Robot cleaner comprising light-emitting unit and control method thereof

Abstract

Provided are a robot cleaner and a control method thereof. The robot cleaner comprises: a camera; a suction unit; a driving unit; and at least one processor which acquires at least one image through the camera while the robot cleaner travels along a first traveling path, identifies the amount of dust in one or more areas within an angle of view of the camera on the basis of the at least one image, controls, on the basis of the identified amount of dust, the suction unit to perform a cleaning operation on a cleaning target area from among the one or more areas, and acquires, on the basis of the identified amount of dust, first map data indicating the amount of dust in a first area in which the robot cleaner has traveled in the areas within the angle of view of the camera, and second map data indicating the amount of dust in a second area in which the robot cleaner is to travel in the areas within the angle of view of the camera.

Description

Robot vacuum cleaner including a light-emitting unit and a control method thereof

[0001] The present disclosure relates to a robot vacuum cleaner including a light-emitting unit and a control method thereof. More specifically, the present disclosure relates to a robot vacuum cleaner that outputs light toward a floor surface located in front of the robot vacuum cleaner through the light-emitting unit to identify the amount of dust present on the floor surface, and a control method thereof.

[0002] Recent advancements in electronic technology have led to the widespread use of robots in a variety of industries. Object recognition technology has enabled robots to accurately distinguish between various objects, while autonomous driving technology has enabled robots to navigate safely without interfering with human traffic.

[0003] In particular, robots, once used solely in industrial settings, are now being used in many homes for household chores. Among these domestic assistance robots, robot vacuum cleaners account for a significant portion. These robot vacuum cleaners autonomously navigate the home and perform cleaning functions. However, because these robot vacuum cleaners rely solely on preset paths, they clean all areas along their path. Consequently, the robots are forced to navigate and clean areas that do not require cleaning (i.e., areas free of dust or clean). This leads to long cleaning times and unnecessary waste of power.

[0004] A robot cleaner according to one or more embodiments of the present disclosure includes a camera, a suction unit for sucking dust from a surface on which the robot cleaner is placed, a driving unit for moving the robot cleaner, at least one memory for storing one or more commands, and one or more processors. The one or more processors, by executing the one or more commands, acquire at least one image through the camera while the robot cleaner moves along the first driving path, identify the amount of dust in one or more areas within the field of view of the camera based on the at least one image, and control the suction unit to perform a cleaning operation on an area to be cleaned among the one or more areas based on the identified amount of dust, acquire first map data indicating the amount of dust in a first area along which the robot cleaner has driven among the one or more areas within the field of view of the camera based on the identified amount of dust, the first area corresponding to a portion of the first driving path along which the robot cleaner has previously driven, acquire second map data indicating the amount of dust in a second area along which the robot cleaner will drive among the one or more areas within the field of view of the camera, the second area corresponding to a portion of the first driving path along which the robot cleaner has not driven, and set a second driving path for the area to be cleaned when the area to be cleaned is not located on the first driving path, and control the driving unit so that the robot cleaner drives along the set second driving path.

[0005] The one or more processors, by executing the one or more commands, identify an area within the field of view of the camera, where the amount of dust is greater than a preset value, as the area to be cleaned based on the identified amount of dust.

[0006] The one or more processors, by executing the one or more commands, acquire the second map data before performing a cleaning operation on the cleaning target area, and when the cleaning operation on the cleaning target area along the second driving path is completed, control the driving unit so that the robot cleaner returns to the first driving path and then drives along the first driving path, and the first driving path includes information for re-identifying the amount of dust in at least one area among one or more areas where the robot cleaner has previously completed a cleaning operation.

[0007] A robot cleaner according to one or more embodiments of the present disclosure further includes a communication interface, wherein the one or more processors, by executing the one or more commands, identify a dust reduction rate for the cleaning target area based on the amount of dust in the cleaning target area included in the first and second map data, and transmit the identified dust reduction rate of the cleaning target area and the first map data to a user terminal device through the communication interface.

[0008] The one or more processors, when the driving of the robot along the first driving path is completed by executing the one or more commands, identify at least one re-cleaning target area among the one or more cleaning target areas based on the first map data, the re-cleaning target area is an area previously cleaned by the robot cleaner, set a third driving path for the identified at least one re-cleaning target area, and control the driving unit so that the robot cleaner drives to the at least one re-cleaning target area according to the set third driving path.

[0009] The one or more processors control the suction unit to suck up dust within the cleaning target area with a suction strength corresponding to the amount of dust in the cleaning target area by executing the one or more commands.

[0010] The one or more processors, by executing the one or more commands, operate the suction unit while the robot cleaner is driving, and de-operate the suction unit when the robot cleaner is located in the one or more cleaning target areas.

[0011] A robot cleaner according to one or more embodiments of the present disclosure further includes a light emitting unit, and the one or more processors, by executing the one or more commands, output light toward a floor surface located in front of the robot through the light emitting unit while the robot cleaner drives along the first driving path, and acquire at least one image of the floor surface to which the output light is irradiated through the camera.

[0012] The one or more processors, by executing the one or more commands, increase the intensity of light output through the light emitting unit from the first intensity to the second intensity when it is identified that the cleaning target area for which the cleaning operation has been completed is included within the field of view of the camera.

[0013] A method for controlling a robot cleaner according to an embodiment of the present disclosure includes the steps of: acquiring at least one image through a camera while the robot cleaner is driving along a first driving path; identifying an amount of dust in one or more areas within a field of view of the camera based on the at least one image; controlling a suction unit to perform a cleaning operation on an area to be cleaned among the one or more areas based on the identified amount of dust; acquiring first map data representing an amount of dust in a first area along which the robot cleaner has driven among the one or more areas within the field of view of the camera based on the identified amount of dust; and acquiring second map data representing an amount of dust in a second area along which the robot cleaner will drive among the one or more areas within the field of view of the camera, wherein the first area corresponds to a portion of the first driving path along which the robot cleaner has previously driven, and the second area corresponds to a portion of the first driving path along which the robot cleaner has not driven; and the step of controlling the suction unit includes the steps of: identifying an area to be cleaned among the areas within the field of view of the camera based on the identified amount of dust; and, if the area to be cleaned is not located on the driving path, controlling the suction unit to perform a cleaning operation on the area to be cleaned among the areas within the field of view of the camera. It includes a step of setting a second driving path and a step of controlling a driving unit so that the robot cleaner runs along the set second driving path.

[0014] Here, the step of controlling the suction unit further includes a step of identifying an area within the field of view of the camera, in which the amount of dust is greater than a preset value, as the cleaning target area based on the identified amount of dust.

[0015] In addition, when the cleaning operation for the cleaning target area along the second driving path is completed, the robot cleaner further includes a step of controlling the driving unit so that the robot cleaner returns to the first driving path and then drives along the first driving path. And, the obtaining step includes a step of obtaining the second map data based on the amount of dust in the cleaning target area identified before performing the cleaning operation for the cleaning target area, and a step of obtaining the first map data based on the re-identified amount of dust when the amount of dust in the cleaning target area is re-identified based on at least one image obtained through the camera while the robot cleaner returns to the first driving path and drives along the first driving path.

[0016] Here, the control method includes a step of identifying a dust reduction rate for the cleaning target area based on the amount of dust in the cleaning target area included in the first and second map data, a step of displaying the identified dust reduction rate on the cleaning target area in the first map data, and a step of transmitting the first map data displaying the identified dust reduction rate to a user terminal device through a communication interface.

[0017] Here, the control method includes a step of identifying at least one re-cleaning target area among a plurality of cleaning target areas based on the identified amount of dust when the driving of the robot along the first driving path is completed, a step of setting a third driving path for the identified at least one re-cleaning target area, and a step of controlling the driving unit so that the robot cleaner drives to the at least one re-cleaning target area along the set third driving path.

[0018] A non-transitory computer-readable recording medium storing computer instructions that, when executed by a processor of a robot cleaner according to one embodiment of the present disclosure, cause the robot cleaner to perform an operation, the operation includes a step of acquiring at least one image through a camera while the robot cleaner is traveling along a first traveling path. In addition, the control method includes a step of identifying an amount of dust in an area within the field of view of the camera based on the acquired image. In addition, the control method includes a step of controlling a suction unit to perform a cleaning operation on an identified cleaning target area based on the identified amount of dust. In addition, the control method includes a step of acquiring, based on the identified amount of dust, first map data representing an amount of dust in an area within the field of view of the camera along which the robot cleaner has traveled, and second map data representing an amount of dust in an area within the field of view of the camera on which the robot cleaner is to travel. In addition, the step of controlling the suction unit includes a step of setting a second driving path for the cleaning target area when the cleaning target area is not located on the driving path, and a step of controlling the driving unit so that the robot cleaner deviates from the first driving path and drives to the cleaning target area along the set second driving path.

[0019] Aspects, features and advantages of specific embodiments of the present disclosure will become more apparent from the following description taken in conjunction with the accompanying drawings.

[0020] FIG. 1 is an exemplary diagram of a robot vacuum cleaner according to an embodiment of the present disclosure.

[0021] FIG. 2 is a block diagram showing the configuration of a robot vacuum cleaner according to an embodiment of the present disclosure.

[0022] FIG. 3 is a flowchart schematically illustrating a method for controlling a robot vacuum cleaner according to an embodiment of the present disclosure.

[0023] FIG. 4 is an exemplary diagram showing a driving path set according to a mode of a robot vacuum cleaner according to an embodiment of the present disclosure.

[0024] FIGS. 5A and 5B are exemplary diagrams illustrating a method for generating first map data and second map data acquired by a robot cleaner according to an embodiment of the present disclosure.

[0025] FIG. 6 is a flowchart schematically illustrating a method for controlling a robot cleaner to perform a cleaning operation on a cleaning target area not located on a driving path according to one embodiment of the present disclosure.

[0026] FIG. 7 is an exemplary diagram schematically illustrating a method for controlling a robot cleaner to perform a cleaning operation on a cleaning target area not located on a driving path according to one embodiment of the present disclosure.

[0027] FIG. 8 is an exemplary diagram illustrating setting a third driving path for an identified re-cleaning target area based on first map data according to one embodiment of the present disclosure.

[0028] FIG. 9 is an exemplary diagram showing first map data and second map data including dust information of a cleaning target area acquired by a robot cleaner according to an embodiment of the present disclosure displayed on a user terminal.

[0029] FIG. 10 is a detailed block diagram of a robot vacuum cleaner according to an embodiment of the present disclosure.

[0030] The present embodiments may be modified and have various embodiments. Therefore, specific embodiments are illustrated in the drawings and described in detail in the detailed description. However, this is not intended to limit the scope to specific embodiments, but should be understood to encompass various modifications, equivalents, and / or alternatives of the embodiments of the present disclosure. In connection with the description of the drawings, similar reference numerals may be used for similar components.

[0031] In describing the present disclosure, if it is determined that a specific description of a related known function or configuration may unnecessarily obscure the gist of the present disclosure, a detailed description thereof will be omitted.

[0032] Additionally, the following embodiments may be modified in various other forms, and the scope of the technical concepts of the present disclosure is not limited to the following embodiments. Rather, these embodiments are provided to further faithfully and completely convey the technical concepts of the present disclosure to those skilled in the art.

[0033] The terminology used in this disclosure is for the purpose of describing specific embodiments only and is not intended to limit the scope of the rights. Singular expressions include plural expressions unless the context clearly dictates otherwise.

[0034] In this disclosure, expressions such as “has,” “can have,” “includes,” or “may include” indicate the presence of a corresponding feature (e.g., a component such as a number, function, operation, or part), and do not exclude the presence of additional features.

[0035] In this disclosure, expressions such as “A or B,” “at least one of A and / or B,” or “one or more of A or / and B” can include all possible combinations of the listed items. 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.

[0036] The expressions “first,” “second,” “first,” or “second,” etc., used in this disclosure can describe various components, regardless of order and / or importance, and are only used to distinguish one component from another, but do not limit the components.

[0037] When it is said that a component (e.g., a first component) is “(operatively or communicatively) coupled with / to” or “connected to” another component (e.g., a second component), it should be understood that said component may be directly coupled to said other component, or may be coupled via another component (e.g., a third component).

[0038] On the other hand, when it is said that a component (e.g., a first component) is "directly connected" or "directly connected" to another component (e.g., a second component), it can be understood that no other component (e.g., a third component) exists between said component and said other component.

[0039] The expression "configured to" as used in the present disclosure may be used interchangeably with, for example, "suitable for," "having the capacity to," "designed to," "adapted to," "made to," or "capable of." The term "configured to" may not necessarily mean only "specifically designed to" in terms of hardware.

[0040] Instead, 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.

[0041] 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, except for a 'module' or 'part' that needs to be implemented as a specific hardware.

[0042] 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.

[0043] Hereinafter, with reference to the attached drawings, embodiments according to the present disclosure will be described in detail so that a person having ordinary knowledge in the technical field to which the present disclosure pertains can easily implement the present disclosure.

[0044] FIG. 1 is an exemplary diagram of a robot vacuum cleaner according to an embodiment of the present disclosure.

[0045] Referring to FIG. 1, a robot cleaner (100) according to an embodiment of the present disclosure outputs light toward a floor surface located in front of the robot cleaner (100). Then, the robot cleaner (100) acquires an image of the floor surface to which the light is irradiated and identifies the amount of dust present on the floor surface based on the acquired image. That is, unlike existing cleaners (or robot cleaners) that identify the amount of dust during the process of sucking up dust, the robot cleaner (100) according to an embodiment of the present disclosure can identify the amount of dust present on the floor surface before sucking up dust. Through this, the robot cleaner (100) according to an embodiment of the present disclosure can perform a cleaning operation by selecting only a floor surface on which dust exists.

[0046] Specifically, existing robot vacuum cleaners simply drive along a set path within the space they are located in, cleaning all surfaces along their path. Consequently, existing robot vacuum cleaners require significant time to complete their journey and waste unnecessary power by cleaning even areas devoid of dust.

[0047] However, unlike conventional robot cleaners, the robot cleaner (100) according to one embodiment of the present disclosure can identify dusty floor surfaces in advance and selectively perform cleaning operations only on areas identified as dusty. Therefore, the cleaning operation for the driving space can be completed quickly, and in particular, unnecessary power waste can be prevented.

[0048] Hereinafter, an embodiment of the present disclosure related thereto will be described with reference to FIGS. 2 to 10.

[0049] FIG. 2 is a block diagram showing the configuration of a robot vacuum cleaner according to an embodiment of the present disclosure.

[0050] Referring to FIG. 2, the robot cleaner (100) includes a camera (110), a suction unit (120), a driving unit (130), and one or more processors (140).

[0051] The camera (110) captures images of objects around the robot cleaner (100) to obtain multiple images of the objects. Specifically, the camera (110) may also obtain images of objects (e.g., people, animals, other objects, etc.) existing around the robot cleaner (100).

[0052] In particular, according to one embodiment of the present disclosure, the camera (110) can capture a floor surface in front of the robot cleaner (100) to obtain multiple images of the floor surface. To this end, the camera (110) can be positioned on the front of the robot cleaner (100) to capture a floor surface in front of the robot.

[0053] The camera (110) may be implemented with an imaging device such as a CMOS image sensor (CIS) having a CMOS structure, a charge coupled device (CCD) having a CCD structure, etc. However, the present invention is not limited thereto, and the camera (110) may be implemented with a camera (110) module of various resolutions capable of photographing a subject. In addition, the camera (110) may be implemented with a depth camera, a stereo camera, an RGB camera, etc. Through this, the camera (110) may obtain depth information of the object together with an image of the object.

[0054] The suction unit (120) is configured to suck up dust and may include a brush, a motor, a fan (or a drum), etc. At this time, the brush of the suction unit (120) may be made of a material with a low coefficient of friction and good wear resistance, such as natural hair or PA (Polyamide: nylon).

[0055] In addition, the suction unit (120) can drive a motor to rotate a fan (or drum) connected to the motor, and as the fan rotates, dust on a surface to be cleaned (e.g., a floor surface) swept by a brush can be sucked into the inside of the cleaner (100) (e.g., the inside of the main body).

[0056] The driving unit (130) is a component for moving the robot cleaner (100). The driving unit (130) may be implemented with wheels, etc. For this purpose, the driving unit (130) may include a motor. Meanwhile, the processor (140) may control the driving unit (130) to control various driving operations of the robot cleaner (100), such as movement, stopping, speed control, and direction change.

[0057] At this time, the driving unit (130) can adjust the driving direction and driving speed according to the control of the processor (140). To this end, the driving unit (130) may include a power generation device (e.g., a gasoline engine, a diesel engine, an LPG (liquefied petroleum gas) engine, an electric motor, etc., depending on the fuel (or energy source) used) that generates power for driving the robot cleaner (100), and a steering device (e.g., manual steering, hydraulics steering, electronic control power steering (EPS), etc.) for adjusting the driving direction.

[0058] One or more processors (140) are electrically connected to the camera (110), suction unit (120), and driving unit (130) to control the overall operation and function of the robot cleaner (100).

[0059] The one or more processors (140) 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 (140) 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 (140) may execute one or more programs or instructions stored in a memory (not shown). For example, the one or more processors (140) may perform a method according to an embodiment of the present disclosure by executing one or more instructions stored in the memory.

[0060] 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 processor or by a plurality of 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-dedicated processor).

[0061] One or more processors (140) 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 (140) are implemented as a multicore processor, each of the multiple cores included in the multicore processor may include internal memory of the processor (140), 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.

[0062] 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.

[0063] In an embodiment of the present disclosure, the processor (140) 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 the embodiments of the present disclosure are not limited thereto.

[0064] Hereinafter, for convenience of explanation, one or more processors (140) will be referred to as processors (140).

[0065] Meanwhile, according to one embodiment of the present disclosure, the robot cleaner (100) may further include a light-emitting unit. Here, the light-emitting unit may be configured to output light toward a floor surface located in front of the robot cleaner (100). To this end, the light-emitting unit may include at least one light-emitting element. For example, the light-emitting element may be an LED (Light Emitting Diode), a micro LED, or the like. In particular, the light-emitting element may be included in an LED array including a plurality of LEDs.

[0066] Meanwhile, the light emitting unit may be positioned lower than the camera (110) positioned at the front of the robot cleaner (100). Accordingly, the camera (110) may acquire an image of the floor surface to which the light output through the light emitting unit has been irradiated. For example, the light emitting unit may be positioned on the robot cleaner (100) at a height of 0.5 cm to 1 cm from the floor surface.

[0067] In addition, the light emitting unit may further include a lens, mirror, etc. that emits light so that the light output from the light emitting unit spreads. Through this, the light emitting unit can irradiate light to a wider area of ​​the floor surface located in front of the robot cleaner (100). For example, the light emitting unit can output light within a range of 20° up and down and a range of 60° or more left and right.

[0068] FIG. 3 is a flowchart schematically illustrating a method for controlling a robot vacuum cleaner according to an embodiment of the present disclosure.

[0069] Referring to FIG. 3, the processor (140) can acquire at least one image via the camera (110) while the robot cleaner (100) is driving along a first driving path (S310). Here, the first driving path may be a driving path set for a driving space in which the robot cleaner (100) is located.

[0070] Specifically, preset driving path information may be stored in the memory (not shown) of the robot cleaner (100). Here, the preset driving path may be set based on the size of the driving space where the robot cleaner (100) is located, the location of the station of the robot cleaner (100), etc. In addition, the driving space may be an indoor space where the robot cleaner (100) is located.

[0071] In order to set a driving path, map data (10) for the driving space where the robot cleaner (100) is located may be stored in the memory (not shown) of the robot cleaner (100). The processor (140) may acquire environmental information of the driving space (e.g., information on objects (walls, furniture, etc.) within the driving space) identified based on an image acquired through the camera (110) of the robot cleaner (100) and generate map data (10) based on the acquired environmental information. In addition, the robot cleaner (100) may identify objects (walls, furniture, etc.) within the driving space based on sensing information (e.g., distance information on objects, point cloud information on objects, etc.) about the environment of the driving space through a sensor (not shown) and generate map data (10) based on information on the identified objects. Meanwhile, the processor (140) may generate map data (10) for the driving space using a simultaneous localization and mapping (SLAM) algorithm.

[0072] The processor (140) can set a driving path for the driving space of the robot cleaner (100) based on the generated map data (10). At this time, the processor (140) can set a driving start position and a driving end position on the map data (10) and then set an optimal driving path for the robot cleaner (100) to drive from the driving start position to the driving end position. For example, the processor (140) can set a driving path for the driving space while generating the map data (10) based on a simultaneous localization and mapping (SLAM) algorithm. Alternatively, the processor (140) can set a driving path within the driving space using a local path planning algorithm.

[0073] FIG. 4 is an exemplary diagram showing a driving path set according to a mode of a robot vacuum cleaner according to an embodiment of the present disclosure.

[0074] The processor (140) can set multiple driving paths depending on the mode of the robot cleaner (100). For example, the mode of the robot cleaner (100) may include a mode in which the robot cleaner (100) drives and simultaneously performs cleaning (hereinafter, a first mode) and a mode in which the robot cleaner (100) only drives and detects a cleaning target area and then selectively cleans only the cleaning target area (hereinafter, a second mode).

[0075] At this time, the driving paths of each of the first mode and the second mode can be set in consideration of the size of the suction unit (120) of the robot cleaner (100), etc. For example, referring to FIG. 4, the interval (d1) of the driving path (510) of the first mode can be set to have a width smaller than or equal to the width (d) of the brush (121) of the suction unit (120). On the other hand, the interval (d2) of the driving path (520) of the second mode can be set to have a width larger than the width (d) of the brush (121) of the suction unit (120).

[0076] That is, the interval (d1) between the driving paths of the first mode can be set smaller than the interval (d2) between the driving paths of the second mode. In the case of the first mode, since the robot cleaner (100) performs the cleaning operation while driving, a more detailed driving path (510) for the driving space can be set. On the other hand, in the case of the second mode, since the robot cleaner (100) does not drive while cleaning but quickly detects the area to be cleaned, the driving path (520) can be set less detailed than the driving path (510) of the first mode. Therefore, in the case of the second mode, the robot cleaner (100) can perform the cleaning operation for the driving space more quickly than in the first mode.

[0077] In the following, the description will be made assuming that the robot cleaner (100) drives in the driving space based on the driving path (520) of the second mode. Accordingly, the driving path (520) of the second mode will be referred to as the first driving path.

[0078] Meanwhile, referring to FIG. 4, map data (10) may include a plurality of cells (1) (or areas). Here, a cell (11) refers to a basic unit of map data (10) that represents a specific location (or area) in a real driving space. For example, the cells (11) each have the same size and shape (e.g., square, triangle, hexagon, polygon, circle, oval, etc.) and may be arranged in a grid shape in the map data (10).

[0079] That is, each cell (11) on the map data (10) may correspond to each location (or area) in real space. Each cell (11) may include probability information on the existence of an object. Accordingly, the processor (140) may identify a drivable location or area of ​​the robot cleaner (100) based on the probability information included in each cell (11) and set a driving path (i.e., the first and second driving paths (510 and 520)). However, for the convenience of explanation of the present disclosure, probability information (i.e., probability information on the existence of an object) is not illustrated in each cell of the map data in FIG. 5A, FIG. 5B, FIG. 7, and FIG. 8, including FIG. 4.

[0080] Meanwhile, the processor (140) can control the driving unit (130) to drive the robot cleaner (100) according to the first driving path (520) stored in the memory (not shown). Specifically, the processor (140) can control the robot cleaner (100) to drive in the driving space along the first driving path by rotating the wheel of the driving unit (130). In addition, the processor (140) can acquire multiple images of the floor surface located in front of the robot cleaner (100) through the camera (110) while the robot cleaner (100) is driving.

[0081] The processor (140) identifies the amount of dust in the area within the field of view of the camera (110) based on the acquired image (S320).

[0082] Specifically, the processor (140) can identify at least one object within the acquired image. In addition, the processor (140) can identify at least one object corresponding to dust among the identified at least one object.

[0083] At this time, the processor (140) can identify objects smaller than a preset size within the acquired image as dust. For example, the processor (140) can identify points smaller than a preset size within the acquired image and identify the identified points as dust on the floor surface in front of the robot cleaner (100).

[0084] In addition, the processor (140) can identify an object having a size smaller than a preset size and a brightness larger than a preset brightness within the acquired image as dust. For example, the processor (140) can identify each pixel value of a plurality of pixels within the acquired image. At this time, the processor (140) can identify a pixel having a pixel value larger than a preset value as dust. In particular, the processor (140) can group a plurality of adjacent pixels having pixel values ​​larger than a preset value, and identify the size of an object corresponding to the plurality of pixels based on the number of the plurality of pixels belonging to the group (or the size of the group including the plurality of pixels). In addition, if the size of the object is identified as being smaller than the preset size, the object can be identified as dust.

[0085] At this time, the processor (140) may identify dust based on the difference in pixel values ​​between a pixel having a pixel value greater than or equal to a preset value and other pixels surrounding the pixel. In particular, the processor (140) may identify the difference in pixel values ​​of other pixels surrounding the pixel having a pixel value greater than or equal to a preset value, and if the difference in pixel values ​​is identified as being greater than or equal to the preset value, the pixel having the pixel value greater than or equal to the preset value may be identified as dust.

[0086] Additionally, the processor (140) can input the acquired image into a neural network model trained to identify dust within an image stored in a memory (not shown), thereby identifying dust within the image. As an output value of the neural network model, the processor (140) can obtain the location and number of dust within the image.

[0087] To this end, the processor (140) can train a neural network model based on training data including multiple images stored in memory. In particular, the multiple images included in the training data may include at least one dust particle. Additionally, each image may be preprocessed so that at least one dust particle included in each image is bounded. Based on the multiple images, the processor (140) can train the neural network model to identify dust particles within the image.

[0088] Neural network models can use various networks such as deep neural networks (DNN), convolutional neural networks (CNN), recurrent neural networks (RNN), restricted Boltzmann machines (RBM), deep belief networks (DBN), and deep Q-networks (DQN).

[0089] The processor (140) can identify a plurality of dust particles within an image and then identify the amount of dust present on the floor surface located within the field of view of the camera (110) based on the number of identified dust particles. For example, the processor (140) can calculate the amount of dust (or dust concentration) in the area of ​​the floor surface located within the field of view of the camera (110) by adding up the number of dust particles.

[0090] In particular, the processor (140) can identify a plurality of cells (11) corresponding to the floor surface located within the field of view of the camera (110) on the map data (10) and identify the amount of dust present in each cell. In addition, the processor (140) can identify the amount of dust relative to the area of ​​each cell and identify the dust concentration of each cell.

[0091] Meanwhile, according to one embodiment of the present disclosure, the processor (140) can irradiate light toward the floor surface in front of the robot cleaner (100) using the light-emitting unit of the robot cleaner (100), obtain an image of the area irradiated with light through the camera (110), and then identify the amount of dust in the area within the field of view of the camera (110) based on the obtained image.

[0092] Specifically, the processor (140) can output light toward the front of the robot cleaner (100) through the light emitting unit while the robot cleaner (100) is moving. In addition, the processor (140) can obtain an image of the floor surface to which the output light is irradiated through the camera (110).

[0093] In particular, the processor (140) can more accurately identify dust existing on the floor surface within the image acquired through the camera (110) as the light output through the light emitting unit is irradiated onto the floor surface. At this time, the processor (140) can identify the pixel value of each pixel within the image and identify dust within the image based on the identified pixel value. In particular, as the light is irradiated, the pixel value of the pixel within the image for dust existing on the floor surface may have a relatively high value. Accordingly, the processor (140) can identify pixels having a pixel value greater than a preset value as dust. In this regard, the above-described embodiment of identifying dust can be equally applied, so a detailed description thereof will be omitted.

[0094] Then, the processor (140) can control the suction unit (120) to perform a cleaning operation on the identified cleaning target area based on the identified dust amount after identifying the dust amount (S330).

[0095] Specifically, the processor (140) can identify a cleaning target area based on the identified amount of dust. In particular, the processor (140) can identify an area where the identified amount of dust is greater than a preset amount as a cleaning target area. In this case, the cleaning target area can be identified among the floor surfaces located within the field of view of the camera (110) identified in the image.

[0096] In order to identify a cleaning target area, the processor (140) can group a plurality of dust particles within an image and identify an area including the grouped plurality of dust particles as a cleaning target area. In particular, the processor (140) can group dust particles whose distance between adjacent dust particles is within a preset distance and identify the number of dust particles included in the group. In addition, the processor (140) can identify that the amount of dust particles is greater than or equal to the preset amount if the number of dust particles included in the group is greater than or equal to the preset number. In addition, the processor (140) can identify a cleaning target area on the floor surface in front of the robot cleaner (100) based on the location of a group including a preset number of dust particles or more within the image.

[0097] For example, the processor (140) may identify a group containing a preset number of dust particles as a cleaning target area within an image. Then, the processor (140) may identify a location of the cleaning target area on the floor surface in front of the robot cleaner (100) based on the location of the cleaning target area identified within the image. For example, the processor (140) may obtain coordinate information of pixels corresponding to the cleaning target area within the image and then convert the obtained coordinate information into coordinate information on the floor surface in front of the robot cleaner (100). Then, the processor (140) may identify a location of the cleaning target area on the floor surface in front of the robot cleaner (100) based on the converted coordinate information. To this end, the processor (140) may perform various image processing processes, such as warping, on the obtained image.

[0098] Meanwhile, the processor (140) can identify the cleaning target area on a cell basis. Specifically, the processor (140) can identify a plurality of cells (11) corresponding to the floor surface within the field of view of the camera (110) on the map data (10) based on the image acquired through the camera (110), and can identify the amount of dust or the dust concentration of each cell (11). In addition, the processor (140) can identify a cell (11) among the plurality of cells (11) in which the amount of dust is equal to or greater than a preset value or the dust concentration is equal to or greater than a preset concentration, and identify the identified cell (11) as the cleaning target area.

[0099] Meanwhile, the processor (140) can control the suction unit (120) to perform a cleaning operation when the cleaning target area is identified. Specifically, the processor (140) controls the driving unit (130) to move the robot cleaner (100) to the cleaning target area, and then, when the robot cleaner (100) is positioned in the cleaning target area, controls the suction unit (120) to suction dust present in the cleaning target area.

[0100] Through this, the robot cleaner (100) of the present disclosure can perform cleaning operations on the driving space quickly and cleanly by selectively performing cleaning operations only on areas requiring cleaning (i.e., cleaning target areas) within the driving space.

[0101] FIGS. 5a and 5b are exemplary diagrams showing a method for generating first map data (20) and second map data (30) acquired by a robot cleaner (100) according to one embodiment of the present disclosure.

[0102] The processor (140) can obtain map data indicating the amount of dust in an area within the field of view of the camera (110) in which the robot cleaner (100) has driven based on the identified amount of dust, and map data indicating the amount of dust in an area within the field of view of the camera (110) in which the robot cleaner (100) will drive (S340).

[0103] Here, the map data indicating the amount of dust may be map data including dust information within the driving space. Hereinafter, for the convenience of explanation of the present disclosure, the map data indicating the amount of dust in the driving area will be referred to as first map data (20), and the map data indicating the amount of dust in the driving area will be referred to as second map data (30).

[0104] The first map data (20) may be map data including information on the amount of dust in an area in which the robot cleaner (100) has already driven or performed a cleaning operation. Furthermore, the first map data (20) may also include information on the amount of dust in an area in which the robot cleaner (100) has already identified the amount of dust while driving. Specifically, information on the amount of dust in an area not located along the driving path of the robot cleaner (100), but in which the robot cleaner (100) has already identified the amount of dust through an acquired image, may be included again in the first map data (20).

[0105] Meanwhile, the second map data (30) may be map data that includes information on the amount of dust in an area where the robot cleaner (100) has not yet driven. In particular, the second map data (30) may include information on the amount of dust in an area that is not located on the driving path, but where the amount of dust can be identified through an image acquired by the robot cleaner (100).

[0106] According to one embodiment of the present disclosure, the processor (140) can identify the amount of dust in an area within a driving space where the robot cleaner (100) is not positioned based on the acquired image. That is, by acquiring an image of the floor surface located in front of the robot through the camera (110) and identifying the amount of dust based on the acquired image, the processor (140) can identify the amount of dust in the floor surface in front where the robot cleaner (100) is not positioned.

[0107] In particular, the image acquired by the camera (110) as the robot cleaner (100) moves along the driving path may also include the area where the robot cleaner (100) has already driven. This is because the angle of view of the camera (110) may be wider than the interval of the driving path. Accordingly, the processor (140) may also re-identify the amount of dust in the area where the robot cleaner (100) has already driven.

[0108] Meanwhile, the first map data (20) and the second map data (30) may be generated based on the map data (10). That is, the processor (140) may identify the amount of dust in units of cells of the map data (10) and generate the first map data (20) and the second map data (30) in units of cells. Specifically, the processor (140) may identify an area located in front of the robot cleaner (100) identified through the location of the robot cleaner (100) and the acquired image on the map data (10), and may identify the amount of dust for the identified area in units of cells of the map data (10) to generate the first and second map data (20 and 30). At this time, the processor (140) may store the first map data (20) and the second map data (30) in a memory, and update the first map data (20) and the second map data (30) whenever the amount of dust in an area within the field of view of the camera (110) is identified. Specifically, the processor (140) can update the first map data (20) stored in the memory whenever the amount of dust in the driven area is identified, and can update the second map data (30) stored in the memory whenever the amount of dust in the non-driven area is identified.

[0109] In addition, the processor (140) may update (or overlap) the dust amount information identified for the driven area in the second map data (30) to generate the first map data (20). In particular, since the second map data (30) includes dust amount information for an area that has not been driven, it may include dust amount information for more cells than the first map data (20). Accordingly, when the dust amount of a cell (11) in which the vehicle has driven or in which the dust amount has already been identified is identified again, the processor (140) may update the dust amount information of the cell (11) corresponding to the same location (or area) included in the second map data (30) with the re-identified dust amount information to generate the first map data (20).

[0110] Hereinafter, a method for generating the first map data (20) and the second map data (30) will be described in detail with reference to FIGS. 5a and 5b. Meanwhile, each cell included in the map data (10) (or the first and second map data (20 and 30)) will be referred to as each area. Meanwhile, the area (11') included in the first map data (20) and the area (11") included in the second map data (30) illustrated in FIGS. 5a, 5b, 7, and 8 correspond to the area (11) of the same location included in the map data (10).

[0111] Referring to FIG. 5A, at time t1, the processor (140) may generate second map data (30) including dust amount information of an area in which the robot cleaner (100) will drive, based on an image acquired through the camera (110). Specifically, the processor (140) may acquire images for areas A, B, C, D, E, I, J, K, L, and M (specifically, areas corresponding to cells) (11-A to 11-E and 11-I to 11-M) included within the field of view of the camera (110) through the camera (110). Hereinafter, each area is referred to by a cell number, and for example, area A is referred to as area 11-A, area B is referred to as area 11-B, etc. In addition, the processor (140) may identify the amount of dust present in areas 11-A to 11-E and 11-I to 11-M based on the acquired images. Meanwhile, 11-A to 11-E and 11-I to 11-M are areas where the robot cleaner (100) has not yet driven, and the processor (140) can generate second map data (30) including dust amount information of 11-A to 11-E and 11-I to 11-M.

[0112] And, the processor (140) can identify a cleaning target area among a plurality of areas located in front of the robot cleaner (100) based on the generated second map data (30). Referring to FIG. 5A, the processor (140) can identify the amount of dust for a plurality of areas (11-A to 11-E and 11-I to 11-M) and then generate second map data (30) including dust amount information. At this time, referring to the second map data (30), the processor (140) can identify the amount of dust present in areas 11-I to 11-K among the plurality of areas (11-A to 11-E and 11-I to 11-M) as 8, 9, and 2, respectively. Accordingly, the processor (140) can identify 11-I to 11-K among the plurality of areas 11-A to 11-E and 11-I to 11-M as areas to be cleaned.

[0113] And, the processor (140) can perform a cleaning operation only for 11-I to 11-K among the plurality of areas 11-A to 11-E and 11-I to 11-M. Specifically, the processor (140) controls the driving unit (130) so that the robot cleaner (100) moves to 11-I to 11-K, and when the robot cleaner (100) moves to 11-I to 11-K, it can suck up dust through the suction unit (120).

[0114] Referring to FIG. 5b, the processor (140) can acquire an image including a plurality of areas (11-A to 11-F and 11-I to 11-O) located in front of the robot cleaner (100) at time t2 (and included in the field of view of the camera (110)) through the camera (110). Then, the processor (140) can identify the amount of dust present in the plurality of areas (11-A to 11-F and 11-I to 11-O) based on the acquired image.

[0115] At this time, the processor (140) can identify the amount of dust in the area where the robot cleaner (100) drove based on the acquired image, and generate first map data (20) including dust amount information of the area where the robot cleaner (100) drove. The processor (140) can re-identify the amount of dust in the area where the robot cleaner (100) drove based on the acquired image. Specifically, referring to FIG. 5B, 11-A to 11-C and 11-I to 11-K may be areas where the robot cleaner (100) has already driven based on the driving path. Here, the driven area may be an area overlapped by the robot cleaner (100) when the robot cleaner (100) moves along the driving path.

[0116] In particular, the areas 11-A to 11-C and 11-I to 11-K in which the robot cleaner (100) has driven may include areas to be cleaned. Areas 11-I to 11-K included in the image acquired at time t2 are areas in which the robot cleaner (100) has performed a cleaning operation immediately after time t1. Therefore, the processor (140) can re-identify the amount of dust in areas 11-I to 11-K in which the robot cleaner (100) has performed a cleaning operation based on the image acquired at time t2.

[0117] The processor (140) can identify the amount of dust in 11-A to 11-C and 11-I to 11-K, along which the robot cleaner (100) has driven since time point t1, based on the acquired image, and generate first map data (20) including dust amount information in 11-A to 11-C and 11-I to 11-K.

[0118] Meanwhile, the processor (140) can identify the amount of dust in the area where the robot cleaner (100) will drive based on the acquired image, and generate second map data (30) including information on the amount of dust in the area where the robot cleaner will drive.

[0119] Specifically, referring to FIG. 5B, 11-E to 11-G and 11-M to 11-O may be areas in which the robot cleaner (100) will drive after time t2. The processor (140) may identify the dust amount of 11-E to 11-G and 11-M to 11-O based on the acquired image, and generate second map data (30) including the identified dust amount information. In particular, the processor (140) may update the previously generated second map data (30) with the newly identified dust amount information of 11-E to 11-G and 11-M to 11-O.

[0120] Meanwhile, the processor (140) can identify the results of the cleaning operation performed on the cleaning target area (11-I to 11-K) by comparing the generated first and second map data (20 and 30). In particular, the processor (140) can identify the dust reduction rate, etc. of the cleaning target area based on the dust amount information of the cleaning target area included in the first and second map data (20 and 30).

[0121] In addition, the processor (140) may identify an area previously identified as an area to drive in again as an area to drive in by the robot cleaner (100). That is, the processor (140) may repeatedly identify an area to drive in by the robot cleaner (100). Specifically, referring to FIG. 5A, the processor (140) may identify 11-D, 11-E, 11-M, and 11-N, which are included within the field of view of the camera (110), as areas to drive in by the robot cleaner (100), based on an image acquired at time t1. Accordingly, the processor (140) may include dust amount information of 11-D, 11-E, 11-M, and 11-N in the second map data (30) at time t1. Referring to FIG. 5B, based on the images acquired at time t2 after time t1, the processor (140) can re-identify 11-D, 11-E, 11-M, and 11-N as areas where the robot cleaner (100) will drive. In addition, based on the acquired images, the processor (140) can re-identify the amount of dust in 11-D, 11-E, 11-M, and 11-N. At this time, if the amount of dust in the area to drive changes, the processor (140) can update the second map data (30) with the most recently acquired dust amount information. This is because the amount of dust in the area to drive may change as dust is generated depending on the environment within the driving space (e.g., operation of an air conditioner within the driving space).

[0122] Referring to FIG. 5a, in the second map data (30) generated at time t1, it is identified that there is no dust in the driving areas 11-D, 11-E, 11-M, and 11-N, whereas in the second map data (30) generated at time t2, it is identified that there is dust in 11-E and 11-M among the driving areas 11-D, 11-E, 11-M, and 11-N. That is, the processor (140) can identify the amount of dust in 11-E and 11-M as 8 and 9, respectively, based on the image acquired at time t2, and update the second map data (30) based on the identified amount of dust. Accordingly, the processor (140) can newly identify the E and M areas (11-E and 11-M) as areas to be cleaned.

[0123] FIG. 6 is a flowchart schematically illustrating a method for controlling a robot cleaner (100) to perform a cleaning operation on a cleaning target area not located on a driving path according to an embodiment of the present disclosure. Steps S610, S620, and S670 illustrated in FIG. 6 may correspond to steps S310, S320, and S340 illustrated in FIG. 3.

[0124] FIG. 7 is an exemplary diagram schematically illustrating a method for controlling a robot cleaner to perform a cleaning operation on a cleaning target area not located on a driving path according to one embodiment of the present disclosure.

[0125] According to one embodiment of the present disclosure, the processor (140) may adjust the first driving path (512) if the cleaning target area is not located on the first driving path (512). Specifically, the processor (140) may change the first driving path (512) so that the robot cleaner (100) can move to a cleaning target area that is not located on the first driving path (512).

[0126] In particular, the processor (140) can set a new driving path for moving from the location of the robot cleaner (100) to the cleaning target area at the time the cleaning target area is identified, and adjust the existing first driving path (512) based on the set driving path.

[0127] In this regard, referring to FIG. 6, the processor (140) identifies a cleaning target area within the camera's field of view based on the amount of dust identified based on the acquired image (S630), and if the cleaning target area is not located on the driving path, a second driving path for the cleaning target area can be set (S640). The second driving path refers to a new driving path that moves from the location of the robot cleaner (100) to the cleaning target area at the time the cleaning target area is identified. The second driving path may include a driving path that moves from the location of the robot cleaner (100) to the cleaning target area that is not located on the first driving path (512) at the time the cleaning target area is identified, and a driving path that returns to the existing first driving path (512) from the cleaning target area.

[0128] The processor (140) can identify whether the cleaning target area identified based on the amount of dust is located on the driving path. When the robot cleaner (100) moves along the driving path, the processor (140) can identify whether the cleaning target area is located on the driving path by identifying whether the robot cleaner (100) can perform a cleaning operation on the cleaning target area.

[0129] In particular, the processor (140) can identify whether the cleaning target area is located on the first driving path (512) by considering not only the first driving path (512) set for the robot cleaner (100), but also the size, width, etc. of the suction unit (120). For example, when the robot cleaner (100) continues to move along the first driving path (512), if the processor (140) identifies that the suction unit (120) of the robot cleaner (100) can be located on the cleaning target area, the processor can identify that the cleaning target area is located on the first driving path (512).

[0130] On the other hand, if the processor (140) identifies that the suction unit (120) of the robot cleaner (100) cannot be positioned on the cleaning target area while the robot cleaner (100) continues to move along the first driving path (512), the processor can identify that the cleaning target area is not positioned on the first driving path (512).

[0131] If the cleaning target area is identified as not being located on the first driving path (512), the processor (140) can set a new driving path from the position of the robot cleaner (100) at the time of identifying the cleaning target area (e.g., the position of the robot cleaner (100) on the first driving path (512) at the time of identifying the cleaning target area) to the cleaning target area.

[0132] For example, referring to FIG. 5b and FIG. 7, the processor (140) can newly identify the E and M areas (11-E and 11-M) as cleaning target areas based on the updated second map data (30). At this time, the processor (140) can identify, based on the positions of the newly identified E and M areas (11-E and 11-M) as cleaning target areas and the preset first driving path (512), that the positions of the E and M areas (11-E and 11-M) as cleaning target areas are not located on the first driving path (512). Accordingly, the processor (140) can set a second driving path (513) along which the robot cleaner (100) can drive to the E and M areas (11-E and 11-M). Here, the second driving path (513) may be a driving path along which the robot cleaner (100) can drive from the existing first driving path (512) to the E and M areas (11-E and 11-M). In addition, the processor (140) may adjust the first driving path (512) based on the set second driving path (513). In particular, the processor (140) may adjust the existing first driving path (512) to include the set second driving path (513).

[0133] In addition, the processor (140) can control the driving unit so that the robot cleaner (100) deviates from the first driving path (512) and drives to the cleaning target area along the set second driving path (513) (S650).

[0134] The processor (140) can control the driving unit (130) to drive the robot cleaner (100) to the cleaning target area according to the set second driving path (513). In addition, when the cleaning operation for the cleaning target area is completed, the processor (140) can control the driving unit (130) to return the robot cleaner (100) to the existing first driving path (512).

[0135] At this time, the processor (140) may obtain second map data (30) based on the amount of dust in the cleaning target area based on the acquired image before performing the cleaning operation on the cleaning target area that is not located on the first driving path (512). Then, when the cleaning operation on the cleaning target area along the second driving path (513) is completed, the processor (140) may control the driving unit so that the robot cleaner (100) returns to the first driving path (512) and then drives along the first driving path (512). Then, when the amount of dust in the cleaning target area is re-identified based on at least one image acquired through the camera (110) while the robot cleaner (100) drives along the first driving path (512), the processor (140) may obtain first map data (20) based on the re-identified amount of dust. Since the description of the present disclosure with respect to FIGS. 5A and 5B described above applies equally to this, a detailed description thereof will be omitted.

[0136] Meanwhile, according to one embodiment of the present disclosure, when the robot's driving along the first driving path (512) is completed, the processor (140) may identify at least one re-cleaning target area among a plurality of cleaning target areas based on the identified amount of dust. Then, the processor (140) may set a third driving path (514) for the at least one identified re-cleaning target area, and control the driving unit so that the robot cleaner (100) drives to the at least one re-cleaning target area along the set third driving path (514).

[0137] Specifically, the processor (140) can identify the amount of dust re-identified for a plurality of cleaning target areas based on the first map data (20) when the robot cleaner (100) completes driving based on the first driving path (512) (or the adjusted first driving path). Then, the processor (140) can identify a re-cleaning target area where the robot cleaner (100) will perform the cleaning operation again among the plurality of cleaning target areas based on the re-identified amount of dust. In particular, the processor (140) can identify a cleaning target area where the re-identified amount of dust is equal to or greater than a preset value or where the dust reduction rate is less than a preset value based on the first and second map data (20 and 30), as a re-cleaning target area.

[0138] In addition, the processor (140) may set a third driving path (514) for the re-cleaning target area so that the robot cleaner (100) can perform cleaning operations only for the identified re-cleaning target area. In particular, since the third driving path (514) is set only for the re-cleaning target area selected from among the plurality of cleaning target areas, the shape and length may be simplified or shorter than those of the first driving path (512).

[0139] In addition, the processor (140) can perform a re-cleaning operation on the re-cleaning target area based on the set third driving path (514). Specifically, the processor (140) controls the driving unit so that the robot cleaner (100) drives to at least one re-cleaning target area according to the third driving path (514), and when the robot cleaner (100) moves to the re-cleaning target area, it can suck up dust through the suction unit.

[0140] FIG. 8 is an exemplary diagram showing setting a third driving path (514) for a re-cleaning target area identified based on first map data (20) according to one embodiment of the present disclosure.

[0141] Referring to FIG. 8, the processor (140) can identify six cleaning target areas (J, M, E, S, U and Y areas (11-J, 11-M, 11-E, 11-Q, 11-R, 11-S, 11-T, 11-U, 11-V, 11-W, 11-X, 11-Y and 11-Z)) among 15 cleaning target areas (I, J, K, M, E, Q, R, S, T, U, V, W, X, Y and Z areas (11-I, 11-J, 11-K, 11-M, 11-E, 11-Q, 11-R, 11-S, 11-T, 11-U, 11-V, 11-W, 11-X, 11-Y and 11-Z)) in which dust still exists as re-cleaning target areas. And, the processor (140) can identify six re-cleaning target areas (J, M, E, S, A third driving path (514) for the U and Y areas (11-J, 11-M, 11-E, 11-S, 11-U, and 11-Y) can be set, and a cleaning operation can be performed for the re-cleaning target areas (J, M, E, S, U, and Y areas (11-J, 11-M, 11-E, 11-S, 11-U, and 11-Y)) based on the set third driving path (514).

[0142] In particular, the re-cleaning target area of ​​the robot cleaner (100) may include not only the cleaning target area identified based on the second map data (30), but also new areas that have not been identified as cleaning target areas. In particular, depending on the environment within the driving space where the robot cleaner (100) is located, dust may newly exist in areas that the robot cleaner (100) has not identified as cleaning target areas. Therefore, the processor (140) uses the first map data (20) including dust amount information for the area in which the robot cleaner (100) has driven to re-identify the amount of dust in the area in which the robot cleaner (100) has already driven, and may identify re-cleaning target areas not only in the existing cleaning target areas but also in areas that have not been identified as cleaning target areas.

[0143] Meanwhile, the processor (140) can control the suction unit (120) to suck up dust within the cleaning target area with a suction strength corresponding to the amount of dust in the cleaning target area. That is, the processor (140) can set the suction strength of the suction unit (120) in the cleaning target area to correspond to the amount of dust. For example, the processor (140) can set the strength of the suction unit (120) to be stronger as the amount of dust increases. Accordingly, when a plurality of cleaning target areas are identified within the driving space, the suction strength through the suction unit (120) in each cleaning target area can be set to be proportional to the amount of dust present in each cleaning target area.

[0144] In the case of a conventional robot cleaner (100), a cleaning operation was performed with a suction strength set in advance. In particular, in the case of the conventional robot cleaner (100), the amount of dust was not identified in advance through an image, but rather the amount of dust was identified during the process of sucking up the dust. Therefore, the suction strength could not be set to correspond to the amount of dust identified in advance. However, since the robot cleaner (100) of the present disclosure can identify the amount of dust on the floor surface located in front of the robot in advance (particularly, the area to be cleaned), the suction strength can be set in accordance with the amount of dust. Through this, the battery power consumption of the robot cleaner (100) can be reduced.

[0145] Meanwhile, the processor (140) can turn off the suction unit (120) while the robot cleaner (100) is driving, and turn on the suction unit (120) when the robot cleaner (100) is located in the cleaning target area. That is, the processor (140) can keep the suction unit (120) from operating by turning off the suction unit (120) while the robot cleaner (100) is driving along the driving path. In addition, the processor (140) can turn on the suction unit (120) to perform the cleaning operation only when the robot cleaner (100) moves to the cleaning target area and is identified as being located in the cleaning target area. Through this, the effect of reducing the battery power consumption of the robot cleaner (100) can be achieved.

[0146] Meanwhile, according to one embodiment of the present disclosure, when the processor (140) identifies that a cleaning target area where a cleaning operation has been completed is included within the field of view range of the camera (110), the intensity of light output through the light emitting unit can be increased from the first intensity to the second intensity.

[0147] Specifically, the processor (140) can identify whether a cleaning target area where a cleaning operation has been completed is included within the field of view of the camera (110) based on an image acquired through the camera (110). Then, if the processor (140) identifies that a cleaning target area where a cleaning operation has been completed is included within the field of view, the processor (140) can increase the intensity of light output through the light emitting unit. In particular, the processor (140) can increase the intensity of light output through the light emitting unit (not shown) from a first intensity to a second intensity, and for this purpose, the processor (140) can increase the driving current transmitted to the light emitting element included in the light emitting unit.

[0148] FIG. 9 is an exemplary diagram showing first map data (20) and second map data (30) including dust information of a cleaning target area acquired by a robot cleaner (100) according to one embodiment of the present disclosure being displayed on a user terminal.

[0149] According to one embodiment of the present disclosure, the processor (140) may transmit the first and second map data (20 and 30) to the user terminal (200) via a communication interface (not shown) whenever the first and second map data (20 and 30) are updated. At this time, the processor (140) may display the amount of dust identified on the cleaning target area included in the first and second map data (20 and 30) or may display a GUI, an image object, etc. to enable the user to recognize the cleaning target area and transmit the displayed information to the user terminal (200). For example, the processor (140) may display a color only for the cleaning target area among the plurality of areas included in the second map data (30) so that the user can intuitively identify the cleaning target area. At this time, the processor (140) may also display a different color for the cleaning target area depending on the amount of dust in the cleaning target area. For example, if the amount of dust is greater than or equal to the first value and less than or equal to the second value, the area to be cleaned may be displayed in yellow, if the amount of dust is greater than or equal to the second value and less than or equal to the third value, the area may be displayed in red, and if the amount of dust is greater than or equal to the third value, the area to be cleaned may be displayed in black.

[0150] Meanwhile, the processor (140) can identify a dust reduction rate for the cleaning target area based on the dust amount information of the cleaning target area included in the first and second map data (20 and 30), and display the identified dust reduction rate on the cleaning target area in the first map data (20) and transmit it to the user terminal (200) via the communication interface. Through this, the user can identify the cleaning efficiency of the robot cleaner (100) for the cleaning target area.

[0151] Referring to FIG. 9, the user terminal (200) can receive the first and second map data (20 and 30) from the robot cleaner (100) in real time whenever the first and second map data (20 and 30) are updated. In particular, the first map data (20) including dust amount information for an area where driving has been completed can be displayed on the user terminal in the form of a cleaning result report for the area to be cleaned, and the second map data (30) including dust amount information for an area where the robot cleaner (100) will drive can be displayed in the form of an identification report for the area to be cleaned.

[0152] Meanwhile, the processor (140) may periodically calculate the number of times a cleaning target area has been identified for each area based on the second map data (30). Then, the processor (140) may select a caution target area for an area that has been identified as a cleaning target area a preset number of times or more, and may transmit information about the caution target area to a user terminal via a communication interface. In particular, the processor (140) may display the location of the caution target area on map data corresponding to the driving space, and may transmit a message requesting user action for the map data and the caution target area to the user terminal via the communication interface.

[0153] FIG. 10 is a detailed block diagram of a robot vacuum cleaner (100) according to one embodiment of the present disclosure.

[0154] Referring to FIG. 10, the robot cleaner (100) includes a camera (110), a suction unit (120), a driving unit (130), a light emitting unit (150), a display (160), a sensor (170), a user interface (180), a communication interface (190), and one or more processors (140). A detailed description of components that overlap with the components illustrated in FIG. 2 will be omitted.

[0155] The light emitting unit (150) may be configured to output light toward the floor surface located in front of the robot cleaner (100). To this end, the light emitting unit (150) may include at least one light emitting element. For example, the light emitting element may be an LED (Light Emitting Diode), a micro LED, etc. In this case, the light emitting element may include an LED array including a plurality of LEDs.

[0156] The display (160) displays various image information under processor control. Here, the image may be in various formats, such as text, still images, moving images, and GUI (Graphic User Interface). In particular, the display (160) may display first and second map data.

[0157] In addition, the display (160) can be implemented as a touch screen along with a touch panel. In this case, the display (160) can function as an output unit that outputs information between the robot cleaner (100) and the user, and at the same time, can function as an input unit that provides an input interface between the robot cleaner (100) and the user.

[0158] For this purpose, various types of displays (160) capable of displaying images, such as LCD (Liquid Crystal Display), LED (Light Emitting Diode), and PDP (Plasma Display Panel), may be included. The display (160) may additionally include additional components depending on its implementation method. For example, when the display (160) is a liquid crystal display, the display (160) may include an LCD display panel (not shown), a backlight unit (not shown) that supplies light thereto, and a panel driver plate (not shown) that drives the panel (not shown).

[0159] The sensor (170) can obtain various sensing information around the robot cleaner (100). For example, the sensor (170) can obtain distance information about objects (e.g., walls, furniture, electronic devices, etc.) located around the robot cleaner (100). Based on the sensing information obtained through the sensor (170), the processor (140) can generate map data about the driving space where the robot cleaner (100) is located. Then, based on the generated map data, the processor (140) can generate first and second map data (20 and 30) including dust amount information. For this purpose, the sensor (170) can be implemented as a ToF sensor, a lidar sensor, an infrared sensor, etc.

[0160] The user interface (180) is a configuration used by the robot cleaner (100) to interact with the user, and the processor (140) can receive various information through the user interface (180). Meanwhile, the user interface (180) may include at least one of a touch sensor (170), a motion sensor (170), a button, a jog dial, a switch, a microphone, or a speaker, but is not limited thereto.

[0161] The communication interface (190) can communicate with external devices (e.g., user terminals (200)) and external servers through various communication methods. The communication interface (190) can be connected to external devices and external servers to communicate with them, including communicating via a third-party device (e.g., a repeater, hub, access point, gateway, etc.). For example, the external device can be implemented as another electronic device, a server, cloud storage, a network, etc.

[0162] The communication interface (190) may include various communication modules to perform communication with an external device. For example, the communication interface (190) may include a wireless communication module, and may include a cellular communication module that uses at least one of, for example, 3rd Generation (3G), 3rd Generation Partnership Project (3GPP), Long Term Evoloution (LTE), LTE-A (LTE Advance), CDMA (code division multiple access), WCDMA (wideband CDMA), UMTS (universal mobile telecommunications system), WiBro (Wireless Broadband), or GSM (Global System for Mobile Communications). As another example, the wireless communication module may include at least one of, for example, WiFi (wireless fidelity), Bluetooth, Bluetooth low energy (BLE), and Zigbee.

[0163] In addition, the robot cleaner (100) may further include memory. The memory may store data required for various embodiments of the present disclosure. Specifically, the memory may store map data (10) and driving path information (e.g., first driving path (512) information) of the robot cleaner (100) according to an embodiment of the present disclosure.

[0164] Depending on the purpose of data storage, the memory may be implemented in the form of memory embedded in the robot cleaner (100) or may be implemented in the form of memory that can be detached from the robot cleaner (100). For example, data for operating the robot cleaner (100) may be stored in a memory embedded in the robot cleaner (100), and data for expanding the functions of the robot cleaner (100) may be stored in a memory that can be detached from the robot cleaner (100).

[0165] Meanwhile, in the case of the memory embedded in the robot cleaner (100), it may be implemented as at least one of volatile memory (e.g., dynamic RAM (DRAM), static RAM (SRAM), or synchronous dynamic RAM (SDRAM)), non-volatile memory (e.g., one time programmable ROM (OTPROM), programmable ROM (PROM), erasable and programmable ROM (EPROM), electrically erasable and programmable ROM (EEPROM), mask ROM, flash ROM, flash memory (e.g., NAND flash or NOR flash), hard drive, or solid state drive (SSD)).

[0166] In addition, in the case of a memory that can be attached or detached to the robot cleaner (100), it can be implemented in the form of a memory card (e.g., CF (compact flash), SD (secure digital), Micro-SD (micro secure digital), Mini-SD (mini secure digital), xD (extreme digital), MMC (multi-media card), etc.), an external memory that can be connected to a USB port (e.g., USB memory (130)), etc.

[0167] Meanwhile, the methods according to various embodiments of the present disclosure described above may be implemented in the form of an application that can be installed on an existing robot cleaner (100). Alternatively, the methods according to various embodiments of the present disclosure described above may be performed using a deep learning-based trained neural network (or a deep learned neural network), i.e., a learning network model. In addition, the methods according to various embodiments of the present disclosure described above may be implemented only with a software upgrade or hardware upgrade for an existing robot cleaner. In addition, the various embodiments of the present disclosure described above may also be performed through an embedded server provided in the robot cleaner (100) or an external server of the robot cleaner (100).

[0168] Meanwhile, according to a temporary example of the present disclosure, the various embodiments described above can be implemented as software including commands 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 commands stored from the storage medium and operate according to the called commands, and may include a display device (e.g., display device (A)) according to the disclosed embodiments. When a command is executed by a processor, the processor can perform a function corresponding to the command directly or under the control of the processor by using other components. The command 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.

[0169] Furthermore, according to one embodiment, 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 device-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.

[0170] 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.

[0171] 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, camera; A suction unit for sucking dust from the surface on which the robot cleaner is placed; A driving unit for moving the above robot cleaner; At least one memory for storing one or more instructions; and comprising one or more processors; The one or more processors, by executing the one or more instructions, While the robot cleaner moves along the first driving path, at least one image is acquired through the camera, Identifying the amount of dust in one or more areas within the field of view of the camera based on at least one image; Controlling the suction unit to perform a cleaning operation on a cleaning target area among the one or more areas based on the identified dust amount; Based on the identified amount of dust, first map data is obtained indicating the amount of dust in a first area where the robot cleaner has driven among one or more areas within the field of view of the camera, and the first area corresponds to a part of the first driving path where the robot cleaner has previously driven. Obtain second map data indicating the amount of dust in a second area where the robot cleaner will drive among one or more areas within the field of view of the camera, wherein the second area corresponds to a part of the first driving path where the robot cleaner did not drive; A robot cleaner, wherein, if the cleaning target area is not located on the first driving path, a second driving path for the cleaning target area is set, and the driving unit is controlled so that the robot cleaner drives along the set second driving path.

2. In paragraph 1, The one or more processors, by executing the one or more instructions, A robot cleaner that identifies an area within the field of view of the camera, wherein the area has a dust amount greater than a preset value, as the cleaning target area based on the identified dust amount.

3. In paragraph 1, The one or more processors, by executing the one or more instructions, Before performing a cleaning operation on the above cleaning target area, the second map data is acquired, When the cleaning operation for the cleaning target area along the second driving path is completed, the driving unit is controlled to return the robot cleaner to the first driving path. A robot cleaner, wherein the first map data includes information for re-identifying the amount of dust in at least one area among one or more areas where the robot cleaner has previously completed a cleaning operation.

4. In paragraph 3, further comprising a communication interface; The one or more processors, by executing the one or more instructions, Identifying a dust reduction rate for the cleaning target area based on the amount of dust in the cleaning target area included in the first and second map data, A robot cleaner that transmits the dust reduction rate of the identified cleaning target area and the first map data to a user terminal device through the communication interface.

5. In paragraph 3, The one or more processors, by executing the one or more instructions, When the driving of the robot along the first driving path is completed, at least one re-cleaning target area among the one or more cleaning target areas is identified based on the first map data, and the re-cleaning target area is an area previously cleaned by the robot cleaner, Establishing a third driving path for at least one of the above-identified re-cleaning target areas, A robot cleaner that controls the driving unit so that the robot cleaner drives to at least one re-cleaning target area according to the third driving path set above.

6. In paragraph 1, The one or more processors, by executing the one or more instructions, A robot cleaner that controls the suction unit to suck up dust within the cleaning target area with a suction strength corresponding to the amount of dust identified in the cleaning target area.

7. In paragraph 2, The one or more processors, by executing the one or more instructions, A robot cleaner, wherein the suction unit is activated while the robot cleaner is driving, and the suction unit is deactivated when the robot cleaner is located in one or more of the target cleaning areas.

8. In paragraph 1, Including more light emitting parts, The one or more processors, by executing the one or more instructions, While the robot cleaner is driving along the first driving path, light is output toward the floor surface located in front of the robot through the light emitting unit, A robot cleaner, wherein said at least one image includes at least one image of a floor surface on which said output light is irradiated.

9. In paragraph 8, The one or more processors, by executing the one or more instructions, A robot cleaner, wherein when it is identified that one or more areas within the field of view of the camera include the cleaning target area where the cleaning operation has been completed, the intensity of light output through the light emitting unit is increased from the first intensity to the second intensity.

10. In the method of controlling a robot vacuum cleaner, A step of acquiring at least one image through a camera while the robot cleaner moves along a first driving path; A step of identifying the amount of dust in one or more areas within the field of view of the camera based on the at least one image; A step of controlling a suction unit to perform a cleaning operation on a cleaning target area among the one or more areas based on the identified dust amount; A step of obtaining first map data indicating the amount of dust in a first area where the robot cleaner has driven among one or more areas within the field of view of the camera based on the identified amount of dust; and A step of acquiring second map data indicating the amount of dust in a second area where the robot cleaner is to drive among one or more areas within the field of view of the camera; The above first region corresponds to a portion of the first driving path along which the robot cleaner previously drove, The second region corresponds to a portion of the first driving path on which the robot cleaner did not drive, The step of controlling the above suction unit is: A step of identifying a cleaning target area within the field of view of the camera based on the identified amount of dust; If the cleaning target area is not located on the driving path, a step of setting a second driving path for the cleaning target area; and A control method comprising: a step of controlling a driving unit to cause the robot cleaner to drive along the second driving path set above.

11. In paragraph 10, The step of controlling the above suction unit is: A control method further comprising: a step of identifying an area among the one or more areas in which the amount of dust is greater than a preset value as the area to be cleaned; 12. In paragraph 10, When the cleaning operation for the cleaning target area along the second driving path is completed, the step of controlling the driving unit so that the robot cleaner returns to the first driving path is further included. The step of obtaining the above second map data is: A step of acquiring the second map data before performing a cleaning operation on the cleaning target area; and A control method, comprising a step of re-identifying the amount of dust in the cleaning target area among one or more areas previously cleaned by the robot cleaner to obtain the first map data.

13. In paragraph 12, A step of identifying a dust reduction rate for the cleaning target area based on the first and second map data; A control method comprising the step of transmitting the first map data and the dust reduction rate for the identified cleaning target area to a user terminal device through a communication interface.

14. In paragraph 12, When the driving of the robot along the first driving path is completed, a step of identifying at least one area to be cleaned among the one or more areas based on the first map data; A step of setting a third driving path for at least one of the identified re-cleaning target areas; and A step of controlling the driving unit so that the robot cleaner drives according to the third driving path set above; A control method, wherein at least one of the re-cleaning target areas is an area previously cleaned by the robot cleaner.

15. A non-transitory computer-readable storage medium storing computer commands that, when executed by a processor of a robot cleaner, cause the robot cleaner to perform an operation, wherein the operation comprises: A step of acquiring at least one image through a camera while the robot cleaner moves along a first driving path; A step of identifying the amount of dust in one or more areas within the field of view of the camera based on the at least one image; A step of controlling a suction unit to perform a cleaning operation on a cleaning target area among the one or more areas based on the identified dust amount; A step of obtaining first map data indicating the amount of dust in a first area where the robot cleaner has driven among one or more areas within the field of view of the camera based on the identified amount of dust; and A step of acquiring second map data indicating the amount of dust in a second area where the robot cleaner will drive among one or more areas within the field of view of the camera; The above first region corresponds to a portion of the first driving path along which the robot cleaner previously drove, The second region corresponds to a portion of the first driving path on which the robot cleaner did not drive, The step of controlling the above suction unit is: A step of identifying a cleaning target area within the field of view of the camera based on the identified amount of dust; If the cleaning target area is not located on the driving path, a step of setting a second driving path for the cleaning target area; and A non-transitory computer-readable storage medium, comprising: a step of controlling a driving unit to cause the robot cleaner to drive along the second driving path set above.

Images