KR20210083812A - Autonomous mobile robots and operating method thereof - Google Patents

Abstract

Embodiments of the present disclosure relate to an autonomous mobile robot and an operating method thereof. The autonomous mobile robot comprises a communications circuit configured to send and receive signals, an information collecting device configured to sense a surrounding environment, a driving device configured to implement movement of the autonomous mobile robot, and a processor configured to control the autonomous mobile robot. The processor obtains first information related to a situation inside an elevator and second information related to a situation outside the elevator in response to detecting an entry into an elevator platform, and based on the first information and the second information, determines a boarding plan for the elevator, and controls a boarding operation to be carried out based on the determined boarding plan.

Description

Autonomous driving mobile robot and its operation method {AUTONOMOUS MOBILE ROBOTS AND OPERATING METHOD THEREOF}

Embodiments of the present disclosure relate to an autonomous driving mobile robot and an operating method thereof.

In general, an autonomous mobile robot is a robot that is equipped with a power supply and a sensor in the main body and can move autonomously without supply of signals and power from the outside. It can handle various tasks such as the security service it is responsible for.

In general, when a self-driving robot performs a task on a plurality of floors, such as a building, a moving means that enables movement to another floor, such as an elevator, may be used. However, when a person and an autonomous mobile robot ride together in an elevator (eg, a lift car) that is a relatively narrow and closed space, there is a risk that a person may feel uncomfortable or injured by the autonomous mobile robot.

An object of the present disclosure is to determine an elevator boarding plan by an autonomous mobile robot so that a person using the elevator does not feel uncomfortable or injured.

An autonomous driving mobile robot according to embodiments of the present disclosure includes a communication circuit configured to send and receive signals, an information collection device configured to sense a surrounding environment, a driving device configured to implement movement of the autonomous driving mobile robot, and the autonomous driving a processor configured to control the mobile robot, wherein the processor obtains first information related to the situation inside the elevator and second information related to the situation outside the elevator in response to detecting the entry into the elevator platform, Determine a boarding plan for the elevator including at least one of a waiting position in the platform, a boarding position in the elevator, or a movement path from the waiting position to the boarding location based on the first information and the second information, , it is possible to control the boarding operation to be performed based on the determined boarding plan.

The operating method of the autonomous driving mobile robot according to embodiments of the present disclosure includes obtaining first information related to the internal situation of the elevator and second information related to the external situation of the elevator in response to detecting an entry into the elevator platform. a boarding plan in the elevator comprising at least one of an operation, a waiting position in the platform, a boarding position in the elevator, or a movement path from the waiting position to the boarding position based on the operation, the first information and the second information It may include the operation of determining and boarding the elevator based on the determined boarding plan.

The autonomous driving mobile robot according to the embodiments of the present disclosure determines the standby position, movement path, and boarding position of the autonomous driving mobile robot based on the waiting situation at the elevator platform and the boarding situation inside the elevator, so that a person can be operated by the autonomous driving mobile robot. You can solve the problem of feeling uncomfortable or hurting.

1 shows an AI device according to an embodiment of the present disclosure.
2 illustrates an AI server according to an embodiment of the present disclosure.
3 shows an AI system according to an embodiment of the present disclosure.
4 conceptually illustrates an autonomous driving mobile robot according to embodiments of the present disclosure.
5 is a flowchart illustrating an operation method of an autonomous driving mobile robot according to embodiments of the present disclosure.
6 is a flowchart illustrating a method of determining a boarding plan of an autonomous driving mobile robot according to embodiments of the present disclosure.
7 is a flowchart illustrating a method for determining a boarding position of an autonomous driving mobile robot according to embodiments of the present disclosure.
8 is a view for explaining an operation of determining a boarding position according to an embodiment of the present disclosure.
9 is a view for explaining an operation of determining a boarding position according to another embodiment of the present disclosure.
10 is a flowchart illustrating a method of checking a free space inside an elevator of an autonomous driving mobile robot according to embodiments of the present disclosure.
11 is a view for explaining the operation of the autonomous driving mobile robot that cannot ride an elevator according to an embodiment of the present disclosure.
12 is a flowchart illustrating a method of determining a boarding standby position of an autonomous driving mobile robot according to embodiments of the present disclosure.
13 is a view for explaining an operation of determining a boarding standby position according to an embodiment of the present disclosure.
14 is a view for explaining an operation of determining a boarding standby position according to another embodiment of the present disclosure.
15 is a view for explaining an operation of determining a boarding standby position according to another embodiment of the present disclosure.
16 is a flowchart illustrating an elevator boarding method of an autonomous mobile robot according to embodiments of the present disclosure.
17 is a flowchart illustrating a method of getting off an elevator of an autonomous mobile robot according to embodiments of the present disclosure.
18 is a view for explaining a disembarkation operation of the autonomous driving mobile robot according to an embodiment of the present disclosure.
19 is a view for explaining a disembarkation operation of an autonomous driving mobile robot according to another embodiment of the present disclosure.

Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings.

Artificial intelligence refers to a field that studies artificial intelligence or methodologies that can make it, and machine learning refers to a field that defines various problems dealt with in the field of artificial intelligence and studies methodologies to solve them. do. Machine learning is also defined as an algorithm that improves the performance of a certain task through constant experience.

An artificial neural network (ANN) is a model used in machine learning, and may refer to an overall model having problem-solving ability, which is composed of artificial neurons (nodes) that form a network by combining synapses. An artificial neural network may be defined by a connection pattern between neurons of different layers, a learning process that updates model parameters, and an activation function that generates 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 neurons and synapses connecting neurons. In the artificial neural network, each neuron may output a function value of an activation function with respect to input signals inputted through a synapse, a weight, and a bias.

Model parameters refer to parameters determined through learning, and include the weight of synaptic connections and the bias of neurons. In addition, the hyperparameter refers to a parameter that must be set before learning in a machine learning algorithm, and includes a learning rate, the number of iterations, a mini-batch size, an initialization function, and the like.

The purpose of learning the artificial neural network can be seen as determining the model parameters that minimize the loss function. The loss function may be used as an index for determining optimal model parameters in the learning process of the artificial neural network.

Machine learning can be classified into supervised learning, unsupervised learning, and reinforcement learning according to a learning method.

Supervised learning refers to a method of training an artificial neural network in a state where a label for training data is given, and the label is the correct answer (or result value) that the artificial neural network should infer when the training data is input to the artificial neural network. can mean Unsupervised learning may refer to a method of training an artificial neural network in a state where no labels are given for training data. Reinforcement learning can refer to a learning method in which an agent defined in an environment learns to select an action or sequence of actions that maximizes the cumulative reward in each state.

Among artificial neural networks, machine learning implemented as a deep neural network (DNN) including a plurality of hidden layers is also called deep learning (deep learning), and deep learning is a part of machine learning. Hereinafter, machine learning is used in a sense including deep learning.

A robot can mean a machine that automatically handles or operates a task given by its own capabilities. In particular, a robot having a function of recognizing an environment and performing an operation by self-judgment may be referred to as an intelligent robot.

Robots can be classified into industrial, medical, home, military, etc. depending on the purpose or field of use.

The robot may be provided with a manipulator including an actuator or a driving device to perform various physical operations such as moving a robot joint. In addition, a mobile robot can travel on the ground or fly in the air, including wheels, brakes, propellers, and the like.

Autonomous driving refers to a technology that drives itself, and an autonomous driving vehicle refers to a vehicle that travels without or with minimal manipulation of a user.

For example, autonomous driving includes technology for maintaining a driving lane, technology for automatically adjusting speed such as adaptive cruise control, technology for automatically driving along a predetermined route, technology for automatically setting a route when a destination is set, etc. All of these can be included.

The vehicle includes a vehicle having only an internal combustion engine, a hybrid vehicle having both an internal combustion engine and an electric motor, and an electric vehicle having only an electric motor, and may include not only automobiles, but also trains, motorcycles, and the like.

In this case, the autonomous vehicle can be viewed as a robot having an autonomous driving function.

The extended reality is a generic term for virtual reality (VR), augmented reality (AR), and mixed reality (MR). VR technology provides only CG images of objects or backgrounds in the real world, AR technology provides virtual CG images on top of images of real objects, and MR technology is a computer that mixes and combines virtual objects in the real world. graphic technology.

MR technology is similar to AR technology in that it shows both real and virtual objects. However, there is a difference in that in AR technology, a virtual object is used in a form that complements a real object, whereas in MR technology, a virtual object and a real object are used with equal characteristics.

XR technology can be applied to HMD (Head-Mount Display), HUD (Head-Up Display), mobile phone, tablet PC, laptop, desktop, TV, digital signage, etc. can be called

1 shows an AI device 100 according to an embodiment of the present disclosure.

AI device 100 is TV, projector, mobile phone, smartphone, desktop computer, notebook computer, digital broadcasting terminal, PDA (personal digital assistants), PMP (portable multimedia player), navigation, tablet PC, wearable device, set-top box (STB) ), a DMB receiver, a radio, a washing machine, a refrigerator, a desktop computer, a digital signage, a robot, a vehicle, etc., may be implemented as a fixed device or a movable device.

Referring to FIG. 1 , the terminal 100 includes a communication circuit 110 , an input device 120 , a learning processor 130 , a sensor 140 , an output device 150 , a memory 170 and a processor 180 , etc. may include.

The communication circuit 110 may transmit/receive data to and from external devices such as other AI devices 100a to 100e or the AI server 200 using wired/wireless communication technology. For example, the communication circuit 110 may transmit and receive sensor information, a user input, a learning model, a control signal, and the like, with external devices.

At this time, the communication technology used by the communication circuit 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 device 120 may acquire various types of data.

In this case, the input device 120 may include a camera for inputting an image signal, a microphone for receiving an audio signal, a user input unit for receiving information from a user, and the like. Here, the camera or microphone may be treated as a sensor, and a signal obtained from the camera or microphone may be referred to as sensing data or sensor information.

The input device 120 may acquire training data for model training and input data to be used when acquiring an output by using the training model. The input device 120 may acquire raw input data, and 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 train a model composed of an artificial neural network by using the training data. Here, the learned artificial neural network may be referred to as a learning model. The learning model may be used to infer a result value with respect to new input data other than the training data, and the inferred value may be used as a basis for a decision 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 .

In this case, the learning processor 130 may include a memory integrated or implemented in the AI device 100 . Alternatively, the learning processor 130 may be implemented using the memory 170 , an external memory directly coupled to the AI device 100 , or a memory maintained in an external device.

The sensor 140 may acquire at least one of internal information of the AI device 100 , surrounding environment information of the AI device 100 , and user information by using various sensors.

At this time, sensors included in the sensor 140 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, radar, etc.

The output device 150 may generate an output related to sight, hearing, or touch.

In this case, the output device 150 may include a display that visually outputs information, a speaker that audibly outputs information, and a haptic actuator that tactically outputs information. For example, the display may output an image or video, the speaker may output voice or sound, and the haptic actuator may generate vibration.

The memory 170 may store data supporting various functions of the AI device 100 . For example, the memory 170 may store input data obtained from the input device 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 using a data analysis algorithm or a machine learning algorithm. In addition, the processor 180 may control the components of the AI device 100 to perform the determined operation.

To this end, the processor 180 may request, search, receive, or utilize the data of the learning processor 130 or the memory 170, and may perform a predicted operation or an operation determined to be desirable among the at least one executable operation. It is possible to control the components of the AI device 100 to execute.

In this case, when the connection of the external device is required to perform the determined operation, the processor 180 may generate a control signal for controlling the corresponding external device and transmit the generated control signal to the corresponding external device.

The processor 180 may obtain intention information with respect to a user input and determine a user's requirement based on the obtained intention information.

In this case, the processor 180 uses at least one of a speech to text (STT) engine for converting a voice input into a character string or a natural language processing (NLP) engine for obtaining intention information of a natural language. Intention information corresponding to the input may be obtained.

At this time, at least one of the STT engine and the NLP engine may be configured as an artificial neural network, at least a part of which is learned according to a machine learning algorithm. And, at least one or more of the STT engine or the NLP engine is learned by the learning processor 130 , or learned by the learning processor 240 of the AI server 200 , or learned by distributed processing thereof. it could be

The processor 180 collects history information including the user's feedback on the operation contents or operation of the AI device 100 and stores it in the memory 170 or the learning processor 130, or the AI server 200 It can be transmitted to an external device. The collected historical information may be used to update the learning model.

The processor 180 may control at least some of the components of the AI device 100 in order to drive an application program stored in the memory 170 . Furthermore, in order to drive the application program, the processor 180 may operate two or more of the components included in the AI device 100 in combination with each other.

2 shows 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 trains an artificial neural network using a machine learning algorithm or uses a learned artificial neural network. Here, the AI server 200 may be configured with a plurality of servers to perform distributed processing, and may be defined as a 5G network. In this case, the AI server 200 may be included as a part of the AI device 100 to perform at least a part of AI processing together.

The AI server 200 may include a communication circuit 210 , a memory 230 , a learning processor 240 , and a processor 260 .

The communication circuit 210 may transmit/receive data to and from an external device such as the AI device 100 .

The memory 230 may store a model (or artificial neural network, 231 ) being trained or learned through the learning processor 240 .

The learning processor 240 may train the artificial neural network 231a using the training data. The learning model may be used while being mounted on the AI server 200 of the artificial neural network, or may be used while 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. When a part or all of the learning model is implemented in software, one or more instructions constituting the learning model may be stored in the memory 230 .

The processor 260 may infer a result value with respect to new input data using the learning model, and may generate a response or a control command based on the inferred result value.

3 shows an AI system 1 according to an embodiment of the present disclosure.

Referring to FIG. 3 , the AI system 1 includes at least one of an AI server 200 , a robot 100a , an autonomous vehicle 100b , an XR device 100c , a smartphone 100d , or a home appliance 100e . It is connected to the cloud network 10 . Here, the robot 100a to which the AI technology is applied, the autonomous driving vehicle 100b, the XR device 100c, the smart phone 100d, or the home appliance 100e may be referred to as AI devices 100a to 100e.

The cloud network 10 may constitute a part of the cloud computing infrastructure or may refer to a network existing in the cloud computing infrastructure. Here, the cloud network 10 may be configured using a 3G network, a 4G or Long Term Evolution (LTE) network, or a 5G network.

That is, each of the devices 100a to 100e and 200 constituting the AI system 1 may be connected to each other through the cloud network 10 . In particular, each of the devices 100a to 100e and 200 may communicate with each other through the base station, but may also directly communicate with each other without passing through the base station.

The AI server 200 may include a server performing AI processing and a server performing an operation on big data.

The AI server 200 includes at least one of the AI devices constituting the AI system 1, such as a robot 100a, an autonomous vehicle 100b, an XR device 100c, a smart phone 100d, or a home appliance 100e, and It is connected through the cloud network 10 and may help at least a part of AI processing of the connected AI devices 100a to 100e.

In this case, the AI server 200 may train the artificial neural network according to a machine learning algorithm on behalf of the AI devices 100a to 100e, and directly store the learning model or transmit it to the AI devices 100a to 100e.

At this time, the AI server 200 receives input data from the AI devices 100a to 100e, infers a result value with respect to the input data received using the learning model, and provides a response or control command based on the inferred result value. It can be generated and transmitted to the AI devices 100a to 100e.

Alternatively, the AI devices 100a to 100e may infer a result value with respect to input data using a direct learning model, and generate a response or a control command based on the inferred result value.

Hereinafter, various embodiments of the AI devices 100a to 100e to which the above-described technology is applied will be described. Here, the AI devices 100a to 100e shown in FIG. 3 can be viewed as specific examples of the AI device 100 shown in FIG. 1 .

The robot 100a may be implemented as a guide robot, a transport robot, a cleaning robot, a wearable robot, an entertainment robot, a pet robot, an unmanned flying robot, etc. to which AI technology is applied.

The robot 100a may include a robot control module for controlling an operation, and the robot control module may mean a software module or a chip implemented as hardware.

The robot 100a obtains state information of the robot 100a by using sensor information obtained from various types of sensors, detects (recognizes) the surrounding environment and objects, generates map data, moves path and travels A plan may be determined, a response to a user interaction may be determined, or an action may be determined.

Here, the robot 100a may use sensor information obtained from at least one sensor among LiDAR, radar, and camera to determine a movement path and a travel plan.

The robot 100a may perform the above-described operations using a learning model composed of at least one artificial neural network. For example, the robot 100a may recognize a surrounding environment and an object using a learning model, and may determine an operation using the recognized surrounding environment information or object information. Here, the learning model may be directly learned from the robot 100a or learned from an external device such as the AI server 200 .

At this time, the robot 100a may perform an operation by generating a result by using the direct learning model, but transmits sensor information to an external device such as the AI server 200 and receives the result generated accordingly to perform the operation You may.

The robot 100a determines a movement path and travel plan using at least one of map data, object information detected from sensor information, or object information obtained from an external device, and controls the driving unit to apply the determined movement path and travel plan. Accordingly, the robot 100a may be driven.

The map data may include object identification information for various objects disposed in a space in which the robot 100a moves. For example, the map data may include object identification information for fixed objects such as walls and doors and movable objects such as flowerpots and desks. In addition, the object identification information may include a name, a type, a distance, a location, and the like.

In addition, the robot 100a may perform an operation or drive by controlling the driving unit based on the user's control/interaction. In this case, the robot 100a may acquire intention information of an interaction according to a user's motion or voice utterance, determine a response based on the acquired intention information, and perform the operation.

The autonomous vehicle 100b may be implemented as a mobile robot, a vehicle, an unmanned aerial vehicle, etc. by applying AI technology.

The autonomous driving vehicle 100b may include an autonomous driving control module for controlling an autonomous driving function, and the autonomous driving control module may mean a software module or a chip implemented as hardware. The autonomous driving control module may be included inside as a configuration of the autonomous driving vehicle 100b, or may be configured and connected as separate hardware to the outside of the autonomous driving vehicle 100b.

The autonomous vehicle 100b obtains state information of the autonomous vehicle 100b using sensor information obtained from various types of sensors, detects (recognizes) surrounding environments and objects, generates map data, A moving route and a driving plan may be determined, or an operation may be determined.

Here, the autonomous driving vehicle 100b may use sensor information obtained from at least one sensor among a lidar, a radar, and a camera, like the robot 100a, in order to determine a moving route and a driving plan.

In particular, the autonomous vehicle 100b may receive sensor information from external devices to recognize an environment or object for an area where the field of view is blocked or an area over a certain distance, or receive information recognized directly from external devices. .

The autonomous vehicle 100b may perform the above-described operations by using a learning model composed of at least one artificial neural network. For example, the autonomous vehicle 100b may recognize a surrounding environment and an object using a learning model, and may determine a driving route using the recognized surrounding environment information or object information. Here, the learning model may be directly learned from the autonomous vehicle 100b or learned from an external device such as the AI server 200 .

In this case, the autonomous vehicle 100b may directly generate a result using the learning model and perform the operation, but it operates by transmitting sensor information to an external device such as the AI server 200 and receiving the result generated accordingly. can also be performed.

The autonomous vehicle 100b determines a moving path and a driving plan by using at least one of map data, object information detected from sensor information, or object information obtained from an external device, and controls the driving unit to determine the moving path and driving. The autonomous vehicle 100b may be driven according to a plan.

The map data may include object identification information for various objects disposed in a space (eg, a road) in which the autonomous vehicle 100b travels. For example, the map data may include object identification information on fixed objects such as street lights, rocks, and buildings, and movable objects such as vehicles and pedestrians. In addition, the object identification information may include a name, a type, a distance, a location, and the like.

In addition, the autonomous vehicle 100b may perform an operation or drive by controlling the driving unit based on the user's control/interaction. In this case, the autonomous vehicle 100b may acquire intention information of an interaction according to a user's motion or voice utterance, determine a response based on the acquired intention information, and perform the operation.

The XR apparatus 100c is AI technology applied, so a head-mount display (HMD), a head-up display (HUD) provided in a vehicle, a television, a mobile phone, a smart phone, a computer, a wearable device, a home appliance, and a digital signage , a vehicle, a stationary robot, or a mobile robot.

The XR device 100c analyzes three-dimensional point cloud data or image data obtained through various sensors or from an external device to generate position data and attribute data for three-dimensional points, thereby providing information on surrounding space or real objects. It can be obtained and output by rendering the XR object to be output. For example, the XR apparatus 100c may output an XR object including additional information on the recognized object to correspond to the recognized object.

The XR apparatus 100c may perform the above operations by using a learning model composed of at least one artificial neural network. For example, the XR apparatus 100c may recognize a real object from 3D point cloud data or image data using a learning model, and may provide information corresponding to the recognized real object. Here, the learning model may be directly learned from the XR device 100c or learned from an external device such as the AI server 200 .

In this case, the XR device 100c may perform an operation by generating a result using the direct learning model, but transmits sensor information to an external device such as the AI server 200 and receives the result generated accordingly to perform the operation. can also be done

The robot 100a may be implemented as a guide robot, a transport robot, a cleaning robot, a wearable robot, an entertainment robot, a pet robot, an unmanned flying robot, etc. to which AI technology and autonomous driving technology are applied.

The robot 100a to which AI technology and autonomous driving technology are applied may mean a robot having an autonomous driving function or a robot 100a that interacts with the autonomous driving vehicle 100b.

The robot 100a having an autonomous driving function may collectively refer to devices that move by themselves according to a given movement line without user control, or move by determining a movement line by themselves.

The robot 100a with the autonomous driving function and the autonomous driving vehicle 100b may use a common sensing method to determine one or more of a moving route or a driving plan. For example, the robot 100a having an autonomous driving function and the autonomous driving vehicle 100b may determine one or more of a movement route or a driving plan by using information sensed through lidar, radar, and camera.

The robot 100a interacting with the autonomous driving vehicle 100b exists separately from the autonomous driving vehicle 100b and is linked to an autonomous driving function inside or outside the autonomous driving vehicle 100b, or the autonomous driving vehicle 100b ) can perform an operation associated with the user on board.

At this time, the robot 100a interacting with the autonomous driving vehicle 100b obtains sensor information on behalf of the autonomous driving vehicle 100b and provides it to the autonomous driving vehicle 100b, or obtains sensor information and obtains information about the surrounding environment or By generating object information and providing it to the autonomous driving vehicle 100b, the autonomous driving function of the autonomous driving vehicle 100b may be controlled or supported.

Alternatively, the robot 100a interacting with the autonomous driving vehicle 100b may monitor a user riding in the autonomous driving vehicle 100b or control a function of the autonomous driving vehicle 100b through interaction with the user. . For example, when it is determined that the driver is in a drowsy state, the robot 100a may activate the autonomous driving function of the autonomous driving vehicle 100b or assist the control of the driving unit of the autonomous driving vehicle 100b. Here, the function of the autonomous driving vehicle 100b controlled by the robot 100a may include not only an autonomous driving function, but also a function provided by a navigation system or an audio system provided in the autonomous driving vehicle 100b.

Alternatively, the robot 100a interacting with the autonomous driving vehicle 100b may provide information or assist a function to the autonomous driving vehicle 100b from the outside of the autonomous driving vehicle 100b. For example, the robot 100a may provide traffic information including signal information to the autonomous vehicle 100b, such as a smart traffic light, or interact with the autonomous vehicle 100b, such as an automatic electric charger for an electric vehicle. You can also automatically connect an electric charger to the charging port.

The robot 100a may be implemented as a guide robot, a transport robot, a cleaning robot, a wearable robot, an entertainment robot, a pet robot, an unmanned flying robot, a drone, etc. to which AI technology and XR technology are applied.

The robot 100a to which the XR technology is applied may mean a robot that is a target of control/interaction within an XR image. In this case, the robot 100a is distinguished from the XR device 100c and may be interlocked with each other.

When the robot 100a, which is the target of control/interaction within the XR image, obtains sensor information from sensors including a camera, the robot 100a or the XR device 100c generates an XR image based on the sensor information. and the XR apparatus 100c may output the generated XR image. In addition, the robot 100a may operate based on a control signal input through the XR device 100c or a user's interaction.

For example, the user can check the XR image corresponding to the viewpoint of the remotely interlocked robot 100a through an external device such as the XR device 100c, and adjust the autonomous driving path of the robot 100a through interaction or , control motion or driving, or check information of surrounding objects.

The autonomous vehicle 100b may be implemented as a mobile robot, a vehicle, an unmanned aerial vehicle, etc. by applying AI technology and XR technology.

The autonomous driving vehicle 100b to which the XR technology is applied may mean an autonomous driving vehicle equipped with a means for providing an XR image or an autonomous driving vehicle subject to control/interaction within the XR image. In particular, the autonomous vehicle 100b, which is the target of control/interaction within the XR image, is distinguished from the XR device 100c and may be interlocked with each other.

The autonomous vehicle 100b provided with means for providing an XR image may obtain sensor information from sensors including a camera, and may output an XR image generated based on the acquired sensor information. For example, the autonomous vehicle 100b may provide an XR object corresponding to a real object or an object in the screen to the occupant by outputting an XR image with a HUD.

In this case, when the XR object is output to the HUD, at least a portion of the XR object may be output to overlap the actual object to which the passenger's gaze is directed. On the other hand, when the XR object is output to a display provided inside the autonomous vehicle 100b, at least a portion of the XR object may be output to overlap the object in the screen. For example, the autonomous vehicle 100b may output XR objects corresponding to objects such as a lane, other vehicles, traffic lights, traffic signs, two-wheeled vehicles, pedestrians, and buildings.

When the autonomous driving vehicle 100b, which is the target of control/interaction in the XR image, acquires sensor information from sensors including a camera, the autonomous driving vehicle 100b or the XR device 100c performs An XR image is generated, and the XR apparatus 100c may output the generated XR image. In addition, the autonomous vehicle 100b may operate based on a control signal input through an external device such as the XR device 100c or a user's interaction.

4 conceptually illustrates an autonomous driving mobile robot 400 according to embodiments of the present disclosure. The autonomous driving mobile robot 400 shown in FIG. 4 may include a configuration similar to or identical to that of the AI device 100 described above with reference to FIG. 1 . For example, referring to FIG. 4 , the autonomous mobile robot 400 may include a communication circuit 410 , an information collection device 420 , a memory 430 , a driving device 440 , and a processor 450 . have. However, this is only an example, and the embodiment of the present disclosure is not limited thereto. For example, at least one of the components of the aforementioned autonomous driving mobile robot may be omitted or one or more other components (eg, a microphone, a speaker, a display device, etc.) may be added.

The autonomous driving mobile robot 400 according to various embodiments of the present disclosure may determine an elevator boarding plan so that a person using the elevator is not uncomfortable or injured. The boarding plan is the boarding waiting position where the autonomous mobile robot 400 should be located within the platform until the elevator door is opened, and autonomous driving in the elevator interior (eg, inside the elevator car) while the elevator is operating (or moving). It may include at least one of a boarding position where the mobile robot 400 should be located and a movement path from the standby position to the boarding location. According to an embodiment, the autonomous driving mobile robot 400 may determine the elevator boarding plan based on the waiting condition of the elevator platform and the boarding condition inside the elevator, as will be described later with reference to the following drawings.

The communication circuit 410 may be configured to transmit/receive data to and from another robot or an external device (eg, a server). According to various embodiments, the communication circuit 410 may be a transceiver configured to transmit and receive a wireless signal, and the communication circuit 410 performs the function of the communication circuit 110 shown in FIG. 1 . can do. According to an embodiment, at least a part of data received from another robot or an external device may include information related to a boarding situation inside the elevator. For example, the communication circuit 410 may include, but is not limited to, a cellular communication circuit, a short-range wireless communication circuit, or a global navigation satellite system (GNSS) communication circuit.

The information collection device 420 may detect the surrounding environment of the autonomous driving mobile robot 400 and generate information about the detected surrounding environment (eg, information related to the waiting situation of the elevator platform). According to various embodiments, the information collection apparatus 420 may detect the user USER and generate information (eg, image data) for identifying the user USER according to the detection result. According to one embodiment, the information collection device 420 may include at least one sensor such as a camera, LIDAR, Radar, an ultrasonic sensor, a proximity sensor, or an optical sensor, but is limited thereto. no.

The memory 430 includes various components used by at least one component (eg, the communication circuit 410 , the information collection device 420 , the driving device 440 , or the processor 450 ) of the autonomous mobile robot 400 . data can be saved. According to various embodiments, the memory 430 may include at least one of a non-volatile memory device and a volatile memory device.

The driving device 440 may generate a driving force for moving the autonomous driving mobile robot 400 . According to various embodiments, the driving device 440 may be a motor, an actuator, or a steering device, but is not limited thereto. According to an embodiment, the driving device 440 may generate a driving force for walking or driving of the autonomous driving mobile robot 400 . For example, the autonomous driving mobile robot 400 may include a driving device or a walking device such as a wheel, a belt, or a leg, and may move by transmitting a driving force generated by the driving device 440 to the driving device or the walking device.

The processor 450 may be configured to control the overall operation of the autonomous mobile robot 400 . According to an embodiment, the processor 450 executes software (eg, a program) stored in the memory 430 , and a component (eg, a communication circuit (eg, a communication circuit) of the autonomous mobile robot 400 connected to the processor 450 . 410), the information collection device 420, the memory 430, or the driving device 440) may control at least one component. For example, the processor 450 may include a processor having an arithmetic processing function. For example, the processor 450 may include an arithmetic processing unit such as a central processing unit (CPU), a micro computer unit (MCU), or a graphics processing unit (GPU), but is not limited thereto.

According to various embodiments, the processor 450 may perform an operation for determining an elevator boarding plan of the autonomous mobile robot 400 , as will be described later.

The processor 450 may detect that the autonomous mobile robot 400 enters the elevator platform. According to an embodiment, the processor 450 may track the location of the autonomous mobile robot 400 based on an indoor location tracking technique, and compare it with pre-stored data (eg, map data) to be installed in a building. It can be determined whether the elevator enters the platform. However, this is only an example, and the embodiment of the present disclosure is not limited thereto. For example, the processor 450 tracks the location of the autonomous mobile robot 400 moving outdoors based on the outdoor location tracking technique, and compares it with pre-stored data to enter the platform of an elevator installed outside the building. It can also be determined whether According to another embodiment, the processor 450 may determine whether to enter the elevator platform by acquiring an image of the surrounding environment of the autonomous driving mobile robot 400 . For example, when the processor 450 processes the captured image to extract predefined features (eg, an elevator door, an elevator call button, etc.), it may be determined that the autonomous mobile robot 400 has entered the platform. .

In response to determining the entry into the elevator platform, the processor 450 may identify a first situation related to the interior of the elevator and a second situation related to the exterior of the elevator. The first situation is whether the number of people boarding the inside of the elevator (eg, elevating car) exceeds the specified number of people, whether the loading weight of the elevator exceeds the specified weight, whether there is free space inside the elevator, Whether a person with impaired mobility is on board, whether there is an auxiliary means of transport (e.g. wheelchair, patient transfer bed, bicycle, stroller, shopping cart, etc.) inside the elevator, or the destination of the person using the elevator (e.g., occupant) For example, it may be related to at least one of information (eg, getting off floor). In addition, the second situation is whether there is a waiting person outside the elevator (eg, at the elevator platform), the number of waiting people, whether a predefined waiting pattern (eg, a pattern to wait in line) is formed, or whether the walking vulnerable or It may relate to at least one of whether the auxiliary mobility means is included in the waiting personnel.

According to an embodiment, the processor 450 may identify the first situation based on information received from at least one external device disposed inside the elevator. For example, at least one weight measuring sensor configured to measure the loading weight, at least one camera configured to take an image inside the elevator, etc. may be installed inside the elevator. In this case, the processor 450 may acquire information collected through at least one weight measurement sensor and at least one camera and analyze it to obtain the first situation. However, this is only an example, and the embodiment of the present disclosure is not limited thereto. As another example, the first situation may be determined by an external device based on information collected through at least one sensor and at least one camera. In this case, the processor 450 receives information related to the first situation from the external device. may be obtained.

Also, the processor 450 may acquire the second situation based on information collected through at least one of the components of the autonomous mobile robot 400 (eg, the information collection device 420 ). However, this is only an example, and the embodiment of the present disclosure is not limited thereto. For example, at least one sensor configured to detect an object existing outside the elevator, at least one camera configured to take an image of the outside of the elevator, etc. may be installed outside the elevator. In this case, the processor 450 is at least one The second situation may be obtained by acquiring information collected through a sensor and at least one camera and analyzing it.

In response to acquiring the first situation and the second situation, the processor 450 may determine an elevator boarding plan of the autonomous mobile robot 400 .

According to an embodiment, the processor 450 may determine the boarding standby position as at least a part of the boarding plan based on at least a part of the first situation and the second situation. As described above, the boarding standby position may be a position in the platform where the autonomous driving mobile robot 400 should be located until the elevator door is opened. For example, the processor 450 may determine that a person who intends to get off the floor on which the autonomous driving mobile robot 400 is located is boarding the elevator or a person who wants to board the elevator on the floor where the autonomous driving mobile robot 400 is located. Based on the waiting situation, the waiting position may be determined so as not to disturb a person getting off or boarding. For example, the processor 450 may determine a partial area of the platform outside the boarding or disembarking flow of the elevator as the waiting position. In this case, the processor 450 may determine a partial area of the platform deviating from a location where the button for elevator call is installed by a certain distance and out of the boarding or disembarking flow as the waiting location. As another example, the processor 450 may detect a situation in which a person whose alighting destination is the floor where the autonomous driving mobile robot 400 is located does not board the elevator or a person who wants to board the elevator on the floor where the autonomous driving mobile robot 400 is located. Based on the waiting situation, the waiting position may be determined so as not to disturb the person on board. For example, the processor 450 may determine a partial area of the platform that is out of the boarding flow of the elevator as the waiting position. As another example, the processor 450 determines a situation in which a person whose alighting destination is the floor where the autonomous driving mobile robot 400 is located does not board the elevator and a person who wants to board the elevator on the floor where the autonomous driving mobile robot 400 is located. Based on this non-waiting situation, the waiting position can be determined so that the waiting time for boarding is not delayed. For example, the processor 450 may determine a predetermined area of the platform adjacent to the door of the elevator or facing the door direction as the standby position. As another example, the processor 450 may determine a waiting position along waiting lines based on a situation in which people who want to take the elevator wait in line.

According to an embodiment, the processor 450 may determine the boarding location as at least a part of the boarding plan based on at least a part of the first situation. The boarding position may be a position inside the elevator where the autonomous mobile robot 400 should be located while the elevator is operating (or moving), as described above. For example, the processor 450 may analyze the image taken inside the elevator to check the distribution state of the occupants, and may determine a free space inside the elevator where the occupants are not relatively located as the boarding position. In addition, when the free space inside the elevator satisfies the specified size condition, the processor 450 determines a part of the free space that is out of the free space by a certain distance from the location where the button for selecting a destination is installed as the boarding location, so that the passenger's destination is set. You can make sure it doesn't get in the way. Additionally, the processor 450 may determine that boarding in the elevator is impossible if there is not enough free space for the autonomous driving mobile robot 400 to board or the loading weight of the elevator exceeds the specified weight. have. In this case, the processor 450 may perform an operation of re-calling the elevator or determining a boarding plan using another elevator.

According to an embodiment, the processor 450 may determine a movement path into the elevator based on the determined standby position and the determined boarding position. The moving path may be a moving path from the standby position to the boarding position, and the processor 450 may determine it in consideration of a reduction in boarding time, safety for passengers, and the like.

In response to determining the boarding plan of the elevator, the processor 450 may perform an elevator boarding operation based on the boarding plan. According to an embodiment, the processor 450 may perform a boarding operation based on the first situation and the second situation. For example, the processor 450 may determine that the person using the elevator is not able to board so that the person who uses the elevator does not feel discomfort when the weak walking person or the auxiliary transportation means is riding in the elevator. In this case, the processor 450 may determine a boarding plan to re-call the elevator or use another elevator. Due to this, it is possible to improve the safety of the person riding the elevator and to solve the inconvenience that people may feel. As another example, the processor 450 determines that it is impossible to board the elevator so that the person using the elevator does not feel inconvenience, or the autonomous driving mobile robot 400 is more vulnerable to walking when the walking vulnerable or auxiliary means is waiting at the platform. You can make it a lower priority.

The autonomous driving mobile robot 400 according to various embodiments of the present disclosure includes a communication circuit 410 configured to send and receive signals, an information collection device 420 configured to sense a surrounding environment, and a movement of the autonomous driving mobile robot. It may include a driving device 440 configured to implement and a processor 450 configured to control the autonomous mobile robot. According to an embodiment, the processor 450 obtains first information related to the internal situation of the elevator and second information related to the external situation of the elevator in response to detecting the entrance to the elevator platform, and the second information It is possible to determine a boarding plan for the elevator based on the first information and the second information, and control to perform a boarding operation based on the determined boarding plan. For example, the boarding plan may include at least one of a waiting location in the platform, a boarding location in the elevator, or a movement path from the waiting location to the boarding location.

According to various embodiments, the processor 450 may control to obtain the first information from at least one external device through the communication circuit 410 .

According to various embodiments, the processor 450 may control to acquire the second information through the information collection device 420 . According to an embodiment, the information collection device 420 may include at least one sensor.

According to various embodiments, the processor 450 may control to determine the free space inside the elevator based on the first information, and determine the boarding position in the elevator based on the checked free space. have.

According to various embodiments, the processor 450 may control to determine the operation button inside the elevator based on the first information, and determine the boarding position in the elevator based on the position of the identified operation button. have.

According to various embodiments, the processor 450 checks the waiting lines inside the platform based on the second information, and controls to determine a waiting position in the platform based on the checked queue. can do.

According to various embodiments, the processor 450 checks the boarding or disembarking flow line for the elevator based on the first information and the second information, and based on the confirmed flow line, a waiting position in the platform can be controlled to determine

According to various embodiments, the processor 450 may control to determine the operation button outside the elevator based on the second information, and to determine the waiting position in the platform based on the confirmed position of the operation button. have.

According to various embodiments, when it is determined that boarding in the elevator is impossible, the processor 450 may control to search for another elevator and determine a boarding plan.

According to various embodiments, the processor 450 monitors the situation inside the elevator based on the first information and the second information after performing the boarding operation in the elevator, and based on the monitoring result, the It can be controlled to determine a disembarkation plan for disembarking from the elevator. According to an embodiment, the getting off plan may include one of a forward getting off or a backward getting off of the autonomous mobile robot 400 .

5 is a flowchart 500 illustrating an operation method of the autonomous driving mobile robot 400 according to embodiments of the present disclosure. In the following embodiment, each operation may be performed sequentially, but is not necessarily performed sequentially. In addition, the following operations may be performed by the processor 450 of the autonomous driving mobile robot 400 or implemented as instructions executable by the processor 450 .

4 and 5 , according to various embodiments, the autonomous driving mobile robot 400 may detect entry into the elevator platform in operation S510. According to an embodiment, as described above with reference to FIG. 4 , the autonomous driving mobile robot 400 may detect an entrance to the platform using a location tracking technique and an image analysis technique. For example, a transmitting device that continuously or periodically transmits a wireless signal may be installed around the platform, and the autonomous driving mobile robot 400 detects a wireless signal transmitted by the transmitting device to determine whether or not to enter the elevator platform. can be judged However, this is only an example, and the embodiment of the present disclosure is not limited thereto, and the autonomous mobile robot 400 may use various known technologies to detect an entrance to the platform. According to various embodiments, the autonomous mobile robot 400 may call an elevator in response to detecting the entrance to the platform. According to an embodiment, the autonomous driving mobile robot 400 may call the elevator by recognizing and pressing the elevator call button installed in the platform. However, this is only an example, and the embodiment of the present disclosure is not limited thereto, and the autonomous mobile robot 400 may use various technologies such as a method of calling an elevator by wirelessly transmitting a call signal to the elevator.

According to various embodiments, the autonomous mobile robot 400 may acquire first information related to the inside of the elevator and second information related to the outside of the elevator in operation S520 . The first information is whether the number of people boarding the inside of the elevator (eg, elevating car) exceeds the specified number of people, whether the loading weight of the elevator exceeds the specified weight, whether there is free space inside the elevator, It may be information for determining at least one of whether a person vulnerable to walking has boarded, whether there is an auxiliary means of transportation inside the elevator, or the destination (eg, getting off floor) information of a person (eg, a passenger) using the elevator . In addition, the second information may include whether there are waiting people outside the elevator (eg, at the elevator platform), the number of waiting people, whether a predefined waiting pattern (eg, a pattern to wait in line) is formed, or whether walking vulnerable or It may be information for determining at least one of whether the auxiliary moving means is included in the waiting person. According to an embodiment, the autonomous mobile robot 400 may acquire the first information through at least one external device installed inside the elevator. Also, the autonomous driving mobile robot 400 may acquire the second information through at least one information collecting device 420 installed in the autonomous driving mobile robot 400 .

According to various embodiments, the autonomous driving mobile robot 400 may determine an elevator boarding plan based on at least a portion of the first information and the second information in operation S530 . According to an embodiment, the autonomous driving mobile robot 400 considers the situation inside the elevator and the situation outside the elevator, and the boarding standby position in the platform, the boarding position in the elevator, or the movement path from the standby position to the boarding position At least one of them may be determined as a boarding plan.

According to various embodiments, the autonomous driving mobile robot 400 may perform an elevator boarding operation based on a boarding plan in operation S540 . According to an embodiment, the autonomous driving mobile robot 400 may move forward toward the door of the elevator to board, and maintain the boarding state so that the rear of the autonomous driving mobile robot 400 faces the door. According to another embodiment, the autonomous driving mobile robot 400 may perform a direction change before boarding the elevator, and may ride backward toward the door of the elevator. This can solve the problem of contact with the occupant as the autonomous driving mobile robot 400 performs a direction change (or rotation) in the elevator space. In addition, after completing the boarding operation, the autonomous mobile robot 400 may move to a destination by inputting a destination designation button installed inside the elevator, or may move to a destination by wirelessly transmitting a destination designation signal. Additionally, the autonomous mobile robot 400 may process the boarding plan so that the user can recognize it when boarding the elevator. For example, the boarding plan may be output in various forms through the output device 150 (eg, a speaker, a display, etc.) provided in the autonomous mobile robot 400 . As another example, the autonomous driving mobile robot 400 may further include a device capable of irradiating light (eg, a hologram device, a projector device, etc.). In this case, the autonomous driving mobile robot 400 may display at least one of a boarding standby position, a boarding position, or a movement path by irradiating light to, for example, a platform floor and an elevator interior floor.

6 is a flowchart 600 illustrating a method of determining a boarding plan of the autonomous mobile robot 400 according to embodiments of the present disclosure. The operations of FIG. 6 described below may represent various embodiments of operation S530 of FIG. 5 . In addition, in the following embodiment, each operation is not necessarily performed sequentially, and may be implemented by the processor 450 of the autonomous driving mobile robot 400 or may be implemented as instructions executable by the processor 450 .

4 and 6 , according to various embodiments, the autonomous driving mobile robot 400 may check the distribution of occupants in the elevator based on the first information in operation S610. According to an embodiment, the autonomous driving mobile robot 400 may check the free space inside the elevator based on the occupant distribution.

According to various embodiments, the autonomous driving mobile robot 400 may determine a boarding position inside the elevator based on the occupant distribution in operation S620 . According to an embodiment, the autonomous driving mobile robot 400 may determine a space having a predetermined size among the confirmed free spaces as the boarding position, as will be described later with reference to FIGS. 7 to 9 . For example, the predetermined size may be such that a person riding in the elevator and the autonomous driving mobile robot 400 do not come into contact with each other.

According to various embodiments of the present disclosure, the autonomous driving mobile robot 400 may identify a waiting pattern for a passenger waiting to board, based on the second information in operation S630 . According to an embodiment, the waiting pattern may include a regular waiting pattern of waiting in line and waiting for boarding, and an irregular waiting pattern of waiting in an irregular manner at the elevator platform.

According to various embodiments, the autonomous driving mobile robot 400 may determine a boarding standby position based on the standby pattern in operation S640 . According to an embodiment, when the waiting people waiting at the platform form a regular waiting pattern, the autonomous driving mobile robot 400 determines the boarding waiting position based on the formed queue, as will be described later with reference to FIGS. 12 and 13 . can decide According to another embodiment, when the waiting people waiting at the platform form an irregular waiting pattern, the autonomous driving mobile robot 400, as will be described later with reference to FIGS. 12, 14 and 15, the boarding flow of the elevator, It is possible to determine the boarding standby position based on at least one of the alighting flow and the position of the elevator call button installed outside the elevator.

According to various embodiments, the autonomous driving mobile robot 400 may perform a boarding standby operation in operation S650. According to an embodiment, the autonomous driving mobile robot 400 may perform a standby operation at the boarding standby position until the called elevator arrives.

7 is a flowchart 700 illustrating a method for determining a boarding position of the autonomous driving mobile robot 400 according to embodiments of the present disclosure. 8 and 9 are views 800 and 900 for explaining an operation of determining a boarding position according to an embodiment of the present disclosure. The operations of FIG. 7 described below may represent various embodiments of the operation S620 of FIG. 6 . In addition, in the following embodiment, each operation is not necessarily performed sequentially, and may be implemented by the processor 450 of the autonomous driving mobile robot 400 or may be implemented as instructions executable by the processor 450 .

4 and 7 , according to various embodiments, the autonomous driving mobile robot 400 may determine whether a occupant exists in the elevator in operation S710. According to an embodiment, the autonomous driving mobile robot 400 may determine the presence of a occupant from the time the elevator is called until the called elevator arrives. For example, the presence or absence of a passenger may be determined based on the first information, as described above.

According to various embodiments, when it is determined that the occupant is present, the autonomous mobile robot 400 may determine the boarding position of the autonomous mobile robot 400 in the elevator through operations S720 and S730. According to an embodiment, the autonomous driving mobile robot 400 may check the free space inside the elevator in operation S720. The free space inside the elevator may be an elevator space in which the autonomous mobile robot 400 can ride. According to an embodiment, the autonomous mobile robot 400 compares the size of the internal space of the elevator occupied by the person on board (or the internal space of the elevator that is not occupied) with the total size of the elevator's internal space to determine the free space. can be checked However, this is only an example, and the embodiment of the present disclosure is not limited thereto. For example, the free space may be determined based on the number of people riding in the elevator and the maximum number of people who can ride the elevator, or may be determined based on the loading weight of the elevator and the maximum loading weight that can be loaded in the elevator. In addition, the autonomous driving mobile robot 400 may determine the first position inside the elevator as the boarding position based on the free space in operation S730. According to an embodiment, the first location may be at least some of the free space inside the elevator. For example, the autonomous driving mobile robot 400 is, as shown in FIG. 8 , at least one operation button 820 installed in the rear of the elevator door 830 and installed in the elevator interior 810 on the left side of the arrangement. When detecting a situation in which the occupants 840 are distributed in the direction and the right direction, the free space P1 in which the occupants are not distributed may be determined as the first position ( 800 ).

According to various embodiments, when it is determined that the occupant does not exist, the autonomous driving mobile robot 400 may determine the boarding position of the autonomous driving mobile robot 400 in the elevator through operations S740 and S750. According to an embodiment, the autonomous driving mobile robot 400 may check the position of at least one operation button installed inside the elevator in operation S740. The operation button installed inside the elevator may include a button for designating a destination (eg, getting off floor), a button for instructing opening and closing an elevator door, and the like. For example, the autonomous driving mobile robot 400 may capture an image inside the elevator and analyze it to determine the position of the operation button. However, this is only an example, and the embodiment of the present disclosure is not limited thereto. For example, the autonomous mobile robot 400 may check the position of the operation button based on information received from an external device installed inside the elevator. Also, according to an embodiment, the autonomous driving mobile robot 400 may determine the second position inside the elevator as the boarding position based on the position of the operation button in operation S750 . For example, the second location may be a space in which the contact between the person riding the elevator and the autonomous mobile robot 400 is relatively small. For example, the autonomous driving mobile robot 400 detects a situation in which at least one operation button 920 is installed in the left direction of the elevator interior 910 with respect to the elevator door 930 as shown in FIG. In this case, the operation button 920 and the inner space P2 of the elevator separated by a predetermined distance or more may be determined as the second position (900). Due to this, it is possible to solve the problem that the input of the operation button 920 by the person on board or the person on board is blocked by the autonomous driving mobile robot 400 .

In the foregoing disclosure, the boarding position is determined based on the free space of the elevator when there is a passenger, and when there is no passenger, the boarding position is determined based on the position of the operation button. no. For example, the autonomous driving mobile robot 400 may consider the position of the operation button to determine the boarding position even when a passenger is present.

10 is a flowchart 1000 illustrating a method of checking the free space inside the elevator of the autonomous driving mobile robot 400 according to embodiments of the present disclosure. And, FIG. 11 is a view 1100 for explaining the operation of the autonomous driving mobile robot 400 that cannot ride an elevator according to an embodiment of the present disclosure. The operations of FIG. 10 described below may represent various embodiments of the operation S720 of FIG. 7 . In addition, in the following embodiment, each operation is not necessarily performed sequentially, and may be implemented by the processor 450 of the autonomous driving mobile robot 400 or may be implemented as instructions executable by the processor 450 .

4 and 10 , according to various embodiments, the autonomous driving mobile robot 400 may determine whether it is possible to board an elevator in operation S1010.

According to an embodiment, the autonomous driving mobile robot 400 may determine whether boarding is possible based on the situation inside the elevator. For example, when the free space inside the elevator is not checked, the autonomous driving mobile robot 400 may determine that it is impossible to board, otherwise it may determine that it is available for boarding. As another example, the autonomous driving mobile robot 400 has free space inside the elevator, but cannot be boarded when a space is not secured so that a person in the elevator and the autonomous driving mobile robot 400 do not contact each other. can also be judged as As another example, the autonomous driving mobile robot 400 may determine that the boarding is not possible if there is a weak walking person or an auxiliary moving means inside the elevator.

According to another embodiment, the autonomous driving mobile robot 400 may consider the situation outside the elevator to determine whether boarding is possible. For example, although free space exists in the autonomous driving mobile robot 400 , the total space occupied by adding the space occupied by the boarding of the waiting personnel and the space occupied by the autonomous driving mobile robot 400 boarding the free space. If it is less than that, it may be determined that the boarding is not possible. As another example, the autonomous driving mobile robot 400 may determine that it is impossible to board even if there is no walking vulnerable or auxiliary moving means inside the elevator, but there is a walking vulnerable or auxiliary moving means among waiting people at the platform. .

According to various embodiments, when it is determined that the autonomous driving mobile robot 400 can board the elevator, it may perform a boarding position determination operation in operation S1020. According to an embodiment, the autonomous driving mobile robot 400 may perform operation S730 illustrated in FIG. 7 .

According to various embodiments, the autonomous driving mobile robot 400 may perform operations S1030 and S1040 when it is determined that it is impossible to board the elevator. According to an embodiment, the autonomous driving mobile robot 400 may search for an elevator that can be boarded in operation S1030. For example, as shown in FIG. 11 , in the case of an elevator system in which a plurality of elevators are installed, the autonomous mobile robot 400 determines another elevator among the plurality of elevators as a boarding elevator, and moves to the platform of the determined elevator. You can move 1110 . In this case, the autonomous driving mobile robot 400 may determine the elevator closest to the floor on which the autonomous driving mobile robot 400 is located among the plurality of elevators as the boarding elevator, or may determine the elevator with a relatively small number of waiting people as the boarding elevator. Also, according to an embodiment, the autonomous driving mobile robot 400 may determine a boarding plan for another found elevator in operation S1040. For example, the autonomous driving mobile robot 400 may perform at least one operation shown in FIGS. 6 and 7 described above and FIG. 16 to be described later. However, this is only an example, and the embodiment of the present disclosure is not limited thereto. For example, the autonomous driving mobile robot 400 may wait until the elevator moves without boarding the elevator determined to be in an impossible state. In addition, the autonomous driving mobile robot 400 may call the moved elevator again, and may determine a boarding plan for the called elevator.

12 is a flowchart 1200 illustrating a method of determining a boarding standby position of the autonomous driving mobile robot 400 according to embodiments of the present disclosure. 13 to 15 are views 1300, 1400, and 1500 for explaining an operation of determining a boarding standby position according to an embodiment of the present disclosure. The operations of FIG. 12 described below may represent various embodiments of operation S640 of FIG. 6 . In addition, in the following embodiment, each operation is not necessarily performed sequentially, and may be implemented by the processor 450 of the autonomous driving mobile robot 400 or may be implemented as instructions executable by the processor 450 .

Referring to FIGS. 4 and 12 , according to various embodiments, the autonomous driving mobile robot 400 may determine whether there is a waiting person for boarding in an elevator (eg, an elevator platform) in S1210. According to an embodiment, the autonomous driving mobile robot 400 may determine whether there is a waiting person for boarding from the time the elevator is called until the called elevator arrives. For example, as described above, the presence or absence of a waiting person may be determined based on the second information.

According to various embodiments, when it is determined that there is a waiting person for boarding, the autonomous driving mobile robot 400 may determine the boarding waiting position of the autonomous driving mobile robot 400 with respect to the outside of the elevator through operations S1220 to S1250. According to an embodiment, the autonomous driving mobile robot 400 may check the waiting pattern of the waiting person in operation S1220. For example, the waiting pattern may include a waiting pattern of waiting by forming a queue (eg, waiting in a line to wait for boarding) and a waiting pattern of waiting irregularly gathered at an elevator platform. According to an embodiment, the autonomous mobile robot 400 may determine whether a waiting pattern in which a queue is formed is confirmed in operation S1230. According to an embodiment, when a waiting pattern in which a queue is formed is confirmed, the autonomous driving mobile robot 400 may determine a third location outside the elevator as a boarding waiting location based on the queue in S1240. For example, as shown in FIG. 13 , the autonomous driving mobile robot 400 may determine a boarding waiting position 1310 to follow the last waiting person forming a queue ( 1300 ). According to an embodiment, when a waiting pattern in which a queue is not formed is confirmed, the autonomous driving mobile robot 400 performs a fourth position outside the elevator based on the position of an external operation button (eg, an elevator call button) in S1250. can be determined as the boarding standby position. For example, as shown in FIG. 14 , the autonomous driving mobile robot 400 may determine a predetermined area 1410 of a platform that is a predetermined distance away from a position where the external button is installed as the standby position ( 1400 ). In addition, the autonomous mobile robot 400 determines a partial area 1410 that does not overlap with the boarding and disembarking movement line 1403 of the elevator among a certain area of the platform that is deviating a certain distance from the location where the external button 1410 is installed as the standby position. By doing so, it is possible to prevent a person using the elevator from coming into contact with the autonomous driving mobile robot 400 .

According to various embodiments, when it is determined that there is no person waiting for boarding, the autonomous driving mobile robot 400 may determine a boarding wait position through operation S1260. According to an embodiment, the autonomous driving mobile robot 400 may determine a fifth position outside the elevator as the boarding standby position based on the position of the entrance (eg, elevator door) of the elevator. For example, as shown in FIG. 15 , the autonomous mobile robot 400 may determine a predetermined area of a platform adjacent to the entrance or exit of the elevator as the standby position ( 1500 ). In addition, when there are no people getting on or getting off, the autonomous driving mobile robot 400 overlaps with the boarding and alighting flow line 1503 of the elevator in a certain area of the platform or a part adjacent to the external button 1501 . By determining the area 1510 as the standby position, it is possible to enable rapid elevator boarding.

16 is a flowchart 1600 illustrating an elevator boarding method of the autonomous mobile robot 400 according to embodiments of the present disclosure. The operations of FIG. 16 described below may represent various embodiments of the operation S540 of FIG. 5 . In addition, in the following embodiment, each operation is not necessarily performed sequentially, and may be implemented by the processor 450 of the autonomous driving mobile robot 400 or may be implemented as instructions executable by the processor 450 .

4 and 16 , according to various embodiments, the autonomous driving mobile robot 400 may determine whether the called elevator arrives in operation S1610.

According to various embodiments, when determining the arrival of the elevator, the autonomous driving mobile robot 400 may perform an elevator boarding operation in operation S1620. According to an embodiment, the autonomous driving mobile robot 400 may move to a desired destination after completing the boarding operation.

According to various embodiments, when it is determined that the elevator does not arrive, the autonomous mobile robot 400 may perform an elevator standby operation through operations S1630 and S1640. According to an embodiment, the autonomous mobile robot 400 may monitor the internal and external conditions of the elevator based on the first information and the second information in operation S1630. For example, the first information may be changed according to a person boarding or alighting from a floor through which the elevator passes until the elevator arrives. In addition, the second information may be changed according to the number of people waiting until the elevator arrives. In consideration of this situation, the autonomous driving mobile robot 400 may monitor the first information and the second information until the called elevator arrives. Also, according to an embodiment, the autonomous driving mobile robot 400 may change the boarding position and the boarding standby position based on the monitoring result in S1640 .

17 is a flowchart 1700 illustrating a method of getting off the elevator of the autonomous mobile robot 400 according to embodiments of the present disclosure. 18 and 19 are views 1800 and 1900 for explaining a disembarkation operation of the autonomous driving mobile robot 400 according to an embodiment of the present disclosure. The operations of FIG. 17 described below may represent various embodiments of operation S540 of FIG. 5 . In addition, in the following embodiment, each operation is not necessarily performed sequentially, and may be implemented by the processor 450 of the autonomous driving mobile robot 400 or may be implemented as instructions executable by the processor 450 .

4 and 17 , according to various embodiments, the autonomous mobile robot 400 may move to a destination floor using an elevator in operation S1710. According to an embodiment, as described above, the autonomous mobile robot 400 may move to a destination by inputting a destination designation button installed inside the elevator, or may move to a destination by wirelessly transmitting a destination designation signal.

According to various embodiments, the autonomous driving mobile robot 400 may monitor the internal situation of the elevator based on the first information in operation S1720. According to an embodiment, the monitoring internal situation of the elevator may be an internal situation of the elevator that is changed according to a person getting on or off while the elevator moves to a destination. According to another embodiment, the autonomous mobile robot may use the second information to monitor the internal situation of the elevator.

According to various embodiments, the autonomous driving mobile robot 400 may determine an elevator getting off plan based on the monitoring result in operation S1730. According to an embodiment, the getting off plan may be related to the forward getting off or the backward getting off of the autonomous mobile robot 400 . For example, as described above, the autonomous driving mobile robot 400 may perform forward boarding to maintain a state in which the rear of the autonomous driving mobile robot 400 faces the door while the elevator moves. In this state, the autonomous driving mobile robot 400 may determine a reverse getting off plan in consideration of the internal situation of the elevator in which an appropriate space for changing direction is not secured. In addition, the autonomous driving mobile robot 400 may determine a forward disembarkation plan in consideration of the internal situation of the elevator in which an appropriate space for changing directions is secured.

According to various embodiments, the autonomous mobile robot 400 may determine whether the elevator has arrived at the destination floor in operation S1740. According to an embodiment, the autonomous mobile robot 400 may receive a signal indicating the arrival floor from the elevator, and may determine whether the elevator has arrived at the destination floor using this. However, this is only an example, and the embodiment of the present disclosure is not limited thereto. For example, the autonomous driving mobile robot 400 may determine whether to arrive by using information collected through the information collection device 420 .

According to various embodiments, the autonomous driving mobile robot 400 may perform an alighting operation according to an alighting plan in operation S1750. According to an embodiment, as shown in FIG. 18 , the autonomous driving mobile robot 400 changes direction in the elevator so that the front (F) of the autonomous driving mobile robot 400 faces the door according to the battery getting off plan. , and a disembarkation operation may be performed by moving forward toward the door (1800). According to another embodiment, as shown in FIG. 19 , the autonomous driving mobile robot 400 maintains a state in which the rear (B) of the autonomous driving mobile robot 400 faces the door according to the reverse getting off plan, and the door It is possible to perform a disembarkation operation by moving backward toward (1900).

In the operating method of the autonomous mobile robot 400 according to various embodiments of the present disclosure, in response to detecting an entry into the elevator platform, first information related to the internal situation of the elevator and second information related to the external situation of the elevator It may include an operation of obtaining information, an operation of determining a boarding plan for the elevator based on the first information and the second information, and an operation of boarding the elevator based on the determined boarding plan. According to an embodiment, the boarding plan may include at least one of a waiting location in the platform, a boarding location in the elevator, or a movement path from the waiting location to the boarding location.

According to various embodiments, the first information may be acquired from at least one external device through the communication circuit 410 included in the autonomous driving mobile robot 400 , and the second information may be the autonomous driving mobile robot 400 . It may be acquired through at least one sensor (eg, the information collection device 420 ) included in the 400 .

According to various embodiments, the determining of the boarding plan includes, based on the first information, checking the free space inside the elevator and determining the boarding position in the elevator based on the checked free space It may include an action to

According to various embodiments, the operation of determining the boarding plan determines the boarding position in the elevator based on the operation of confirming the operation button inside the elevator and the position of the checked operation button on the basis of the first information It may include an action to

According to various embodiments, the determining of the boarding plan includes checking waiting lines inside the platform based on the second information and determining a waiting position in the platform based on the checked queue. It may include an action to

According to various embodiments, the determining of the boarding plan may include, based on the first information and the second information, checking the boarding or disembarking line for the elevator and the inside of the platform based on the confirmed moving line. It may include an operation of determining a standby position.

According to various embodiments, the operation of determining the boarding plan determines a waiting position in the platform based on the operation of confirming the operation button outside the elevator and the position of the confirmed operation button based on the second information It may include an action to

According to various embodiments, the operation method of the autonomous driving mobile robot 400 may include an operation of determining a boarding plan by searching for another elevator when it is determined that boarding in the elevator is impossible.

According to various embodiments, the operation method of the autonomous mobile robot 400 is based on the operation of monitoring the situation inside the elevator based on the first information and the second information while riding in the elevator and the monitoring result Thus, it may include the operation of determining an alighting plan for getting off the elevator. According to an embodiment, the getting off plan may include one of a forward getting off or a backward getting off of the autonomous driving mobile robot 400 .

The autonomous driving mobile robot 400 and its operating method according to the embodiments of the present disclosure may be implemented with instructions that are stored in a computer-readable storage medium and executed by the processor 450 .

A storage medium, whether directly and/or indirectly, in a raw, formatted, organized or any other accessible state, may include a relational database, a non-relational database, an in-memory database, Alternatively, it may include a database, including a distributed one, such as any other suitable database capable of storing data and allowing access to such data through a storage controller. In addition, the storage medium includes a primary storage device (storage), a secondary storage device, a tertiary storage device, an offline storage device, a volatile storage device, a non-volatile storage device, a semiconductor storage device, a magnetic storage device, an optical storage device, a flash. It may include any type of storage device, such as a storage device, a hard disk drive storage device, a floppy disk drive, magnetic tape, or other suitable data storage medium.

Although the present disclosure has been described with reference to the embodiment shown in the drawings, this is merely exemplary, and those of ordinary skill in the art will understand that various modifications and equivalent other embodiments are possible therefrom. Therefore, the true technical protection scope of the present disclosure will have to be determined by the technical spirit of the appended claims.

Claims (20)

In the self-driving mobile robot,
a communication circuit configured to send and receive signals;
an information collection device configured to sense a surrounding environment;
a driving device configured to implement the movement of the autonomous mobile robot; and
a processor configured to control the autonomous driving mobile robot;
The processor is
In response to detecting the entry into the elevator platform, obtain first information related to the situation inside the elevator and second information related to the situation outside the elevator,
determine a boarding plan to the elevator based on the first information and the second information,
Based on the determined boarding plan, control to perform a boarding operation,
The boarding plan includes at least one of a waiting position in the platform, a boarding location in the elevator, or a movement path from the waiting location to the boarding location. The method of claim 1, wherein the processor comprises:
An autonomous driving mobile robot that controls to obtain the first information from at least one external device through the communication circuit. The method of claim 1,
The information collection device includes at least one sensor,
The processor is
An autonomous driving mobile robot that controls to acquire the second information through the information collection device. The method of claim 1, wherein the processor comprises:
Based on the first information, check the free space inside the elevator,
An autonomous driving mobile robot that controls to determine a boarding position in the elevator based on the identified free space. The method of claim 1, wherein the processor comprises:
Based on the first information, check the operation button inside the elevator,
An autonomous driving mobile robot that controls to determine a boarding position in the elevator based on the confirmed position of the operation button. The method of claim 1, wherein the processor comprises:
Based on the second information, check the waiting lines inside the platform,
An autonomous driving mobile robot that controls to determine a waiting position in the platform based on the confirmed queue. The method of claim 1, wherein the processor comprises:
Based on the first information and the second information, check the boarding or alighting flow for the elevator,
An autonomous driving mobile robot that controls to determine a waiting position in the platform based on the identified movement line. The method of claim 1, wherein the processor comprises:
Based on the second information, check the operation button outside the elevator,
An autonomous driving mobile robot that controls to determine a standby position in the platform based on the confirmed position of the operation button. The method of claim 1, wherein the processor comprises:
When it is determined that it is impossible to board the elevator, the autonomous driving mobile robot searches for another elevator and controls to determine a boarding plan. The method of claim 1, wherein the processor comprises:
After performing the boarding operation to the elevator, monitoring the situation inside the elevator based on the first information and the second information,
Control to determine an alighting plan for getting off the elevator based on the monitoring result,
and the getting off plan includes one of a forward getting off and a backward getting off of the autonomous driving mobile robot. In the operation method of the autonomous driving mobile robot,
in response to detecting the entry into the elevator platform, obtaining first information related to the situation inside the elevator and second information related to the situation outside the elevator;
determining a boarding plan for the elevator based on the first information and the second information; and
Based on the determined boarding plan, comprising the operation of boarding the elevator,
The boarding plan includes at least one of a waiting location in the platform, a boarding location in the elevator, or a movement path from the waiting location to the boarding location. The method of claim 11 , wherein the first information comprises:
A method of acquiring from at least one external device through a communication circuit included in the autonomous mobile robot. The method of claim 11, wherein the second information,
A method of acquiring through at least one sensor included in the autonomous driving mobile robot. The method of claim 11, wherein the determining of the boarding plan comprises:
based on the first information, checking the free space inside the elevator; and
and determining a boarding position in the elevator based on the identified free space. The method of claim 11, wherein the determining of the boarding plan comprises:
Based on the first information, the operation of confirming the operation button inside the elevator; and
and determining a boarding position in the elevator based on the identified position of the operation button. The method of claim 11, wherein the determining of the boarding plan comprises:
checking waiting lines inside the platform based on the second information; and
and determining a waiting position in the platform based on the identified queue. The method of claim 11, wherein the determining of the boarding plan comprises:
Based on the first information and the second information, the operation of checking the boarding or disembarking flow for the elevator; and
and determining a waiting position in the platform based on the identified movement line. The method of claim 11, wherein the determining of the boarding plan comprises:
Based on the second information, the operation of confirming the operation button outside the elevator; and
and determining a standby position in the platform based on the identified position of the operation button. 12. The method of claim 11,
The method further comprising the operation of determining a boarding plan by searching for another elevator when it is determined that boarding in the elevator is impossible. 12. The method of claim 11,
After performing the boarding operation in the elevator, the operation of monitoring the situation inside the elevator based on the first information and the second information; and
Based on the monitoring result, further comprising the operation of determining an alighting plan for getting off the elevator,
The getting off plan includes one of a forward getting off and a backward getting off of the autonomous mobile robot.

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