Robot cleaner and method for predicting cleaning time thereof

Abstract

A robot cleaner is disclosed. The robot cleaner includes: a sensor unit; a memory in which an artificial intelligence model for outputting an estimated cleaning time of the robot cleaner for an area on the basis of information on the area is stored; and one or more processors for acquiring information on an area in an indoor space using the sensor unit while the robot cleaner is cleaning the area, and inputting the obtained information to the artificial intelligence model to acquire an estimated cleaning time for the area from the artificial intelligence model. The information on the area includes cell information about a map of the area, environment information about the area, and type information about an obstacle in the area.

Description

Robot vacuum cleaner and method for predicting its cleaning time

[0001] The present disclosure relates to a robot vacuum cleaner for cleaning an indoor space and a method for predicting a cleaning time thereof.

[0002] Beyond simple repetitive tasks, robots can sense their surroundings in real time using sensors, cameras, and other tools, gather information, and navigate autonomously. These robots are currently being used in a wide range of fields, with robot vacuum cleaners being a prime example.

[0003] Robot vacuum cleaners can clean indoor spaces by moving around and sucking up debris. Typically, robot vacuum cleaners cannot predict how long a cleaning will take. This can cause inconvenience to users and hinder efficient cleaning.

[0004] A robot cleaner according to an embodiment of the present disclosure includes a sensor unit, a memory storing an artificial intelligence model that outputs an estimated cleaning time of the robot cleaner for an area based on information about the area, and one or more processors. The one or more processors acquire information about the area using the sensor unit while the robot cleaner cleans the area within an indoor space. The one or more processors input the acquired information into the artificial intelligence model, and acquire an estimated cleaning time for the area from the artificial intelligence model. The information about the area includes cell information of a map for the area, environmental information about the area, and information about the type of obstacles within the area.

[0005] Additionally, cell information for the area may include information about cells in which objects exist and cells in which no objects exist among multiple cells of a map for the area. Environmental information for the area may include information about a location where the robot cleaner collided with an object within the area and information about the location of a cliff within the area.

[0006] Additionally, the memory may store cleaning history information for the area. The cleaning history information may include information about the area obtained using the sensor unit when the robot cleaner cleaned the area, and information about the cleaning time taken to clean the area.

[0007] In addition, the one or more processors may obtain the most recent cleaning history information among the cleaning history information for the area stored in the memory, obtain first information about a portion of the area using the sensor unit while the robot cleaner cleans the area, identify second information about the portion of the area among the information about the area included in the most recent cleaning history information, and if the first information is identified as being different from the second information, obtain third information about the remaining area among the information about the area included in the most recent cleaning history information, and input the first information and the third information into the artificial intelligence model to obtain an expected cleaning time for the area.

[0008] Additionally, the robot vacuum cleaner according to the present disclosure may further include a communication interface. The one or more processors may transmit the acquired expected cleaning time to a mobile device via the communication interface.

[0009] Additionally, the robot vacuum cleaner according to the present disclosure may further include a communication interface. The one or more processors may transmit a notification message to a mobile device via the communication interface, requesting removal of obstacles within the area, if the acquired estimated cleaning time is greater than the cleaning time included in the most recent cleaning history information.

[0010] In addition, when cleaning of the area is completed, the one or more processors can train the artificial intelligence model based on information about the area obtained using the sensor unit while the robot cleaner is cleaning the area and the cleaning time required for the area.

[0011] In addition, when cleaning of the area is completed, the one or more processors may store information about the area obtained using the sensor unit while the robot cleaner is cleaning the area and the cleaning time for the area in the memory.

[0012] A method for predicting a cleaning time of a robot cleaner, including a memory storing an artificial intelligence model that outputs an expected cleaning time of a robot cleaner for an area based on information about the area according to an embodiment of the present disclosure, comprises the steps of: obtaining information about the area using a sensor unit of the robot cleaner while the robot cleaner cleans the area in an indoor space; and inputting the obtained information into the artificial intelligence model to obtain an expected cleaning time for the area from the artificial intelligence model. The information about the area includes cell information of a map for the area, environmental information about the area, and type information of an obstacle in the area.

[0013] A non-transitory computer-readable medium storing computer instructions that cause a robot to perform an operation when executed by one or more processors of a robot cleaner including a memory storing an artificial intelligence model that outputs an expected cleaning time of a robot cleaner for an area based on information about the area according to an embodiment of the present disclosure, wherein the operation includes a step of obtaining information about the area using a sensor unit of the robot cleaner while the robot cleaner cleans an area in an indoor space, and a step of inputting the obtained information into the artificial intelligence model to obtain an expected cleaning time for the area from the artificial intelligence model. The information about the area includes cell information of a map for the area, environmental information about the area, and type information of an obstacle in the area.

[0014] FIG. 1 is a drawing for explaining the operation of a robot vacuum cleaner according to an embodiment of the present disclosure.

[0015] FIG. 2A is a block diagram for explaining the configuration of a robot vacuum cleaner according to an embodiment of the present disclosure.

[0016] FIG. 2b is a block diagram illustrating the configuration of a robot vacuum cleaner according to an embodiment of the present disclosure.

[0017] FIG. 3 is a drawing for explaining an example of cleaning history information according to an embodiment of the present disclosure.

[0018] FIG. 4 is a flowchart illustrating a method for predicting an expected cleaning time by a robot vacuum cleaner according to an embodiment of the present disclosure.

[0019] FIG. 5 is a diagram illustrating an example in which a robot vacuum cleaner acquires the most recent cleaning history information according to an embodiment of the present disclosure.

[0020] FIGS. 6 and 7 are drawings illustrating an example of a method for a robot cleaner to obtain an expected cleaning time for an area using an artificial intelligence model according to an embodiment of the present disclosure.

[0021] FIG. 8 and FIG. 9 are diagrams illustrating an example in which a mobile device displays information related to cleaning of a robot vacuum cleaner according to an embodiment of the present disclosure.

[0022] FIG. 10 is a flowchart for explaining a method for predicting a cleaning time of a robot vacuum cleaner according to an embodiment of the present disclosure.

[0023] It should be understood that the various embodiments of the present disclosure and the terminology used therein are not intended to limit the technical features described in the present disclosure to specific embodiments, but rather to encompass various modifications, equivalents, or alternatives of the embodiments.

[0024] In connection with the description of the drawings, similar reference numerals may be used for similar or related components.

[0025] The singular form of a noun corresponding to an item may include one or more items, unless the context clearly indicates otherwise.

[0026] In this disclosure, phrases such as "A or B," "at least one of A and B," "at least one of A or B," "A, B, or C," "at least one of A, B, and C," and "at least one of A, B, or C" can each include any one of the items listed together in that phrase, or all possible combinations thereof. For example, "A or B," "at least one of A and B," or "at least one of A or B" can all refer to (1) including at least one A, (2) including at least one B, or (3) including both at least one A and at least one B.

[0027] Terms such as "first," "second," or "first" or "second" may be used simply to distinguish one component from another and do not qualify the components in any other respect (e.g., importance or order).

[0028] When a component (e.g., a first component) is referred to as being “coupled” or “connected” to another component (e.g., a second component), with or without the terms “functionally” or “communicatively,” it means that the component can be connected to the other component directly (e.g., wired), wirelessly, or through a third component.

[0029] The terms "include" or "have" are intended to specify the presence of a feature, number, step, operation, component, part or combination thereof described in this document, but do not preclude the presence or addition of one or more other features, numbers, steps, operations, components, parts or combinations thereof.

[0030] When a component is said to be “connected,” “coupled,” “supported,” or “in contact with” another component, this includes not only cases where the components are directly connected, coupled, supported, or in contact, but also cases where the components are indirectly connected, coupled, supported, or in contact through a third component.

[0031] When we say that a component is "on" another component, this includes not only cases where the component is in contact with the other component, but also cases where there is another component between the two components.

[0032] The term "and / or" includes any combination of a plurality of related described elements or any one of a plurality of related described elements.

[0033] In some contexts, the phrase "a device configured to" may mean that the device, in conjunction with other devices or components, is "capable of" performing A, B, and C. For example, the phrase "a processor configured (or set) to perform A, B, and C" may refer to a dedicated processor (e.g., an embedded processor) for performing those operations, or a general-purpose processor (e.g., a CPU or application processor) that can perform those operations by executing one or more software programs stored in a memory device.

[0034] In the embodiments, a "module" or "part" performs at least one function or operation and may be implemented as hardware or software, or as a combination of hardware and software. Furthermore, a plurality of "modules" or "parts" may be integrated into at least one module and implemented as at least one processor (not shown), excluding any "module" or "part" that needs to be implemented as specific hardware.

[0035] Meanwhile, the various elements and areas in the drawings are schematically drawn. Therefore, the technical concept of the present invention is not limited by the relative sizes or spacing depicted in the attached drawings.

[0036] An embodiment of the present disclosure will be described in more detail with reference to the attached drawings below.

[0037] FIG. 1 is a drawing for explaining the operation of a robot vacuum cleaner according to an embodiment of the present disclosure.

[0038] Referring to FIG. 1, the robot cleaner (100) can clean an indoor space where the robot cleaner (100) is located.

[0039] The cleaning operation may include the robot cleaner (100) moving around an indoor space, sucking up foreign substances such as dust on the floor, and cleaning the floor using a mop, etc. The indoor space may include various places where the robot cleaner (100) can move, such as a home, office, hotel, factory, store, supermarket, restaurant, etc.

[0040] The robot cleaner (100) can predict the time it takes for the robot cleaner (100) to clean an area within an indoor space (e.g., the expected cleaning time (11 in FIG. 1)). The robot cleaner (100) can obtain the expected cleaning time for an area using an artificial intelligence model. For example, the robot cleaner (100) can input cell information of a map for an area, environmental information for the area, and type information of obstacles within the area into the artificial intelligence model, and obtain the expected cleaning time for an area from the artificial intelligence model.

[0041] In addition, the robot cleaner (100) can compare the cleaning time that the robot cleaner (100) took to clean an area in the past with the expected cleaning time, and if the expected cleaning time is greater than the cleaning time, it can notify the user of this.

[0042] The user can organize clutter within the area being cleaned by the robot cleaner (100) or change the cleaning order within the area being cleaned by the robot cleaner (100). Accordingly, according to the present disclosure, the robot cleaner (100) can clean indoor spaces more efficiently.

[0043] Additionally, when predicting cleaning time, cell information of the map for the area, environmental information of the area, and type information of obstacles within the area can be used.

[0044] Cell information in a map for an area may include information about cells containing objects and cells without objects among multiple cells in which the map for the area is divided. Objects may include walls and obstacles in an indoor space. Obstacles may include various objects present in the indoor space, such as furniture, home appliances, people, and pets.

[0045] Additionally, the environmental information of the region may include 3D environmental information of the region. For example, the environmental information of the region may include information about the location where the robot cleaner (100) collided with an object within the region and information about the location of a cliff within the region.

[0046] In this way, according to the present disclosure, the expected cleaning time of the robot cleaner (100) for an area can be predicted based on various information that can affect the driving pattern of the robot cleaner (100).

[0047] For example, the driving pattern of the robot cleaner (100) may vary depending on the geometric shape of the plurality of cells in which objects exist in the area (e.g., the geometric shape represented by the lines connecting the plurality of cells), the type of object existing in the area, the location where the robot cleaner (100) collides with the object in the area, the location of a cliff in the area, etc. Depending on this driving pattern, the time taken for the robot cleaner (100) to move around the area for cleaning may vary. Therefore, as in the present disclosure, when the expected cleaning time is predicted using the cell information of the map for the area, the environmental information of the area, and the type information of the obstacle in the area, the expected cleaning time of the robot cleaner (100) for the area can be predicted more accurately.

[0048] FIG. 2A is a block diagram for explaining the configuration of a robot vacuum cleaner according to an embodiment of the present disclosure.

[0049] Referring to FIG. 2a, the robot cleaner (100) includes a sensor unit (110), a memory (120), and one or more processors (130).

[0050] The sensor unit (110) can detect structures or obstacles in an indoor space. Additionally, the information acquired from the sensor unit (110) can be used to create a map of the indoor space.

[0051] The sensor unit (110) may include a light detection and ranging (LiDAR) sensor (111), a bumper sensor (112), a cliff sensor (113), and a camera (114).

[0052] The lidar sensor (111) outputs a laser in a 360-degree direction, and when the laser reflected from an object is received, the difference in time taken for the laser to reflect from the object and return, the signal intensity of the received laser, etc. are analyzed to obtain geometry information for an indoor space. The geometry information may include the position, distance, direction, etc. of an object within the indoor space. The lidar sensor (111) may provide the obtained geometry information to one or more processors (130).

[0053] The bumper sensor (112) can detect an external impact. The bumper sensor (112) can be provided on at least one of the front, left, and right sides of the main body of the robot cleaner (100). The bumper sensor (112) can detect an impact amount generated when the main body collides with an external object (e.g., a threshold), and can detect a collision between the robot cleaner (100) and the object based on the detected impact amount. In addition, the bumper sensor (112) can provide information about the detected collision of the robot cleaner (100) to one or more processors (130).

[0054] The cliff sensor (113) can detect a cliff existing on the movement path of the robot cleaner (100). The cliff sensor (113) can include one or more light-emitting elements and one or more light-receiving elements. The cliff sensor (113) can measure the time it takes for light irradiated from the light-emitting elements to be received by the light-receiving element, thereby measuring the distance between the cliff sensor (113) and the floor, and can detect a cliff using the measured distance. For example, if there is a steep slope or a cliff that suddenly lowers on the movement path of the robot cleaner (100), the time it takes for the light to be received by the light-receiving element increases rapidly, or the light is not received by the light-receiving element. Accordingly, the cliff sensor (113) can detect a cliff using the amount of change in the received time. In addition, the cliff sensor (113) can provide information on the detected cliff to one or more processors (130).

[0055] The camera (114) (or camera module) can capture images by taking pictures of the surrounding area of ​​the robot cleaner (100). The camera can capture images of the front of the robot cleaner (100), the ceiling, the floor, etc. of an indoor space, and can obtain images that include various objects existing around the robot cleaner (100), such as the ceiling, walls, entrances, and obstacles.

[0056] For example, a robot cleaner (100) may include a front camera for capturing images of the front of the robot cleaner (100), an upper camera for capturing images of the ceiling, and a lower camera for capturing images of the floor. However, this is not limited to this example, and the number of cameras, their placement, and their shooting ranges may vary.

[0057] The camera (114) may include one or more optical lenses, an image sensor including a plurality of pixels that receive light passing through the optical lenses, an image signal processor that constructs an image based on a signal output from the image sensor, etc. One or more processors (130) may use the camera (114) to photograph an indoor space where the robot cleaner (100) is located, thereby obtaining an image of at least one object within the indoor space.

[0058] The memory (120) can store data necessary for the robot cleaner (100) to operate according to various embodiments of the present disclosure.

[0059] For example, one or more artificial intelligence models may be stored in the memory (120).

[0060] The artificial intelligence model may include a neural network model trained to predict the expected cleaning time of a robot cleaner (100) for a region based on information about the region. The information about the region may include cell information of a map for the region, environmental information about the region, and type information of obstacles within the region. The artificial intelligence model may receive information about the region and, based on the information about the region, output an estimated cleaning time expected to be required for the robot cleaner (100) to clean the region.

[0061] For example, the artificial intelligence model may include a neural network model composed of learned model parameters that apply information about each area acquired from each of multiple areas as input data and apply the time it takes for the robot cleaner (100) to actually clean each area as an output ground truth.

[0062] Additionally, the AI ​​model may include a neural network model trained to recognize object types from images. The AI ​​model may output a label value for the type of object inferred from the image input to the AI ​​model and a confidence value for the label value. The confidence value may include a probability value indicating that the type of object inferred from the image is a specific type.

[0063] For example, an artificial intelligence model may include a neural network model composed of model parameters learned by applying multiple images as input data and applying the label values ​​of objects included in the images as output correct values.

[0064] The neural network model according to the present disclosure refers to an artificial intelligence model including a neural network and can be trained by deep learning. The neural network may include, for example, at least one of a convolutional neural network (CNN), a recurrent neural network (RNN), a restricted boltzmann machine (RBM), a deep belief network (DBN), a bidirectional recurrent deep neural network (BRDNN), a generative adversarial network (GAN), and a deep Q-network. However, the neural network model is not limited to the examples described above.

[0065] As described above, the artificial intelligence model may be implemented in the form of an on-device included in the robot cleaner (100). However, the present invention is not limited thereto, and the artificial intelligence model may also be stored on a server connected to the robot cleaner (100). If the artificial intelligence model is stored on the server, the robot cleaner (100) may transmit information or images regarding an area to the server and receive information about the expected cleaning time or object type from the server.

[0066] One or more instructions may be stored in the memory (120). One or more processors (130) may execute one or more instructions stored in the memory (120) to perform operations of the robot cleaner (100) according to an embodiment of the present disclosure. Programs, applications, and data for driving the robot cleaner (100) may be stored in the memory (120).

[0067] One or more processors (130) control the overall operation of the robot cleaner (100). Specifically, one or more processors (130) may be connected to components of the robot cleaner (100) to control the overall operation of the robot cleaner (100). For example, one or more processors (130) may be connected to a sensor unit (110) and a memory (120) to control the robot cleaner (100). One or more processors (130) may be composed of one or more processors.

[0068] One or more processors (130) can perform operations of a robot cleaner (100) according to various embodiments of the present disclosure by executing one or more instructions stored in a memory (120).

[0069] The one or more processors (130) may include one or more of a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), an Accelerated Processing Unit (APU), a Many Integrated Core (MIC), a Digital Signal Processor (DSP), a Neural Processing Unit (NPU), a hardware accelerator, or a machine learning accelerator. The one or more processors (130) may control one or any combination of other components of the robot cleaner (100) and may perform operations or data processing related to communication. The one or more processors (130) may execute one or more programs or instructions stored in the memory (120). For example, the one or more processors (130) may perform a method according to an embodiment of the present disclosure by executing one or more instructions stored in the memory (120).

[0070] When a method according to an embodiment of the present disclosure includes multiple operations, the multiple operations may be performed by one processor or by multiple processors. For example, when a first operation, a second operation, and a third operation are performed by a method according to an embodiment, the first operation, the second operation, and the third operation may all be performed by the first processor, or the first operation and the second operation may be performed by the first processor (e.g., a general-purpose processor) and the third operation may be performed by the second processor (e.g., an artificial intelligence-specific processor).

[0071] One or more processors (130) may be implemented as a single core processor including one core, or may be implemented as one or more multicore processors including multiple cores (e.g., homogeneous multicores or heterogeneous multicores). When one or more processors (130) are implemented as a multicore processor, each of the multiple cores included in the multicore processor may include an internal processor memory, such as a cache memory or an on-chip memory, and a common cache shared by the multiple cores may be included in the multicore processor. In addition, each of the multiple cores (or some of the multiple cores) included in the multicore processor may independently read and execute a program instruction for implementing a method according to an embodiment of the present disclosure, or all (or some) of the multiple cores may be linked to read and execute a program instruction for implementing a method according to an embodiment of the present disclosure.

[0072] When a method according to an embodiment of the present disclosure includes a plurality of operations, the plurality of operations may be performed by one core among the plurality of cores included in a multi-core processor, or may be performed by the plurality of cores. For example, when a first operation, a second operation, and a third operation are performed by a method according to an embodiment, the first operation, the second operation, and the third operation may all be performed by a first core included in the multi-core processor, or the first operation and the second operation may be performed by a first core included in the multi-core processor, and the third operation may be performed by a second core included in the multi-core processor.

[0073] In embodiments of the present disclosure, a processor may mean a system on a chip (SoC) in which one or more processors and other electronic components are integrated, a single-core processor, a multi-core processor, or a core included in a single-core processor or a multi-core processor, wherein the core may be implemented as a CPU, a GPU, an APU, a MIC, a DSP, an NPU, a hardware accelerator, or a machine learning accelerator, but embodiments of the present disclosure are not limited thereto.

[0074] FIG. 2b is a block diagram illustrating the configuration of a robot vacuum cleaner according to an embodiment of the present disclosure.

[0075] Referring to FIG. 2b, the robot cleaner (100) may include a sensor unit (110), a memory (120), one or more processors (130), a driving unit (140), a cleaning device (150), a communication interface (160), an input interface (170), a display (180), and a speaker (190). However, such a configuration is exemplary, and it is obvious that new configurations may be added or some configurations may be omitted in addition to such configurations when implementing the present disclosure. Meanwhile, a detailed description of configurations that overlap with the configurations illustrated in FIG. 2a among the configurations illustrated in FIG. 2b will be omitted.

[0076] The sensor unit (110) may include an obstacle detection sensor for detecting obstacles present around the robot cleaner (100). For example, the obstacle detection sensor may include an ultrasonic sensor, an infrared sensor, an RF (radio frequency) sensor, etc. The obstacle detection sensor may detect obstacles present in front, behind, on the side, or along the movement path of the robot cleaner (100). One or more processors (130) may control the movement of the robot cleaner (100) using information about obstacles detected using the obstacle detection sensor.

[0077] The sensor unit (110) may include a driving detection sensor for detecting the driving of the robot cleaner (100). For example, the driving detection sensor may be

[0078] The sensor unit (110) may include a gyro sensor, a wheel encoder, an acceleration sensor, etc. The gyro sensor may detect the rotation direction and rotation angle of the robot cleaner (100). The wheel encoder may detect the rotation speed of one or more wheels of the robot cleaner (100). The acceleration sensor may detect a change in the speed of the robot cleaner (100). One or more processors (130) may control the driving of the robot cleaner (100) using information acquired through the sensor unit (110).

[0079] The driving unit (140) can move the robot cleaner (100). For example, the driving unit (140) can include at least one wheel, at least one motor for rotating the wheel, a brake for stopping the rotating wheel, etc. One or more processors (130) can control the driving unit (140) to perform various driving operations, such as moving, stopping, controlling speed, changing direction, and changing angular velocity of the robot cleaner (100).

[0080] The cleaning device (150) can suck up foreign substances from the floor. For example, the cleaning device (150) can include a brush assembly for sweeping dust from the floor and sucking up dust, a vacuum cleaning module for sucking up dust, and a mop cleaning module for performing mopping. The sucked up foreign substances can be accommodated in a dust bin provided in the robot cleaner (100). One or more processors (130) can control the cleaning device (150) to suck up foreign substances present on the floor while the robot cleaner (100) is stationary or moving. Accordingly, the robot cleaner (100) can perform cleaning of an indoor space.

[0081] The communication interface (160) can communicate with external devices via a network. External devices may include servers, home appliances, mobile devices (e.g., smartphones, tablet PCs, wearable devices, etc.). The communication interface (160) may include a wireless communication module or a wired communication module. The communication module may be implemented with at least one hardware chip.

[0082] A network may include a wide area network (WAN) such as the Internet, a local area network (LAN) formed around an access point (AP), and a short-range wireless network that does not use an access point (AP). A short-range wireless network is a Bluetooth TM , IEEE 802.15.1), Zigbee (IEEE 802.15.4), Wi-Fi Direct, Near Field Communication (NFC), Z-Wave, etc., but are not limited thereto.

[0083] According to an example, the communication interface (160) can communicate with an external device through an access point (AP). For example, the access point (AP) can connect a local area network (LAN) to which the robot cleaner (100) is connected to a wide area network (WAN) to which the server is connected. The robot cleaner (100) can be connected to the server through the wide area network (WAN). The access point (AP) can be Wi-Fi TM , IEEE 802.11), Bluetooth, Zigbee, etc., to communicate with the robot cleaner (100), and can connect to a wide area network (WAN) using wired communication. In addition, the communication interface (160) can communicate with other external devices through the server. For example, the communication interface (160) can communicate with home appliances, mobile devices, etc. through the server.

[0084] For example, the robot cleaner (100) may be directly connected to an external device without going through an access point (AP). For example, the communication interface (160) may communicate with the external device via a long-range wireless network or a short-range wireless network. The robot cleaner (100) may be connected to home appliances, mobile devices, etc. via a short-range wireless network (e.g., Wi-Fi Direct). In addition, the robot cleaner (100) may be connected to an external device via a wide area network (WAN) using a long-range wireless network (e.g., a cellular communication module).

[0085] The input interface (170) includes circuitry. The input interface (170) can receive user input and transmit the user input to one or more processors (130). For example, the input interface (170) can receive various user inputs for setting or selecting various functions supported by the robot cleaner (100).

[0086] The input interface (170) may include various types of input devices.

[0087] In one example, the input interface (170) may include a physical button. The physical button may include a function key or a dial button. The physical button may also be implemented as one or more keys.

[0088] In one example, the input interface (170) can receive user input using a touch method. For example, the input interface (170) can be implemented as a touch screen capable of performing the function of the display (180).

[0089] For example, the input interface (170) may receive a user's voice using a microphone. One or more processors (130) may perform a function corresponding to the user's voice using voice recognition. For example, one or more processors (130) may convert the user's voice into text data using a STT (Speech To Text) function, obtain control command data based on the text data, and perform a function corresponding to the user's voice based on the control command data. Depending on the embodiment, the STT function may be performed by an external server.

[0090] The display (180) can display various screens. One or more processors (130) can display various notifications, messages, information, etc. related to the operation of the robot cleaner (100) on the display (180).

[0091] The display (180) may be implemented as a display including a self-luminous element or a display including a non-luminous element and a backlight. For example, the display (180) may be implemented as various types of displays such as an LCD (Liquid Crystal Display), an OLED (Organic Light Emitting Diodes) display, an LED (Light Emitting Diodes) display, a micro LED display, a Mini LED display, a QLED (Quantum dot light-emitting diodes) display, etc.

[0092] The speaker (190) can output audio signals. One or more processors (130) can output warning sounds, notification messages, response messages corresponding to user input, etc. related to the operation of the robot (100) through the speaker (190).

[0093] For convenience of explanation, one or more processors (130) are referred to as processors (130) below.

[0094] The processor (130) can use information acquired using the sensor unit (110) to create a map of the indoor space where the robot cleaner (100) is located. The map can be created during the initial exploration process of the indoor space.

[0095] For example, the processor (130) can use a lidar sensor (111) to search an indoor space to obtain topographic information about the indoor space, and create a map of the indoor space using the topographic information.

[0096] The map may include a grid map. A grid map is a map that divides an indoor space into cells of a certain size and expresses them. For example, a grid map may be a map that divides an indoor space into a plurality of cells of a predetermined size and indicates the presence or absence of an object in each cell. The plurality of cells may be divided into cells without objects (e.g., cells in which a robot cleaner (100) can drive) (free space) and cells with objects (occupied space). A line connecting cells occupied by objects may represent a boundary of the space (e.g., a wall, an obstacle, etc.). Meanwhile, the processor (130) may also generate a map of the indoor space using information acquired using an obstacle detection sensor.

[0097] A map can contain multiple areas. Areas can be separated from other areas by walls or other obstacles (e.g., door frames, windows, fences, stairs, etc.). Areas can correspond to subspaces within an indoor space (e.g., rooms).

[0098] The processor (130) can identify the location of the robot cleaner (100) on the map using SLAM (Simultaneous Localization and Mapping) technology.

[0099] For example, the processor (130) can obtain topographic information of an indoor space using a lidar sensor (111), compare the obtained topographic information with pre-stored topographic information, or compare the obtained topographic information to identify the location of the robot cleaner (100) on the map.

[0100] In addition, the processor (130) may acquire an image using the camera (114) while the robot cleaner (100) is driving in an indoor space, map features for each location of the area acquired from the image to each location of the map, and store the mapped features in the memory (120). In addition, the processor (130) may acquire an image using the camera (114) while the robot cleaner (100) is driving in an indoor space, and compare the features for the area acquired from the image with the features stored in the memory (120) to identify the location of the robot cleaner (100) on the map.

[0101] The processor (130) can acquire information using the sensor unit (110) while the robot cleaner (100) is driving in an indoor space, and can detect obstacles around the robot cleaner (100) using the acquired information. Then, when the processor (130) identifies the presence of an obstacle, it can determine a driving pattern such as straight ahead or turning based on the properties of the obstacle, and control the driving unit (140) so that the robot cleaner (100) moves according to the determined driving pattern.

[0102] Accordingly, the robot cleaner (100) can move while avoiding obstacles in an indoor space and perform cleaning of the indoor space by sucking up foreign substances on the floor.

[0103] The robot cleaner (100) can sequentially clean multiple areas of a map. For example, the processor (130) can determine a cleaning order for multiple areas so that cleaning starts from an area closest to the charging station or the location where the robot cleaner (100) starts cleaning, and control the robot cleaner (100) to clean the multiple areas according to the cleaning order. In addition, the processor (130) can transmit the cleaning order of the robot cleaner (100) to the server via the communication interface (160). The server can store information about the cleaning order received from the robot cleaner (100) and display a UI screen including cleaning information of the robot cleaner (100) on the mobile device according to the cleaning order.

[0104] Meanwhile, the cleaning order can be determined or changed based on user input.

[0105] For example, a user can access a server by executing an application installed on the user's mobile device, create a user account, and communicate with the server based on the logged-in user account to register a robot cleaner (100). The server can register the robot cleaner (100) to the user account by registering identification information (e.g., serial number or MAC address) of the robot cleaner (100) to the user account.

[0106] In addition, the user can control the robot cleaner (100) using an application installed on the mobile device. For example, when the user logs into a user account using an application installed on the mobile device, a user interface (UI) screen related to the robot cleaner (100) registered in the user account can be displayed on the mobile device. The UI screen can receive user input for controlling the robot cleaner (100) and display various information related to the operation of the robot cleaner (100). When the user inputs a user input for setting a cleaning order of the robot cleaner (100) on the UI screen, a control command for causing the robot cleaner (100) to clean a plurality of areas according to the set cleaning order can be transmitted to the robot cleaner (100) through the server. The processor (130) can receive a control command from the server through the communication interface (160) and control the robot cleaner (100) to clean a plurality of areas according to the cleaning order based on the control command.

[0107] The processor (130) can obtain information about an area using the sensor unit (110) while the robot cleaner (100) is cleaning the area.

[0108] Information about an area may include cell information of a map for the area, environmental information of the area, and type information of obstacles within the area.

[0109] Cell information of a map for an area may include information about cells in which an object exists and cells in which no object exists among multiple cells of the map for the area. For example, the processor (130) may obtain topographic information about the area using the lidar sensor (111) while the robot cleaner (100) is cleaning the area. In addition, the processor (130) may obtain a grid map for the area using the topographic information, thereby obtaining cell information about the area. The grid map may include multiple cells, and the multiple cells may include one or more cells in which an object is detected and one or more cells in which no object is detected according to a search result by the lidar sensor (111).

[0110] The environmental information of a region may include information about a location where the robot cleaner (100) collided with an object within the region. For example, the processor (130) may detect a collision between the robot cleaner (100) and an object using a bumper sensor (112) while the robot cleaner (100) is cleaning the region, and identify the location where the robot cleaner (100) collided with the object on a map of the region.

[0111] Environmental information of an area may include information about the location of a cliff within the area. For example, the processor (130) may detect a cliff using a cliff sensor (113) while the robot cleaner (100) is cleaning the area, and identify the location of the cliff on a map of the area.

[0112] Additionally, the processor (130) can identify the type of obstacle within an area. For example, the processor (130) can capture an image of an area using a camera (114) while the robot cleaner (100) is cleaning the area. The processor (130) can input the acquired image into an artificial intelligence model to recognize the type of obstacle (e.g., wire, toilet pad, clothes, carpet, etc.) present within the area. For example, the processor (130) can obtain information on the type of object based on the label value and confidence value output by the artificial intelligence model. In addition, the processor (130) can obtain information on the type of obstacle within the area by labeling the type of object at the location of the object on a map for the area.

[0113] The processor (130) can store cleaning history information for an area in the memory (120).

[0114] Cleaning history information for an area may include information about the area and the time required to clean the area.

[0115] For example, the processor (130) can obtain the cleaning time for the area by measuring the time from when the robot cleaner (100) starts cleaning the area to when the cleaning is completed. In addition, when the robot cleaner (100) completes cleaning the area, the processor (130) can store, in the memory (120), information about the area obtained using the sensor unit (110) while the robot cleaner (100) was cleaning the area and information about the cleaning time taken by the robot cleaner (100) to clean the area.

[0116] The processor (130) can obtain cleaning history information each time the robot cleaner (100) cleans an area and store the obtained cleaning history information in the memory (120). The memory (120) can store cleaning history information for each area. In addition, the processor (130) can transmit the cleaning history information to a server via the communication interface (160). The server can store information about the cleaning history information received from the robot cleaner (100) and display the cleaning time on the mobile device.

[0117] For example, let's assume that there are four rooms in an indoor space. Referring to FIG. 3, the processor (130) can store cleaning history information (310, 320, 330, 340) of the robot cleaner (100) for the four rooms in the memory (120). The memory (120) can include cleaning history information (310) for room 1, cleaning history information (320) for room 2, cleaning history information (330) for room 3, and cleaning history information (340) for room 4.

[0118] Cleaning history information may include information about the area obtained using the sensor unit (110) when the robot cleaner (100) cleaned the area and information about the cleaning time taken to clean the area. For example, cleaning history information (320) for room 2 may include information about the area (x1, x2,..., x n ) and cleaning time (t1,t2,...,t n ) may be included. n is the number of times the robot vacuum cleaner (100) cleaned room 2.

[0119] Meanwhile, the processor (130) can obtain information about the area using the sensor unit (110) while the robot cleaner (100) cleans the area within the indoor space, input the obtained information into an artificial intelligence model, and obtain an expected cleaning time for the area from the artificial intelligence model.

[0120] Below, a method for predicting the expected cleaning time of a robot vacuum cleaner (100) will be described in more detail.

[0121] FIG. 4 is a flowchart illustrating a method for predicting an expected cleaning time of a robot vacuum cleaner according to an embodiment of the present disclosure.

[0122] The processor (130) can identify an area on which the robot cleaner (100) is to perform cleaning among multiple areas on the map (S410). For example, the processor (130) can identify an area on which the robot cleaner (100) is to perform cleaning among multiple areas on the map based on the cleaning order.

[0123] The processor (130) can obtain the most recent cleaning history information for an area identified as a cleaning target among multiple areas (S420). The processor (130) can obtain the most recent cleaning history information among the cleaning history information for the area stored in the memory (120).

[0124] For example, referring to FIG. 5, it is assumed that room 2 (510) is an area where the robot cleaner (100) performs cleaning. The processor (130) stores information (x) about an area that the robot cleaner (100) most recently acquired while cleaning room 2 among the cleaning history information (320) for room 2 stored in the memory (120). n )(520) and the cleaning time (t) taken by the robot vacuum cleaner (100) to clean room 2 most recently n )(520) can be obtained.

[0125] The processor (130) can control the robot cleaner (100) to clean an area identified as a cleaning target (S430). For example, the processor (130) can control the driving unit (140) to move within the area after the robot cleaner (100) moves to the area, and can control the cleaning device (150) to suck up foreign substances present on the floor.

[0126] The processor (130) can obtain first information about some area among the areas using the sensor unit (110) while the robot cleaner (100) is cleaning the area (S440).

[0127] For example, when the robot cleaner (100) moves within an area to clean the area, the processor (130) can search for a portion of the area within a range that can be detected by the sensor unit (110) based on the current location of the robot cleaner (100) among the entire area, and obtain information about the portion of the area based on the search result.

[0128] The first information for some area may include cell information of the map for some area, environment information of some area, and type information of obstacles within some area.

[0129] For example, as shown in FIG. 6, the processor (130) can obtain terrain information using the lidar sensor (111) while the robot cleaner (100) moves from the first location (611) to the second location (612) in room 2 (510). Then, the processor (130) can obtain a grid map for a portion of room 2 (510) using the terrain information, and obtain cell information (620) of the map for the portion of the area.

[0130] In addition, the processor (130) can detect whether the robot cleaner (100) collides with an object using the bumper sensor (112) while the robot cleaner (100) moves from a first location (611) to a second location (612) in room 2 (510). In addition, if the processor (130) identifies that a collision has occurred between the robot cleaner (100) and an object, the processor (130) can obtain information (630) about the location where the robot cleaner (100) collided with the object within a certain area of ​​room 2 (510).

[0131] Additionally, the processor (130) can detect whether a cliff exists using the cliff sensor (113) while the robot cleaner (100) moves from the first location (611) to the second location (612) in room 2 (510). Then, if the processor (130) identifies that a cliff has been detected, it can obtain information (640) about the location of the cliff within a certain area of ​​room 2 (510).

[0132] Additionally, the processor (130) can acquire an image using the camera (114) while the robot cleaner (100) moves from the first location (611) to the second location (612) in room 2 (510). Then, the processor (130) can recognize the type of obstacle from the image and acquire type information (650) of the obstacle within a certain area of ​​room 2 (510).

[0133] The processor (130) can identify second information for some areas among the information for areas included in the most recent cleaning history information (S450).

[0134] For example, the processor (130) can identify some areas searched by the sensor unit (110) among the areas. According to one example, the processor (130) can identify some areas searched by the sensors in the area for each sensor based on the movement path of the robot cleaner (100) within the area, the detection range of the sensor, etc. In addition, the processor (130) can obtain second information about some areas identified among the most recently obtained information about the areas. The second information about some areas can include cell information of the map for some areas, environmental information of some areas, and type information of obstacles within some areas.

[0135] The processor (130) can identify whether there is a difference between the first information and the second information for some area (S460).

[0136] For example, the processor (130) may identify that the first information and the second information are different when a new object that was not most recently detected by the lidar sensor (111) in some area is currently detected by the lidar sensor (111) in some area, or when an object that was most recently detected by the lidar sensor (111) in some area is currently not detected by the lidar sensor (111) in some area.

[0137] Additionally, the processor (130) may identify that the first information and the second information are different if a collision that was not most recently detected by the bumper sensor (112) in some area is now detected by the bumper sensor (112) in some area, or if a collision that was most recently detected by the bumper sensor (112) in some area is now not detected by the bumper sensor (112) in some area.

[0138] Additionally, the processor (130) can identify that there is a difference between the first information and the second information if a cliff that was not most recently detected by the cliff sensor (113) in some area is now detected by the cliff sensor (113) in some area, or if a cliff that was most recently detected by the cliff sensor (113) in some area is now not detected by the cliff sensor (113) in some area.

[0139] Additionally, the processor (130) may identify that the first information and the second information are different when an object that was not most recently recognized in the image by the camera (114) in some areas is now recognized by the camera (114) in some areas, or an object that was most recently recognized by the camera (114) in some areas is now not recognized by the camera (114) in some areas.

[0140] In this way, the processor (130) can identify that the first information and the second information are different when the information searched by at least one sensor while the robot cleaner (100) is cleaning an area is different from the most recently searched information.

[0141] If the processor (130) identifies that the first information is different from the second information (S460-Y), it can obtain third information about the remaining areas among the information about the areas included in the most recent cleaning history information (S470).

[0142] For example, the processor (130) can identify the remaining areas that have not been explored by the sensor unit (110) within the area. In one example, the processor (130) can identify the remaining areas that have not been explored by the sensors within the area for each sensor. In addition, the processor (130) can acquire third information about the identified remaining areas among the most recently acquired information about the areas. The third information about the remaining areas can include cell information of the map for the remaining areas, environmental information about the remaining areas, and type information of obstacles within the remaining areas.

[0143] The processor (130) can input the first information and the third information into an artificial intelligence model to obtain an expected cleaning time for the area (S480).

[0144] According to one example, the processor (130) can obtain information about some areas searched by the sensor unit (110) while the robot cleaner (100) is currently cleaning the area and information about the remaining areas searched by the sensor unit (110) when the robot cleaner (100) most recently cleaned the area as input data for the artificial intelligence model. Then, the processor (130) can input the input data into the artificial intelligence model to obtain an expected cleaning time of the robot cleaner (100) for the area.

[0145] For example, as shown in FIG. 7, the processor (130) can obtain cell information (620) of a map for a portion of an area acquired using a lidar sensor (111) while the robot cleaner (100) was cleaning room 2 and cell information (720) of a map for the remaining area of ​​room 2 most recently acquired using a lidar sensor (111) as input data for the artificial intelligence model (710).

[0146] In addition, the processor (130) can obtain information (630) on the collision location of the robot cleaner (100) within a certain area acquired using the bumper sensor (112) while the robot cleaner (100) is cleaning room 2, and information (730) on the collision location of the robot cleaner (100) within the remaining area acquired most recently using the bumper sensor (112), as input data for the artificial intelligence model (710).

[0147] In addition, the processor (130) can obtain information (640) on the location of a cliff within a certain area obtained using a cliff sensor (113) while the robot cleaner (100) is cleaning room 2 and information (740) on the location of a cliff within a remaining area most recently obtained using a bumper sensor (112) as input data for the artificial intelligence model (710).

[0148] In addition, the processor (130) can obtain type information (650) of obstacles in some areas acquired using the camera (114) while the robot cleaner (100) is cleaning room 2 and type information (750) of obstacles in the remaining areas acquired most recently using the camera (114) as input data for the artificial intelligence model (710).

[0149] And, the processor (130) can input input data into the artificial intelligence model (710) to obtain the expected cleaning time (760) of the robot cleaner (100) for room 2.

[0150] The processor (130) can transmit the estimated cleaning time for the area to the mobile device via the communication interface (160) (S490). For example, the processor (130) can transmit the estimated cleaning time to the server via the communication interface (160). When the server receives the estimated cleaning time from the robot cleaner (100), it can transmit the estimated cleaning time to the mobile device registered to the user account. The mobile device can display the estimated cleaning time.

[0151] For example, referring to FIG. 8, it is assumed that the cleaning order of the robot vacuum cleaner (100) is Room 1 -> Room 2 -> Room 3 -> Room 4.

[0152] The mobile device (200) can display a UI screen (810) containing cleaning information of the robot cleaner (100) for each room.

[0153] The cleaning information may include the cleaning time (821) of a room currently being cleaned by the robot cleaner (100), the expected cleaning time (822) of a room currently being cleaned by the robot cleaner (100), and the expected cleaning time (823, 824) of a room not yet being cleaned.

[0154] For example, the cleaning time (821) may be the time taken to clean a room when the robot cleaner (100) has completed cleaning the room. The expected cleaning time (822) may be the expected cleaning time of the room that the robot cleaner (100) is currently cleaning, and may be the expected cleaning time predicted by an artificial intelligence model. The expected cleaning times (823, 824) may be the time taken by the robot cleaner (100) to most recently clean the room.

[0155] Cleaning information may include a GUI (831, 832, 833, 834) indicating the cleaning progress. For example, the processor (130) may calculate the cleaning progress based on the number of cells moved by the robot cleaner (100) relative to the total number of cells in the grid map for the area, and transmit the calculated cleaning progress to the mobile device via the server.

[0156] A user can change the cleaning order of the robot cleaner (100) using the UI screen. For example, assume that the user still has work to do in Room 3 when the robot cleaner (100) starts cleaning Room 3 after completing cleaning Room 2. The user can input a user input to the UI screen (810) to change the cleaning order of Room 3 and Room 4 so that the robot cleaner (100) cleans Room 2 and then cleans Room 4. The server can transmit a control command corresponding to the user input to the robot cleaner (100). The processor (130) can change the cleaning order of the robot cleaner (100) based on the control command received from the server. Accordingly, the robot cleaner (100) can clean Room 4 after completing cleaning Room 2.

[0157] Additionally, the processor (130) may transmit a notification message to the mobile device via the communication interface (160) if the expected cleaning time for the area is greater than the cleaning time included in the most recent cleaning history information. For example, the processor (130) may transmit a control command to the mobile device via the server to display the notification message.

[0158] The notification message may include a notification message requesting that obstacles within the area be cleared.

[0159] For example, as shown in FIG. 9, the mobile device (200) may display a notification message (910) such as, "An object that increases the cleaning time of Room 2 has been detected. Cleaning can be completed more quickly when the object is removed." The user may clean up any clutter in Room 2, where the robot cleaner (100) is currently cleaning, according to the notification message. In addition, if the user anticipates that cleaning Room 2 will take a long time, the user may change the cleaning order of Room 2 to that of other rooms. Accordingly, according to the present disclosure, the robot cleaner (100) can clean an indoor space more efficiently.

[0160] Meanwhile, when cleaning of an area is completed, the processor (130) may store information about the area obtained using the sensor unit (110) while the robot cleaner (100) is cleaning the area and the cleaning time required for the area in the memory (120). In addition, when cleaning of an area is completed, the processor (130) may train an artificial intelligence model based on information about the area obtained using the sensor unit (110) while the robot cleaner (100) is cleaning the area and the cleaning time required for the area.

[0161] In the above example, the processor (130) measures the time from when the robot cleaner (100) starts cleaning room 2 to when it completes cleaning, and calculates the cleaning time (t) for room 2. n+1 ) can be obtained. And, when the robot cleaner (100) completes cleaning of room 2, the processor (130) acquires information (x) about room 2 using the sensor unit (110) while the robot cleaner (100) is cleaning room 2. n+1 ) and cleaning time for room 2 (t n+1 ) can be stored in the memory (120) to update the cleaning history information for room 2. In addition, the processor (130) can store information (x) for room 2 n+1) is applied as input data for the artificial intelligence model and the cleaning time for room 2 (t n+1 ) can be applied as the output answer value of the artificial intelligence model to train the artificial intelligence model.

[0162] In addition, when cleaning of an area is completed, the processor (130) can transmit information about the area acquired using the sensor unit (110) while the robot cleaner (100) is cleaning the area and the cleaning time for the area to the server via the communication interface (160). The server can use the information about the area and the cleaning time received from the robot cleaner (100) to train an artificial intelligence model stored in the server. The artificial intelligence model trained in this way can be used to update the artificial intelligence model stored in the robot cleaner (100).

[0163] FIG. 10 is a flowchart for explaining a method for predicting a cleaning time of a robot vacuum cleaner according to an embodiment of the present disclosure.

[0164] The robot vacuum cleaner includes a memory storing an artificial intelligence model that outputs an estimated cleaning time of the robot vacuum cleaner for an area based on information about the area.

[0165] While a robot cleaner is cleaning an area within an indoor space, information about the area is acquired using the robot cleaner's sensor unit (S1010). The information about the area includes cell information of a map for the area, environmental information about the area, and information about the types of obstacles within the area. The cell information about the area may include information about cells in which objects exist and cells in which objects do not exist among multiple cells of the map for the area. In addition, the environmental information about the area may include information about a location where the robot cleaner collided with an object within the area and information about the location of a cliff within the area.

[0166] Then, the acquired information is input into the artificial intelligence model, and the expected cleaning time for the area is obtained from the artificial intelligence model (S1020).

[0167] Meanwhile, the memory can store cleaning history information for an area. The cleaning history information can include information about the area acquired using a sensor unit when the robot vacuum cleaner cleaned the area, as well as information about the cleaning time required to clean the area.

[0168] In addition, step S1010 may obtain the most recent cleaning history information among the cleaning history information for the area stored in the memory, obtain first information about some areas among the areas using a sensor unit while the robot cleaner cleans the area, identify second information about some areas among the information about the areas included in the most recent cleaning history information, and if the first information is identified as being different from the second information, obtain third information about the remaining areas among the information about the areas included in the most recent cleaning history information. Step S1020 may input the first information and the third information into an artificial intelligence model to obtain an expected cleaning time for the area.

[0169] Additionally, the cleaning time prediction method according to the embodiment of the present disclosure can transmit the obtained predicted cleaning time to a mobile device.

[0170] Additionally, the cleaning time prediction method according to the embodiment of the present disclosure can transmit a notification message to the mobile device to request removal of obstacles within the area when the acquired estimated cleaning time is greater than the cleaning time included in the most recent cleaning history information.

[0171] In addition, the cleaning time prediction method according to the embodiment of the present disclosure can train an artificial intelligence model based on information about the area acquired using a sensor unit while the robot cleaner is cleaning the area and the cleaning time required for the area when cleaning of the area is completed.

[0172] In addition, the cleaning time prediction method according to the embodiment of the present disclosure can store, in memory, information about the area acquired using a sensor unit while the robot cleaner is cleaning the area and the cleaning time required for the area when cleaning of the area is completed.

[0173] Meanwhile, according to a temporary example of the present disclosure, the various embodiments described above can be implemented as software including instructions stored in a machine-readable storage medium that can be read by a machine (e.g., a computer). The device is a device that can call instructions stored from the storage medium and operate according to the called instructions, and may include an electronic device according to the disclosed embodiments. When an instruction is executed by a processor, the processor can perform a function corresponding to the instruction directly or by using other components under the control of the processor. The instruction may include code generated or executed by a compiler or interpreter. The machine-readable storage medium may be provided in the form of a non-transitory storage medium. Here, 'non-transitory' means that the storage medium does not contain a signal and is tangible, but does not distinguish between data being stored semi-permanently or temporarily in the storage medium.

[0174] Furthermore, according to one embodiment of the present disclosure, the method according to the various embodiments described above may be provided as included in a computer program product. The computer program product may be traded as a product between a seller and a buyer. The computer program product may be distributed in the form of a machine-readable storage medium (e.g., compact disc read-only memory (CD-ROM)) or online through an application store (e.g., Play Store™). In the case of online distribution, at least a portion of the computer program product may be temporarily stored or temporarily generated in a storage medium, such as the memory of a manufacturer's server, an application store's server, or a relay server.

[0175] Furthermore, according to one embodiment of the present disclosure, the various embodiments described above may be implemented in a computer-readable recording medium or a similar device using software, hardware, or a combination thereof. In some cases, the embodiments described herein may be implemented by the processor itself. In a software implementation, embodiments such as the procedures and functions described herein may be implemented as separate software. Each software may perform one or more functions and operations described herein.

[0176] Meanwhile, computer instructions for performing processing operations of a device according to the various embodiments described above may be stored in a non-transitory computer-readable medium. The computer instructions stored in such a non-transitory computer-readable medium, when executed by a processor of a specific device, cause the specific device to perform processing operations in the device according to the various embodiments described above. A non-transitory computer-readable medium refers to a medium that permanently stores data and can be read by a device, rather than a medium that stores data for a short period of time, such as a register, cache, or memory. Specific examples of non-transitory computer-readable media may include a CD, DVD, hard disk, Blu-ray disk, USB, memory card, or ROM.

[0177] In addition, each of the components (e.g., modules or programs) according to the various embodiments described above may be composed of a single or multiple entities, and some of the corresponding sub-components described above may be omitted, or other sub-components may be further included in various embodiments. Alternatively or additionally, some components (e.g., modules or programs) may be integrated into a single entity, which may perform the same or similar functions as those performed by each of the corresponding components prior to integration. Operations performed by modules, programs or other components according to various embodiments may be executed sequentially, in parallel, iteratively or heuristically, or at least some operations may be executed in a different order, omitted, or other operations may be added.

[0178] Although the preferred embodiments of the present disclosure have been illustrated and described above, the present disclosure is not limited to the specific embodiments described above, and various modifications may be made by a person having ordinary skill in the art to which the present disclosure pertains without departing from the gist of the present disclosure as claimed in the claims, and such modifications should not be understood individually from the technical idea or prospect of the present disclosure.

Claims

1. In robot vacuum cleaners, Sensor section; A memory storing an artificial intelligence model that outputs an expected cleaning time of the robot cleaner for the area based on information about the area; and While the robot cleaner is cleaning an area within an indoor space, information about the area is obtained using the sensor unit, Including one or more processors that input the acquired information into the artificial intelligence model and obtain an expected cleaning time for the area from the artificial intelligence model; Information about the above areas: A robot vacuum cleaner including cell information of a map for the above area, environmental information of the above area, and type information of obstacles within the above area.

2. In paragraph 1, Cell information for the above area is: Contains information about cells in which objects exist and cells in which objects do not exist among multiple cells of the map for the above area, Environmental information in the above area is: A robot cleaner comprising information about a location where the robot cleaner collided with an object within the area and information about a location of a cliff within the area.

3. In paragraph 1, The above memory stores cleaning history information for the above area, The above cleaning history information is: A robot cleaner comprising information about the area obtained using the sensor unit when the robot cleaner cleans the area and information about the cleaning time taken to clean the area.

4. In paragraph 3, One or more of the above processors, Obtain the most recent cleaning history information among the cleaning history information for the area stored in the above memory, While the robot cleaner is cleaning the area, the sensor unit is used to obtain first information about a portion of the area, Identifying second information for said part of the area among the information for said area included in said most recent cleaning history information, If the first information is identified as being different from the second information, third information for the remaining areas among the information for the areas included in the most recent cleaning history information is obtained, A robot vacuum cleaner that inputs the first information and the third information into the artificial intelligence model to obtain an expected cleaning time for the area.

5. In paragraph 4, further comprising a communication interface; One or more of the above processors, A robot vacuum cleaner that transmits the obtained expected cleaning time to a mobile device through the communication interface.

6. In paragraph 4, further comprising a communication interface; One or more of the above processors, A robot cleaner that transmits a notification message to a mobile device via the communication interface to request removal of obstacles within the area when the estimated cleaning time obtained above is greater than the cleaning time included in the most recent cleaning history information.

7. In paragraph 1, One or more of the above processors, A robot cleaner that trains the artificial intelligence model based on information about the area acquired using the sensor unit and the cleaning time required for the area while the robot cleaner is cleaning the area, when cleaning of the area is completed.

8. In paragraph 1, One or more of the above processors, A robot cleaner which, when cleaning of the above area is completed, stores information about the area obtained using the sensor unit while the robot cleaner is cleaning the area and the cleaning time for the area in the memory.

9. A method for predicting a cleaning time of a robot cleaner, comprising a memory storing an artificial intelligence model that outputs an expected cleaning time of the robot cleaner for the area based on information about the area, A step of obtaining information about an area using a sensor unit of the robot cleaner while the robot cleaner is cleaning an area within an indoor space; and A step of inputting the acquired information into the artificial intelligence model and obtaining an expected cleaning time for the area from the artificial intelligence model; including, Information about the above areas: A cleaning time prediction method including cell information of a map for the above area, environmental information of the above area, and type information of obstacles within the above area.

10. In paragraph 9, Cell information for the above area is: Contains information about cells in which objects exist and cells in which objects do not exist among multiple cells of the map for the above area, Environmental information in the above area is: A cleaning time prediction method including information about a location where the robot cleaner collided with an object within the area and information about a location of a cliff within the area.

11. In paragraph 9, The above memory stores cleaning history information for the above area, The above cleaning history information is: A cleaning time prediction method including information about the area obtained using the sensor unit when the robot cleaner cleans the area and information about the cleaning time taken to clean the area.

12. In paragraph 11, The steps for obtaining information about the above areas are: A step of acquiring the most recent cleaning history information among the cleaning history information for the area stored in the above memory; A step of obtaining first information about a part of the area using the sensor unit while the robot cleaner cleans the area; A step of identifying second information for said part of the area among the information for said area included in said most recent cleaning history information; and If the first information is identified as being different from the second information, a step of obtaining third information for the remaining areas among the information for the areas included in the most recent cleaning history information is included; The step of obtaining the above expected cleaning time is: A cleaning time prediction method, comprising: a step of inputting the first information and the third information into the artificial intelligence model to obtain an expected cleaning time for the area; 13. In paragraph 12, A cleaning time prediction method further comprising the step of transmitting the obtained expected cleaning time to a mobile device.

14. In paragraph 12, A cleaning time prediction method further comprising the step of transmitting a notification message to a mobile device to request removal of obstacles within the area if the obtained expected cleaning time is greater than the cleaning time included in the most recent cleaning history information.

15. In paragraph 9, A cleaning time prediction method further comprising: a step of training the artificial intelligence model based on information about the area acquired using the sensor unit and the cleaning time required for the area while the robot cleaner is cleaning the area, when cleaning of the area is completed;

Images