train a data recognition model, for example, through reinforcement learning using feedback on whether a result of a safety operation level change according to training is correct.

In addition, when a data recognition model is trained, the model-training unit <NUM>-<NUM> may be configured to store the trained data recognition model. In this case, the model-training unit <NUM>-<NUM> may be configured to store the trained data recognition model in a memory of the mobile robot device <NUM> including the data recognition unit <NUM>. Alternatively, the model-training unit <NUM>-<NUM> may be configured to store the trained data recognition model in a memory of the mobile robot device <NUM> including the data recognition unit <NUM> to be described later. Alternatively, the model-training unit <NUM>-<NUM> may be configured to store the trained data recognition model in a memory of the server <NUM> connected to the mobile robot device <NUM> through a wired or wireless network.

In this case, a memory in which the trained data recognition model is stored may store, for example, commands or data related to at least one other component of the mobile robot device <NUM>. In addition, the memory may store software and/or programs. The program may include, for example, a kernel, middleware, application programming interface (API), and/or application program (or "application").

The model evaluation unit <NUM>-<NUM> may be configured to input evaluation data into a data recognition model, and when the recognition result output from the evaluation data does not satisfy predetermined criteria, to cause the model-training unit <NUM>-<NUM> to train again. In this case, the evaluation data may be preset data for evaluating the data recognition model.

For example, the model evaluation unit <NUM>-<NUM> may be configured to evaluate that recognition result of a trained data recognition model for evaluation data does not satisfy a predetermined criteria when the number of ratio of inaccurate evaluation data among the recognition result exceeds a preset threshold. For example, in case where a predetermined criteria is defined as a ratio of <NUM>%, when the trained data recognition model outputs incorrect recognition results for more than <NUM> evaluation data out of a total of <NUM> evaluation data, the model evaluation unit <NUM>-<NUM> may be configured to evaluate that the trained data recognition model is not suitable.

On the other hand, when there are a plurality of trained data recognition models, the model evaluation unit <NUM>-<NUM> may be configured to evaluate whether or not a predetermined criteria is satisfied for each trained data recognition model, and to determine a model that satisfies the predetermined criteria as the final data recognition model. In this case, when there are a plurality of models satisfying the predetermined criteria, the model evaluation unit <NUM>-<NUM> may be configured to determine any one or a predetermined number of models preset in order of highest evaluation score as the final data recognition model.

Meanwhile, at least one of the data acquisition unit <NUM>-<NUM>, the pre-processing unit <NUM>-<NUM>, the training data-selection unit <NUM>-<NUM>, the model-training unit <NUM>-<NUM>, or the model evaluation unit <NUM>-<NUM> in the data-training unit <NUM> may be manufactured in the form of at least one hardware chip and mounted on the mobile robot device <NUM>. For example, at least one of the data acquisition unit <NUM>-<NUM>, the pre-processing unit <NUM>-<NUM>, the training data-selection unit <NUM>-<NUM>, the model-training unit <NUM>-<NUM>, or the model evaluation unit <NUM>-<NUM> may be manufactured in the form of a dedicated hardware chip for artificial intelligence (Al) or may be manufactured as part of an existing general-purpose processor (e.g., CPU or application processor) or graphics-only processor (e.g., GPU) and mounted on various mobile robot devices <NUM> described above.

In addition, the data acquisition unit <NUM>-<NUM>, the pre-processing unit <NUM>-<NUM>, the training data-selection unit <NUM>-<NUM>, the model-training unit <NUM>-<NUM>, and the model evaluation unit <NUM>-<NUM> may be mounted in one electronic device, or may be mounted on separate electronic devices, respectively. For example, some of the data acquisition unit <NUM>-<NUM>, the pre-processing unit <NUM>-<NUM>, the training data-selection unit <NUM>-<NUM>, the model-training unit <NUM>-<NUM>, and the model evaluation unit <NUM>-<NUM> may be included in the mobile robot device <NUM>, and the other part may be included in the server <NUM>.

In addition, at least one of the data acquisition unit <NUM>-<NUM>, the pre-processing unit <NUM>-<NUM>, the training data-selection unit <NUM>-<NUM>, the model-training unit <NUM>-<NUM>, or the model evaluation unit <NUM>-<NUM> may be implemented as a software module. When at least one of the data acquisition unit <NUM>-<NUM>, the pre-processing unit <NUM>-<NUM>, the training data-selection unit <NUM>-<NUM>, the model-training unit <NUM>-<NUM>, or the model evaluation unit <NUM>-<NUM> is implemented as a software module (alternatively, a program module including an instruction), the software module may be stored in a non-transitory computer-readable media. In addition, in this case, at least one software module may be provided by an operating system (OS) or may be provided by a predetermined application. Alternatively, some of at least one software module may be provided by an operating system (OS), and the other may be provided by a predetermined application.

<FIG> is a block diagram of a data recognition unit <NUM> according to some embodiments.

Referring to <FIG>, according to some embodiments, a data recognition unit <NUM> according to some embodiments may include a data acquisition unit <NUM>-<NUM>, a pre-processing unit <NUM>-<NUM>, a recognition data-selection unit <NUM>-<NUM>, a recognition result-providing unit <NUM>-<NUM>, and a model-refining unit <NUM>-<NUM>.

The data acquisition unit <NUM>-<NUM> may be configured to obtain the data necessary to change a safety operation level, and the pre-processing unit <NUM>-<NUM> may be configured to preprocess the obtained data such that the obtained data may be used to change a safety operation level. The pre-processing unit <NUM>-<NUM> may be configured to process the obtained data in a preset format such that the recognition result-providing unit <NUM>-<NUM>, which will be described later, may use the obtained data to change a safety operation level.

The recognition data-selection unit <NUM>-<NUM> may be configured to select data required for changing a safety operation level among the pre-processed data. The selected data may be provided to the recognition result-providing unit <NUM>-<NUM>. The recognition data-selection unit <NUM>-<NUM> may be configured to select some or all of the pre-processed data according to preset criteria for changing a safety operation level. In addition, the recognition data-selection unit <NUM>-<NUM> may also be configured to select data according to preset criteria by training by the model-training unit <NUM>-<NUM>, which will be described later.

The recognition result-providing unit <NUM>-<NUM> may be configured to change a safety operation level by applying the selected data to a data recognition model. The recognition result-providing unit <NUM>-<NUM> may be configured to provide a recognition result according to the purpose of recognizing data. The recognition result-providing unit <NUM>-<NUM> may be configured to apply the selected data to a data recognition model by using data selected by the recognition data selection unit <NUM>-<NUM> as an input value. In addition, the recognition result may be determined by a data recognition model.

The model-refining unit <NUM>-<NUM> may be configured to refine the data recognition model based on evaluation of recognition results provided by recognition result-providing unit <NUM>-<NUM>. For example, the model-refining unit <NUM>-<NUM> may provide a recognition result provided by the recognition result-providing unit <NUM>-<NUM> to the model training unit <NUM>-<NUM>, so that the model-training unit <NUM>-<NUM> may thereby refine the data recognition model.

Meanwhile, at least one of the data acquisition unit <NUM>-<NUM>, the pre-processing unit <NUM>-<NUM>, the recognition data-selection unit <NUM>-<NUM>, the recognition result-providing unit <NUM>-<NUM>, or the model-refining unit <NUM>-<NUM> in the data recognition unit <NUM>, may be manufactured in the form of at least one hardware chip and mounted on the mobile robot device <NUM>. For example, at least one of the data acquisition unit <NUM>-<NUM>, the pre-processing unit <NUM>-<NUM>, the recognition data-selection unit <NUM>-<NUM>, the recognition result-providing unit <NUM>-<NUM>, or the model-refining unit <NUM>-<NUM> may be manufactured in the form of a dedicated hardware chip for artificial intelligence (Al) or may be manufactured as part of an existing general-purpose processor (e.g., CPU or application processor) or graphics-only processor (e.g., GPU) and mounted on various types of mobile robot device <NUM> described above.

In addition, the data acquisition unit <NUM>-<NUM>, the pre-processing unit <NUM>-<NUM>, the recognition data-selection unit <NUM>-<NUM>, the recognition result-providing unit <NUM>-<NUM>, and the model-refining unit <NUM>-<NUM> may be mounted in one electronic device, or may be mounted on separate electronic devices, respectively. For example, some of the data acquisition unit <NUM>-<NUM>, the pre-processing unit <NUM>-<NUM>, the recognition data-selection unit <NUM>-<NUM>, the recognition result-providing unit <NUM>-<NUM>, and the model-refining unit <NUM>-<NUM> may be included in the mobile robot device <NUM>, and the other part may be included in the server <NUM>.

In addition, at least one of the data acquisition unit <NUM>-<NUM>, the pre-processing unit <NUM>-<NUM>, the recognition data-selection unit <NUM>-<NUM>, the recognition result-providing unit <NUM>-<NUM>, or the model-refining unit <NUM>-<NUM> may be implemented as a software module. When at least one of the data acquisition unit <NUM>-<NUM>, the pre-processing unit <NUM>-<NUM>, the recognition data-selection unit <NUM>-<NUM>, the recognition result-providing unit <NUM>-<NUM>, or the model-refining unit <NUM>-<NUM> is implemented as a software module (alternatively, a program module including an instruction), the software module may be stored in a non-transitory computer-readable media. In addition, in this case, at least one software module may be provided by an operating system (OS) or may be provided by a predetermined application. Alternatively, some of at least one software module may be provided by an operating system (OS), and the other may be provided by a predetermined application.

<FIG> is a diagram illustrating an example in which a mobile robot device <NUM> and a server <NUM> train and recognize data by interworking with each other, according to some embodiments.

Referring to <FIG>, the server <NUM> may be configured to train criteria for changing a safety operation level, and the mobile robot device <NUM> may be configured to change a safety operation level based on a result of the training by the server <NUM>.

In this case, a model-training unit <NUM> of the server <NUM> may perform the function of the data-training unit <NUM> shown in <FIG>. The model-training unit <NUM> of server <NUM> may be configured to train criteria on which data to use to change a predetermined safety operation level and how to determine a safety operation level using the data. The model-training unit <NUM> may be configured to train criteria for changing a safety operation level by obtaining data to be used for training and applying the obtained data to a data recognition model to be described later.

In addition, a recognition result-providing unit <NUM>-<NUM> of the mobile robot device <NUM> may be configured to determine a safety operation level to be changed by applying data selected by the recognition data-selection unit <NUM>-<NUM> to a data recognition model generated by the server <NUM>. For example, the recognition result-providing unit <NUM>-<NUM> may be configured to transmit data selected by the recognition data-selection unit <NUM>-<NUM> to the server <NUM>, and to request the server <NUM> to determine a safety operation level to be changed by applying the data selected by the recognition data-selection unit <NUM>-<NUM> to a recognition model. Further, the recognition result-providing unit <NUM>-<NUM> may receive information on a safety operation level determined by the server <NUM> from the server <NUM>.

For example, the mobile robot device <NUM> may be configured to transmit sensing information obtained by sensing the surrounding environment of the mobile robot device <NUM> to the server <NUM>, and to request the server <NUM> to determine a safety operation level of the mobile robot device <NUM> by applying the sensed information to a data recognition model. In addition, an electronic device <NUM> may be configured to receive a safety operation level determined by the server <NUM> from the server <NUM>.

Alternatively, a recognition result-providing unit <NUM>-<NUM> of the electronic device <NUM> may be configured to receive a recognition model generated by the server <NUM> from the server <NUM>, and to determine a safety operation level to be changed using the received recognition model. In this case, the recognition result-providing unit <NUM>-<NUM> of the mobile robot device <NUM> may be configured to determine a situation by applying data selected by the recognition data-selection unit <NUM>-<NUM> to the data recognition model received from the server <NUM>.

For example, the mobile robot device <NUM> may be configured to change a safety operation level of the mobile robot device <NUM> by applying sensing information obtained by sensing the surrounding environment of the mobile robot device <NUM> to the data recognition model received from server <NUM>.

Some embodiments may also be embodied in the form of a recording medium including computer-executable instructions, such as a program module executed by a computer. Computer-readable media may be any available media that can be accessed by a computer, and may include both volatile and nonvolatile media, removable and non-removable media. In addition, computer-readable media may include both computer storage media and communication media. Computer storage media includes both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules or other data. Communication media typically include computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave, or other transmission mechanism, and include any information delivery media.

Further, in the present specification, the "unit" may be a hardware component such as a processor or a circuit, and/or a software component executed by a hardware component such as a processor.

Claim 1:
A method of providing, by a mobile robot device (<NUM>) comprising an arm device (<NUM>), a service to a user, the method comprising:
obtaining sensing information (<NUM>) obtained by sensing a surrounding environment of the mobile robot device (<NUM>) while the mobile robot device (<NUM>) is traveling;
the method characterized by:
obtaining, based on the sensing information (<NUM>), information about a type of person around the mobile robot device (<NUM>) and information about the surrounding environment (<NUM>) of the mobile robot device (<NUM>);
changing, based on the information about the type of person and the information about the surrounding environment (<NUM>), a safety operation level (<NUM>) of the mobile robot device (<NUM>); and
controlling, based on the changed safety operation level (<NUM>), an operation of the mobile robot device (<NUM>),
wherein the safety operation level (<NUM>) is a level for controlling an operation related to a movement of the mobile robot device (<NUM>) and a motion of the arm device (<NUM>), and
the mobile robot device (<NUM>) changes the safety operation level (<NUM>) by applying the obtained information about the type of person and the obtained information about the surrounding environment (<NUM>) to a training model (<NUM>) trained using an artificial intelligence algorithm.