Patent Application
20250213089
The present disclosure relates to a robot vacuum cleaner and a controlling method thereof. Specifically, the present disclosure relates to a robot vacuum cleaner capable of cleaning a carpet present on a floor surface, and a controlling method thereof.
Recently, the technology of robot vacuum cleaners that may automatically move along a driving path and clean a floor surface has been continuously developed. In particular, recently, the robot vacuum cleaners are being provided that may perform cleaning in an appropriate way depending on a type of the floor surface.
However, when a carpet exists on the floor surface, the carpet is a target of cleaning just like the floor surface, but it often has different characteristics from the floor surface. In addition, since the carpets also have very different characteristics, the appropriate cleaning method may vary depending on the characteristics of the carpets, and the driving path of the robot vacuum cleaner may also vary depending on the characteristics of the carpets.
For example, the appropriate suction force may vary depending on the type of carpet fur included in the carpet, and the path that the robot vacuum cleaner will take while cleaning the carpet may also vary depending on a thickness and shape of the carpet.
However, the robot vacuum cleaners according to the conventional technology have the limitation in that when the carpet exists on the floor surface, they only attempt to climb onto the carpet in various ways and perform cleaning in a limited way, and do not provide an appropriate driving path and cleaning method considering the various characteristics of the carpet.
The present disclosure provides a robot vacuum cleaner capable of providing a suitable driving path and cleaning method by taking into account various characteristics of a carpet, and a controlling method thereof.
According to an aspect of the present disclosure, a robot vacuum cleaner includes a driving unit, memory configured to store at least one instruction, and at least one processor configured to execute the at least one instruction, in which the processor may be configured to acquire information on a carpet located in a cleaning space, determine a path for the robot vacuum cleaner to clean the carpet based on the information on the carpet, determine, based on the information on the carpet, a first suction force for cleaning a first area corresponding to a center of the carpet and a second suction force for cleaning a second area of the carpet different from the first area, and control the driving unit to operate the robot vacuum cleaner based on the first suction force and the second suction force while moving along the path.
According to an aspect of the present disclosure, a controlling method of a robot vacuum cleaner includes acquiring information on a carpet located in a cleaning space; determining a path for the robot vacuum cleaner to clean the carpet based on the information on the carpet; determining, based on the information on the carpet, a first suction force for cleaning a first area corresponding to a center of the carpet and a second suction force for cleaning a second area different from the first area; and controlling the robot vacuum cleaner to operate based on the first suction force and the second suction force while the robot vacuum cleaner moves along the path.
According to an aspect of the present disclosure, a non-transitory computer readable medium storing one or more instructions, that when executed by at least one processor, cause the at least one processor to acquire information on a carpet located in a cleaning space, determine a path for the robot vacuum cleaner to clean the carpet based on the information on the carpet, determine, based on the information on the carpet, a first suction force for cleaning a first area corresponding to a center of the carpet and a second suction force for cleaning a second area of the carpet different from the first area, and control the driving unit to operate the robot vacuum cleaner based on the first suction force and the second suction force while moving along the path.
The above and other aspects, features and advantages of characteristic embodiments of the present disclosure will become more apparent from the following description in conjunction with the accompanying drawings:
Because the disclosure may be variously modified and have several embodiments, specific embodiments of the disclosure will be illustrated in the drawings and described in detail in a detailed description. However, it is to be understood that the disclosure is not limited to specific embodiments, but include various modifications, equivalents, and/or alternatives according to embodiments of the disclosure. Throughout the accompanying drawings, similar components will be denoted by similar reference numerals.
In describing the present disclosure, when it is decided that a detailed description for the known functions or configurations related to the present disclosure may unnecessarily obscure the gist of the present disclosure, the detailed description therefor will be omitted.
In addition, the following embodiments may be modified in several different forms, and the scope and spirit of the disclosure are not limited to the following embodiments. Rather, these embodiments make the disclosure thorough and complete, and are provided to completely transfer a technical spirit of the disclosure to those skilled in the art.
Terms used in the disclosure are used only to describe specific embodiments rather than limiting the scope of the disclosure. Singular forms include plural forms unless the context clearly indicates otherwise.
In the specification, an expression “have”, “may have”, “include”, “may include”, or the like, indicates existence of a corresponding feature (for example, a numerical value, a function, an operation, a component such as a part, or the like), and does not exclude existence of an additional feature.
In the disclosure, an expression “A or B”, “at least one of A and/or B”, or “one or more of A and/or B”, may include all possible combinations of items enumerated together. For example, “A or B”, “at least one of A and B”, or “at least one of A or B” may indicate all of 1) a case where at least one A is included, 2) a case where at least one B is included, or 3) a case where both of at least one A and at least one B are included.
Expressions “first” or “second” used in the disclosure may indicate various components regardless of a sequence and/or importance of the components, will be used only to distinguish one component from the other components, and do not limit the corresponding components.
When it is mentioned that any component (for example, a first component) is (operatively or communicatively) coupled with/to or is connected to another component (for example, a second component), it is to be understood that any component is directly coupled to another component or may be coupled to another component through the other component (for example, a third component).
On the other hand, when it is mentioned that any component (for example, a first component) is “directly coupled” or “directly connected” to another component (for example, a second component), it is to be understood that the other component (for example, a third component) is not present between any component and another component.
An expression “configured (or set) to” used in the disclosure may be replaced by an expression “suitable for”, “having the capacity to” “designed to”, “adapted to”, “made to”, or “capable of” depending on a situation. A term “configured (or set) to” may not necessarily mean “specifically designed to” in hardware.
Instead, in some situations, an expression “apparatus configured to” may mean that the apparatus may “do” together with other apparatuses or components. For example, a “processor configured (or set) to perform A, B, and C” may mean a dedicated processor (for example, an embedded processor) for performing the corresponding operations or a generic-purpose processor (for example, a central processing unit (CPU) or an application processor) that may perform the corresponding operations by executing one or more software programs stored in a memory apparatus.
In embodiments, a ‘module’ or a ‘˜er/or’ may perform at least one function or operation, and be implemented by hardware or software or be implemented by a combination of hardware and software. In addition, a plurality of “modules” or a plurality of “˜ers/ors” may be integrated in at least one module and be implemented by at least one processor except for a ‘module’ or an ‘˜er/or’ that needs to be implemented by specific hardware.
Meanwhile, various elements and regions in the drawings are schematically illustrated. Therefore, the spirit of the disclosure is not limited by relatively sizes or intervals illustrated in the accompanying drawings.
Hereinafter, embodiments of the disclosure will be described in detail with reference to the accompanying drawings so that those skilled in the art to which the disclosure pertains may easily practice the disclosure.
As illustrated in
The driving unit 110 may control the driving of the robot vacuum cleaner 100. Specifically, the driving unit 110 may include a plurality of wheels and at least one motor, and at least one motor may include a suction motor, a brush control motor, a wheel motor, etc. At least one motor included in the driving unit 110 may be implemented as various types of motors, such as a direct current (DC) motor, an alternative current (AC) motor, and a brushless DC (BLDC) motor.
The ‘suction motor’ refers to a motor capable of generating suction pressure. Specifically, when a control signal is received from the processor 130 and power is supplied from a power supply unit, an impeller may rotate by the driving of the suction motor. The rotation of the impeller generates the suction pressure, and air containing pollutants may be sucked into a suction port of the robot vacuum cleaner 100 by the suction pressure. Meanwhile, the suction pressure may increase as the speed of the suction motor increases. As described below, in the description of the present disclosure, the size of the suction pressure generated by the suction motor is referred to as ‘suction force’.
The ‘brush motor’ refers to a motor that may control the position and operation of at least one of a plurality of brushes included in the robot vacuum cleaner 100. For example, the processor 130 may control the brush motor to lower a position of a wet brush (i.e., a mop) among the plurality of brushes to perform cleaning of a floor surface, so the wet brush may come into contact with a floor surface. In addition, the processor 130 may control the brush motor to raise the position of the wet brush to prevent the cleaning of the floor surface using the wet brush, so the wet brush may not come into contact with the floor surface.
The ‘wheel motor’ may control the operation of the wheel included in the robot vacuum cleaner 100. Specifically, the wheel motor may control the direction of rotation and the speed of the wheel included in the robot vacuum cleaner 100, thereby controlling the movement direction and the speed of the robot vacuum cleaner 100. When the robot vacuum cleaner 100 includes two wheels, a left wheel and a right wheel, the wheel motor may include a left wheel motor and a right wheel motor, and the left wheel motor and the right wheel motor may control the rotation direction and the speed of the left wheel and the right wheel, respectively.
At least one instruction regarding the robot vacuum cleaner 100 may be stored in the memory 120. The memory 120 may store an operating system (O/S) for driving the robot vacuum cleaner 100. In addition, various software programs or applications for operating the robot vacuum cleaner 100 according to various embodiments of the present disclosure may be stored in the memory 120. In addition, the memory 120 may include a semiconductor memory such as a flash memory or the like, or a magnetic storing medium such as a hard disk or the like.
Specifically, various software modules for operating the robot vacuum cleaner 100 according to diverse embodiments of the present disclosure may be stored in the memory 120, and the processor 130 may run the various software modules stored in the memory 120 to control the operation of the robot vacuum cleaner 100. That is, the memory 120 may be accessed by the processor 130, and readout, recording, correction, deletion, update, and the like, of data in the memory 120 may be performed by the processor 130.
Meanwhile, in the disclosure, the term “memory 120” may be used as the meaning including the memory 120, a read only memory (ROM) in the processor 130, a random access memory (RAM), or a memory card (for example, a micro secure digital (SD) card or a memory stick) mounted in the robot vacuum cleaner 100.
According to one or more embodiments, the memory 120 may store information on a driving path of the robot vacuum cleaner 100, information on a carpet, information on a suction force for each path, information on a brush for each path, and the like. The information on the driving path stored in the memory 120 may be updated based on user input, type information on a type of floor surface, and the like, and the information on the carpet may be updated whenever it is acquired through at least one sensor 140.
In addition, various pieces of information necessary within the scope for achieving the object of the present disclosure may be stored in the memory 120, and the information stored in the memory 120 may be updated as received from an external device or input by a user.
The processor 130 controls the overall operation of the robot vacuum cleaner 100. Specifically, the processor 130 is connected to the configuration of the robot vacuum cleaner 100 including at least one sensor 140, the driving unit 110, and the memory 120, and may control the overall operation of the robot vacuum cleaner 100 by executing at least one instruction stored in the memory 120 as described above.
The processor 130 may be implemented in various manners. For example, the processor 130 may be implemented by at least one of an application specific integrated circuit (ASIC), an embedded processor, a microprocessor, a hardware control logic, a hardware finite state machine (FSM), or a digital signal processor (DSP). Meanwhile, in the present disclosure, the term processor 130 may be used as meaning including a central processing unit (CPU), a graphic processing unit (GPU), a micro processing unit (MPU), and the like.
According to one or more embodiments, the processor 130 may determine, based on the information on the carpet, a path for the robot vacuum cleaner 100 to clean the carpet, and may determine various methods for cleaning the carpet, including a suction force for cleaning the carpet, based on the information on the carpet. Hereinafter, various embodiments implemented by the processor 130 will be described.
The processor 130 may acquire the information on the carpet placed in a cleaning space. In the present disclosure, the term ‘carpet’ collectively refers to an object that is a target of cleaning, similar to a floor surface, among objects placed in the cleaning space (i.e., a floor surface) of the robot vacuum cleaner 100. In other words, the term ‘carpet’ includes all objects that may perform various functions, such as protecting a floor surface, decorating a space, and keeping a space warm, and may include objects commonly referred to as a carpet, objects referred to as a rug, a mat, a tile, etc.
The ‘Information on the carpet’ is used as a general term for all information on the carpet that may affect the cleaning of the carpet by the robot vacuum cleaner 100. Specifically, the information on the carpet may include at least one of information on a thickness of carpet, information on a size of carpet, information on a shape of carpet, and information on a type of carpet fur included in the carpet.
The ‘information on the thickness of the carpet’ may include an average thickness of the entire area of the carpet, a thickness of an edge of the carpet, etc. The ‘Information on the size of the carpet’ may include information on an area of the carpet, a perimeter length of the carpet, lengths of each side constituting the carpet, etc. The ‘information on the shape of the carpet’ may include information on whether the shape of the carpet is rectangular, circular, or oval, and may also include information on the detailed shape of each area of the carpet.
In addition, the ‘information on the type of carpet fur’ may specifically include information on the material, length, and surface characteristics of the carpet fur. For example, the information on the type of carpet fur may include various information such as information on whether the material of the carpet is nylon, polyester, or silk, information on whether the length of the carpet fur is long fur greater than or equal to a defined critical length or short fur less than the critical length, information on the strength of the carpet fur, the information on whether the surface of the carpet is waterproofed.
Specifically, the processor 130 may acquire the information on the carpet through at least one sensor 140, and acquire the information on the carpet by receiving the information on the carpet from an external device. The process of acquiring the information on the carpet through at least one sensor 140 or an external device is described in detail with reference to
The processor 130 may determine, based on the information on the carpet, a path for the robot vacuum cleaner 100 to clean the carpet. Here, the ‘path for cleaning the carpet’ may include a first path for the robot vacuum cleaner 100 to enter the carpet and a second path for the robot vacuum cleaner 100 to move on the carpet.
The ‘first path’ refers to a path for the robot vacuum cleaner 100 to enter the carpet, and determining the first path may include determining a position at which the robot vacuum cleaner 100 enters the carpet and determining a direction in which the robot vacuum cleaner 100 enters the carpet.
According to one or more embodiments, the processor 130 may determine the first path based on the information on the shape of the carpet included in the information on the carpet so that the robot vacuum cleaner 100 enters the widest surface among a plurality of surfaces of the carpet. For example, the processor 130 may identify that the shape of the carpet is a rectangle based on the information on the shape of the carpet. When the shape of the carpet is a rectangle, the processor 130 may determine a midpoint of the longest side of the rectangle as an entry position of the robot vacuum cleaner 100.
According to one or more embodiments, the processor 130 may determine the first path based on information on the carpet so that at least one of the plurality of wheels comes into contact with the carpet before at least one brush 150 while the robot vacuum cleaner 100 enters the carpet. For example, when the plurality of wheels are composed of a left wheel and a right wheel, the processor 130 may determine an entry direction of the robot cleaning as a diagonal direction so that the left wheel or the right wheel comes into contact with the carpet before at least one brush 150 when the robot vacuum cleaner 100 enters the carpet.
As described above, the embodiment of determining the entry position of the robot vacuum cleaner 100 and the embodiment of determining the entry direction of the robot vacuum cleaner 100 are described in more detail with reference to
The ‘second path’ refers to a path for the robot vacuum cleaner 100 to move on the carpet, and determining the second path may include determining a position at which the robot vacuum cleaner 100 starts cleaning after entering the carpet, determining a path for cleaning the entire area of the carpet, and determining a position at which the robot vacuum cleaner 100 leaves the carpet after finishing cleaning the carpet.
In addition, the second path may include a movement path between a first area corresponding to the center of the carpet and a second area different from the first area. Specifically, the ‘first area’ refers to an area corresponding to the center of the carpet, that is, an area including the center or center of gravity of the carpet, and the size of the first area may vary depending on the embodiment. The ‘second area’ is an area different from the first area and may include the edge of the carpet.
According to one or more embodiments, the processor 130 may determine the second path so that the robot vacuum cleaner 100 moves in a spiral shape from the first area to the second area. Specifically, the processor 130 may determine, based on the information on the carpet, the second path so that the robot vacuum cleaner 100 moves in the spiral shape from the first area to the second area.
For example, the processor 130 may identify that the shape of the carpet is circular based on the information on the carpet. When the shape of the carpet is circular, the processor 130 may determine the second path so that the robot vacuum cleaner 100 starts from the center of the carpet corresponding to the center of the circle and moves to the edge corresponding to the circumference of the circle of the carpet while drawing the spiral shape. The operation of the robot vacuum cleaner 100 moving in the spiral shape will be described in more detail with reference to
Meanwhile, the embodiment in which the second path is determined so that the robot vacuum cleaner 100 moves in the spiral shape from the first area to the second area has been described above, but conversely, the processor 130 may determine the second path so that the robot vacuum cleaner 100 moves in the spiral shape from the second area to the first area.
According to one or more embodiments, the processor 130 may determine the second path so that the robot vacuum cleaner 100 reciprocally or cyclically moves from the first area to each of a plurality of points included in the second area and back. For example, the processor 130 may determine the second path so that the robot vacuum cleaner 100 moves from the first area to a first point included in the second area, then back to the center, and then to a second point in the second area. As another example, the processor 130 may determine the second path so that the robot vacuum cleaner 100 moves from the first area to a first point in the second area and then to a second point in the second area. Specifically, the processor 130 may determine, based on the information on the carpet, the second path so that the robot vacuum cleaner 100 reciprocally or cyclically moves from the first area to each of the plurality of points included in the second area.
For example, the processor 130 may identify that the shape of the carpet is rectangular based on the information on the carpet. When the shape of the carpet is rectangular, the processor 130 may determine the second path to pass through the entire area of the carpet by repeating the operation of the robot vacuum cleaner 100 moving from the center of the carpet corresponding to the center of the rectangle to a first point at the edge of the carpet and then returning from the first point to the center of the carpet and moving from the center of the carpet to a second point at the edge of the carpet and then returning from the second point to the center of the carpet. The operation of the robot vacuum cleaner 100 repeatedly passing through the center of the carpet will be described in more detail with reference to
Meanwhile, the embodiment in which the robot vacuum cleaner 100 determines the path for cleaning the carpet on the premise that the robot vacuum cleaner 100 decides to clean the carpet has been described above, but the processor 130 may determine, based on the information on the carpet, that the robot vacuum cleaner 100 should not enter the carpet and may determine the driving path of the robot vacuum cleaner 100 to bypass the carpet.
Specifically, the processor 130 may identify the thickness of the carpet based on the information on the thickness of the carpet. When the identified thickness of the carpet is greater than or equal to a critical thickness, the processor 130 may determine that the robot vacuum cleaner 100 does not enter the carpet and determine the driving path of the robot vacuum cleaner 100 to bypass the carpet. On the other hand, when the identified thickness of the carpet is less than the critical thickness, the processor 130 may determine that the robot vacuum cleaner 100 enters the carpet and determine the path for the robot vacuum cleaner 100 to clean the carpet according to various embodiments as described above.
Meanwhile, the above description describes a process in which the processor 130 determines the path for the robot vacuum cleaner 100 to clean the carpet based on the information on the carpet. However, the processor 130 may determine the suction force for the robot vacuum cleaner 100 to clean the carpet based on the information on the carpet. That is, the processor 130 may determine the suction force for the robot vacuum cleaner 100 to clean the carpet based on at least one of the information on the thickness of the carpet, the information on the size of the carpet, the information on the shape of the carpet, and the information on the type of carpet fur included in the carpet.
For example, the processor 130 may determine the suction force for the robot vacuum cleaner 100 to clean the carpet based on the fact that when the size of the carpet is small, the force resisting the carpet from being rolled into the suction port of the robot vacuum cleaner 100 may be stronger than when the size of the carpet is large, and when the thickness of the carpet is thick, the force resisting the carpet from being rolled into the suction port of the robot vacuum cleaner 100 may be stronger than when the thickness of the carpet is thin.
As another example, the processor 130 may determine the suction force for the robot vacuum cleaner 100 to clean the carpet based on the fact that the shorter the length of the carpet fur, the stronger the force resisting the carpet from being rolled into the suction port of the robot vacuum cleaner 100, the stronger the strength of the material of the carpet fur, the stronger the force resisting the carpet from being rolled into the suction port of the robot vacuum cleaner 100 compared to the case where the strength of the material of the carpet fur is low, etc. Specifically, the processor 130 may determine the initial suction force for the robot vacuum cleaner 100 to clean the carpet according to the following Equation, and may update the information on the suction force while gradually increasing the suction force while performing the cleaning based on the determined suction force. However, the process of calculating the suction force is not limited to the following Equation.
Here, k is a constant, the size is the size of the carpet identified based on the information on the carpet, the thickness is the thickness of the carpet identified based on the information on the carpet, and w1 and w2 are weights calculated based on the type of carpet fur (e.g., length of carpet fur, strength according to material, etc.).
In particular, the processor 130 may determine different suction forces for each area of the carpet. Specifically, the processor 130 may differently determine, based on the information on the carpet, a first suction force for cleaning the first area corresponding to the center of the carpet and a second suction force for cleaning the second area different from the first area.
Here, the ‘first suction force’ refers to the suction force of the robot vacuum cleaner 100 for cleaning the first area corresponding to the center of the carpet, and may be determined according to the speed of the suction motor and the operation time of the suction motor, etc. The first suction force is not necessarily fixed to one value, and may be changed while performing the cleaning of the first area.
The ‘second suction force’ refers to the suction force of the robot vacuum cleaner 100 for cleaning the second area, which is different from the first area, and, like the first suction force, may be determined based on the speed of the suction motor, the operation time of the suction motor, etc. Like the first suction force, the second suction force is not necessarily fixed to one value and may be changed while cleaning the second area. In particular, the first suction force may be greater than the second suction force. In other words, when the robot vacuum cleaner 100 moves from the first area to the second area, the processor 130 may control the suction motor to perform the cleaning by reducing the suction force of the robot vacuum cleaner 100.
For example, since fibers included in the center of the carpet are coupled with surrounding fibers in a 360° direction, the force resisting the fibers from being rolled into the suction port of the robot vacuum cleaner 100 may be strong, while since fibers included in the edge area of the carpet may be coupled with the smaller number of surrounding fibers than the center area, the force resisting the fibers from being rolled into the suction port of the robot vacuum cleaner 100 may be relatively weak depending on the suction of the robot vacuum cleaner 100. Accordingly, the processor 130 may control the suction motor so that the robot vacuum cleaner 100 cleans the carpet with a weaker suction force when cleaning the second area than when cleaning the first area.
As another example, when lengths of the fibers included in the edge area of the carpet are longer than lengths of the fibers included in the center area of the carpet, the fibers included in the edge area of the carpet may be more likely to be rolled into the suction port of the robot vacuum cleaner 100 than the fibers included in the center area of the robot depending on the suction of the robot vacuum cleaner 100. Accordingly, the processor 130 may control the suction motor so that the robot vacuum cleaner 100 cleans the carpet with a smaller suction force when cleaning the second area than when cleaning the first area.
According to one or more embodiments, when the second path is determined so that the robot vacuum cleaner 100 moves in the spiral shape from the first area to the second area, the processor 130 may gradually reduce the suction force or reduce the suction force at a predetermined rate while the robot vacuum cleaner 100 moves in the spiral shape from the first area to the second area. According to one or more embodiments, when the second path is determined so that the robot vacuum cleaner 100 reciprocally or cyclically moves from the first area to each of the plurality of points included in the second area, the processor 130 may control the driving unit 110 to operate according to the first suction force while the robot vacuum cleaner 100 moves from the first area to each of the plurality of points along the second path, and may control the driving unit 110 to operate according to the second suction force while the robot vacuum cleaner 100 moves from each of the plurality of points to the first area along the second path. Meanwhile, the embodiment in which the first suction force is controlled to be greater than the second suction force has been described above, but the present disclosure is not limited thereto. That is, the processor 130 may control the first suction force to be less than the second suction force according to the thickness of the carpet, the size of the carpet, the shape of the carpet, and what the type of carpet fur included in the carpet is that are identified based on the information on the carpet. For example, when a stitching is formed in the edge area of the carpet to improve the durability of the carpet by preventing the fibers of the carpet from being loosened, the fibers included in the edge area of the carpet may have a relatively stronger resistance to being rolled into the suction port of the robot vacuum cleaner 100 than the fibers included in the center area of the robot vacuum cleaner 100. Accordingly, the processor 130 may control the suction motor so that the robot vacuum cleaner 100 cleans the carpet with a stronger suction force when cleaning the second area than when cleaning the first area.
As another example, when lengths of the fibers included in the edge area of the carpet are shorter than lengths of the fibers included in the center area of the carpet, the fibers included in the edge area of the carpet may be less likely to be rolled into the suction port of the robot vacuum cleaner 100 than the fibers included in the center area of the robot. Accordingly, the processor 130 may control the suction motor so that the robot vacuum cleaner 100 cleans the carpet with a stronger suction force when cleaning the second area than when cleaning the first area.
Meanwhile, the processor 130 inputs at least one of the information on the carpet, that is, the information on the thickness of the carpet, the information on the size of the carpet, the information on the shape of the carpet, and the information on the type of carpet fur included in the carpet, to a neural network model trained to produce the suction force optimized for the information on the carpet, so the robot vacuum cleaner 100 may determine the suction force for cleaning the carpet.
In addition, when the information on the carpet is divided for each area of the carpet and the neural network model is trained to produce the optimal suction force for each area of the carpet, the processor 130 inputs the information on the carpet to the neural network model, so the first suction force for cleaning the first area corresponding to the center of the carpet and the second suction force for cleaning the second area different from the first area may be differently determined.
In the above, embodiments related to controlling the suction force differently for each area of the carpet have been described, but it should be understood that the present disclosure is not limited to the above-described embodiments and examples.
Meanwhile, then embodiment of differently describing the suction force for each area of the carpet has been described above, but the processor 130 may also determine a brush for cleaning the carpet among a plurality of brushes included in the robot vacuum cleaner 100 based on the information on the carpet. The embodiment of selecting the optimal brush will be described in more detail with reference to
The processor 130 may control the driving unit 110 to operate according to the determined first suction force and second suction force while the robot vacuum cleaner 100 moves along the determined path. Specifically, the processor 130 may control the wheel motor so that the robot vacuum cleaner 100 moves along the first path and the second path, and may control the suction motor so that the robot vacuum cleaner 100 has the first suction force while moving through the first area, and may control the suction motor so that the robot vacuum cleaner 100 has the second suction force while moving through the second area.
Meanwhile, the processor 130 may change at least one of the first path, the second path, the first suction force, and the second suction force based on the information on the carpet acquired through at least one sensor 140 while operating based on the first path, the second path, the first suction force, and the second suction force determined according to various embodiments as described above. One or more embodiments related thereto are described above with reference to
Meanwhile, the processor 130 may not only adjust the suction force differently depending on the area of the carpet, but may also adjust whether or not to suction differently. For example, the processor 130 may stop or pause the operation of the suction motor included in the driving unit 110 while the robot vacuum cleaner 100 enters the carpet along the first path, and may resume or restart the operation of the suction motor when entering the carpet is completed. For example, in an embodiment, the processor 130 may stop or pause the operation of the suction motor included in the driving unit 110 during the time the robot vacuum cleaner 100 enters the carpet along the first path, and resume the operation of the suction motor once the robot vacuum cleaner 100 is firmly on the carpet.
The processor 130 may determine whether to enter the carpet and the entry speed into the carpet based on the information on the carpet. For example, when the thickness of the carpet is greater than or equal to a critical thickness, the processor 130 may determine the driving path of the robot vacuum cleaner 100 to bypass the carpet. In addition, when the thickness of the carpet is greater than or equal to the critical thickness, the processor 130 may increase the moving speed of the carpet to smoothly enter the carpet. According to one or more of the embodiments described above with reference to
As described above, the processor 130 may determine a position where the robot vacuum cleaner 100 enters the carpet based on the information on the carpet. Here, the ‘position where the robot vacuum cleaner 100 enters the carpet’ may mean an intersection point where the first path, which is the path for the robot vacuum cleaner 100 to enter the carpet, and the edge of the carpet meet.
According to one or more embodiments, the processor 130 may determine the first path based on the information on the shape of the carpet so that the robot vacuum cleaner 100 enters the widest surface among a plurality of surfaces of the carpet. Referring to the example of
According to one or more embodiments, the processor 130 may determine the first path so that the robot vacuum cleaner 100 enters the carpet at a point with the lowest curvature based on the information on the shape of the carpet.
Referring to the example of
Meanwhile, the processor 130 may not only determine the position at which the robot vacuum cleaner 100 enters the carpet based on the information on the carpet, but may also determine the direction in which the robot vacuum cleaner 100 enters the carpet based on the information on the carpet. Here, the ‘direction in which the robot vacuum cleaner 100 enters the carpet’ may mean the direction in which the front of the robot vacuum cleaner 100 faces when the robot vacuum cleaner 100 enters the carpet. According to one or more embodiments, the processor 130 may determine the first path based on information on the carpet so that at least one of the plurality of wheels comes into contact with the carpet before at least one brush 150 while the robot vacuum cleaner 100 enters the carpet. Specifically, when the plurality of wheels are composed of a left wheel and a right wheel, the processor 130 may determine an entry direction of the robot cleaning as a diagonal direction so that the left wheel or the right wheel comes into contact with the carpet before at least one brush 150 when the robot vacuum cleaner 100 enters the carpet.
The ‘entry direction of the robot vacuum cleaner 100 is diagonal’ means a direction that, when the shape of the carpet is rectangular as in the example of
The ‘entry direction of the robot vacuum cleaner 100 is diagonal’ means a direction that, when the shape of the carpet is rectangular as in the example of
According to one or more of the embodiments described above with reference to
In particular, when the robot vacuum cleaner 100 enters a wide surface of the carpet or a point with low curvature as in the examples of
However, only a part of the second path is illustrated in
Referring to the example of
Referring to the example of
In other words, the robot vacuum cleaner 100 may clean the carpet while drawing the spiral shape from the center of the carpet to the edge, but drawing the spiral shape in a shape corresponding to the shape of the carpet.
Meanwhile, the embodiment in which the second path is determined so that the robot vacuum cleaner 100 moves in the spiral shape from the first area to the second area has been described above, but conversely, the processor 130 may determine the second path so that the robot vacuum cleaner 100 moves in the spiral shape from the second area to the first area.
In particular, when the robot vacuum cleaner 100 performs the cleaning while moving on a carpet in a spiral path as in the examples of
According to one or more embodiments, the processor 130 may determine, based on the information on the carpet, the second path to move along a plurality of continuous straight paths from the first area to the second area.
In other words, the robot vacuum cleaner 100 moves from the center of the carpet to the edge, but may clean the carpet by moving along the paths 610 and 710 that include a plurality of continuous straight lines as illustrating in
According to one or more embodiments, the processor 130 may determine, based on the information on the carpet, the second path so that the robot vacuum cleaner 100 reciprocally or cyclically moves from the first area to each of a plurality of points included in the second area.
When the second path is determined so that the robot vacuum cleaner 100 reciprocally or cyclically moves from the first area to each of the plurality of points included in the second area, the processor 130 may control the driving unit 110 to operate according to the first suction force while the robot vacuum cleaner 100 moves from the first area to each of the plurality of points along the second path, and may control the driving unit 110 to operate according to the second suction force while the robot vacuum cleaner 100 moves from each of the plurality of points to the first area along the second path.
Referring to the example of
Referring to the example of
In
In particular, when the robot vacuum cleaner 100 performs the cleaning while moving along the plurality of discontinuous straight paths on the carpet as in the examples of
As illustrated in
At least one sensor 140 may detect various information inside and outside the robot vacuum cleaner 100. Specifically, at least one sensor 140 may include an image sensor and an object detection sensor.
The term ‘image sensor’ is a general term for a sensor that detects light and converts the detected light into an image. That is, in the present disclosure, the term image sensor is used as meaning including a camera including an image sensor and a vision sensor performing image processing based on the image sensor.
The term ‘object detection sensor’ is a general term for sensors that may recognize the presence of an object placed in the surrounding environment of the robot vacuum cleaner 100 and the characteristics of the object. Specifically, the object detection sensor may include various types of sensors that may acquire information on an object by emitting light and receiving light reflected by the object. For example, the object detection sensor may include a light detection and ranging (LiDAR) sensor, a time of flight (ToF) sensor, an ultrasonic sensor, an infrared sensor, etc. The object detection sensor may acquire various information such as a distance between the robot vacuum cleaner 100 and the object, a size of the object, a shape of the object, a color of the object, etc.
According to one or more embodiments, the processor 130 may acquire the image of the carpet through the image sensor. The processor 130 may acquire the information on the size, shape, thickness, etc., of the carpet by analyzing the image of the carpet. For example, the processor 130 may acquire the information on the carpet by inputting the image of the carpet to the trained neural network model.
According to one or more embodiments, the processor 130 may acquire the information on the size, shape, color, etc., of the carpet through the object detection sensor, and identify the characteristics and type of the carpet based on the information on the size, shape, color, etc., of the carpet.
According to one or more embodiments, the processor 130 may identify an obstacle existing on the first path or the second path while the robot vacuum cleaner 100 moves along the first path or the second path, and determine the first path or the second path to bypass the identified obstacle.
According to one or more embodiments, the processor 130 may acquire the information on the change in the shape of the carpet through at least one sensor 140 while the robot vacuum cleaner 100 moves along the second path. Additionally, the processor 130 may adjust at least one of the first suction force and the second suction force based on the information on the change in the shape of the carpet.
For example, while the robot vacuum cleaner 100 is performing the cleaning while moving along the second path, when it is identified that a part of the carpet is being rolled into or has been rolled into the suction port of the robot vacuum cleaner 100, the processor 130 may control the suction motor to reduce at least one of the first suction force and the second suction force, or may stop or pause the suction motor to make at least one of the first suction force and the second suction force zero (0).
At least one brush 150 may sweep away pollutants placed on the floor surface so that the suction unit of the robot vacuum cleaner 100 may easily capture the pollutants. Specifically, at least one brush 150 may include a dry brush and a wet brush.
The ‘dry brush’ refers to a brush that may clean the floor surface without being wetted with water. Specifically, the dry brush may sweep away pollutants such as dust placed on the floor surface so that the suction unit of the robot vacuum cleaner 100 may easily capture the pollutants. The operation mode of performing the cleaning using the dry brush may be referred to as a ‘dry mode’.
The ‘wet brush’ refers to a brush that may clean the floor surface with water while being wetted, and may be referred to by terms such as a ‘mop’ or a ‘water mop’. For example, the wet brush may be supplied with water from a user or a water tank, detach pollutants adsorbed on the floor surface while being wetted with water, and sweep away the detached pollutants, so the suction unit of the robot vacuum cleaner 100 may easily capture the detached pollutants. The operation mode of performing the cleaning using the wet brush may be referred to as a ‘dry mode’.
When the robot vacuum cleaner 100 includes both the dry brush and the wet brush and is implemented so that the position of the wet brush may be controlled, the robot vacuum cleaner 100 may perform the cleaning without using the wet brush when operating in the dry mode, and perform the water cleaning using the wet brush by automatically controlling the position of the wet brush when operating in the wet mode.
According to one or more embodiments, the processor 130 may determine, based on the information on the carpet, one of at least one brush 150 to clean the carpet while the robot vacuum cleaner 100 moves along the second path. The communication unit 160 includes a circuit and may perform communication with an external device. Specifically, the processor 130 may receive various data or information from an external device connected through the communication unit 160, and may transmit various data or information to the external device.
The communication unit 160 may include at least one of a WiFi module, a Bluetooth module, a wireless communication module, an NFC module, and an ultra-wide band (UWB) module. Specifically, the WiFi module and the Bluetooth module may each perform communication using WiFi and Bluetooth methods. In the case of using the Wi-Fi module or the Bluetooth module, various connection information such as SSID, is first transmitted and received, communication is connected using the connection information, and various information may then be transmitted and received.
In addition, the wireless communication module may perform communication depending on various communication protocols such as Institute of Electrical and Electronics Engineers (IEEE), Zigbee, 3rd generation (3G), 3rd generation partnership project (3GPP), long term evolution (LTE), and 5th generation (5G). The NFC module may perform communication in a near field communication (NFC) manner using a band of 13.56 MHz among various radio frequency identification (RFID) frequency bands such as 135 kHz, 13.56 MHz, 433 MHz, 860 to 960 MHz, and 2.45 GHz. In addition, the UWB module may accurately measure time of arrival (ToA), which is the time for a pulse to reach a target, and angle of arrival (AoA), which is an angle of arrival of a pulse at the transmitting device, through communication between UWB antennas, so precise distance and position recognition is possible indoors within an error range of several tens of centimeters.
According to one or more embodiments, the processor 130 may acquire an image of a carpet through an image sensor included in at least one sensor 140. The processor 130 may control the communication unit 160 to transmit the image of the carpet to a server that performs a search for the image. The processor 130 may acquire the information on the carpet by acquiring the information on the image from the server.
For example, the processor 130 may control the communication unit 160 to transmit the image of the carpet to a server providing a search engine, and may receive information on the manufacturer, origin, identification number, material, color, size, etc., of the carpet corresponding to the image from the server.
In addition, the processor 130 may receive various information, such as the information on the driving path of the robot vacuum cleaner 100 and the information on the carpet, from the external device through the communication unit 160.
The input unit 170 includes a circuit, and the processor 130 may receive a user command for controlling the operation of the robot vacuum cleaner 100 through the input unit 170. Specifically, the input unit 170 may be configured to include components such as a microphone, a camera, and a remote control signal receiving unit, etc. The input unit 170 is a touch screen, and may be implemented as the form included in the display. In particular, the microphone may receive voice signals and convert the received voice signals into electrical signals.
According to one or more embodiments, the processor 130 may receive a user input for starting cleaning via the input unit 170, a user input for stopping or pausing cleaning, a user input for ending cleaning, a user input for setting the driving path of the robot vacuum cleaner 100, a user input for controlling the suction force, etc.
The output unit 180 may include a circuit, and the processor 130 may output various functions that may be performed by the robot vacuum cleaner 100 through the output unit 180. In addition, the output unit 180 may include at least one of a display, a speaker, and an indicator.
The display may output video data under the control of the processor 130. Specifically, the display may output videos pre-stored in the memory 120 under the control of the processor 130. In particular, the display according to one or more embodiments of the present disclosure may display a user interface stored in the memory 120. The display may be implemented as a liquid crystal display panel (LCD), organic light emitting diodes (OLED), etc., and in some cases, the display may also be implemented as a flexible display, a transparent display, etc. However, the display according to the present disclosure is not limited to a specific type.
The speaker may output audio data under the control of the processor 130. The indicator may be turned on under the control of the processor 130. Specifically, the indicator may be turned on in various colors under the control of the processor 130. For example, the indicator may be implemented as light emitting diodes (LEDs), a liquid crystal display panel (LCD), a vacuum fluorescent display (VFD), etc., but is not limited thereto.
According to one or more embodiments, the processor 130 may control the output unit 180 to output at least one of the information on the driving path of the robot vacuum cleaner 100, the information on the carpet, the information on the suction force for each path, and the information on the brush for each path. In this case, the user may input the user input for setting the driving path of the robot vacuum cleaner 100 or the user input for adjusting the suction force, and accordingly, the processor 130 may update the driving path of the robot vacuum cleaner 100 or adjust the suction force.
As described above, the processor 130 may acquire the information on the carpet through at least one sensor 140, and determine a path for the robot vacuum cleaner 100 to clean the carpet based on the information on the carpet.
In particular, the processor 130 may acquire the information on the carpet in different ways according to the size of the carpet. Specifically, the processor 130 may identify the size of the carpet based on the information on the carpet. When the size of the carpet is identified as being greater than a critical size, the processor 130 may control the driving unit 110 to move around the carpet. In addition, the processor 130 may update the information on the carpet through at least one sensor 140 while the robot vacuum cleaner 100 moves around the carpet.
For example, the processor may control the driving unit 110 to cause the robot vacuum cleaner 100 to collect the information on the carpet while circulating around the carpet outside the carpet as in a path 1110 illustrated in
In other words, when the size of the carpet is large enough to be greater than the critical size, the processor 130 may control the driving unit 110 to cause the robot vacuum cleaner 100 to acquire a larger amount of information (e.g., a larger amount of images of the carpet) on the carpet while moving around the carpet.
In addition, the processor 130 may acquire the information on the distance that the robot vacuum cleaner 100 has moved around the carpet while moving around the carpet, and may acquire the information on the size of the carpet based on at least one of the updated information on the carpet and the information on the distance.
In other words, the processor 130 may acquire the information on the size of the carpet not only by using the information on the carpet acquired while the robot vacuum cleaner 100 is moving around the carpet, but also by using the distance of the path that the robot vacuum cleaner 100 has moved around the carpet.
The robot vacuum cleaner 100 may acquire the information on the carpet placed in a cleaning space (S1310). Specifically, the robot vacuum cleaner 100 may acquire the information on the carpet through at least one sensor 140 included in the robot vacuum cleaner 100, and may acquire the information on the carpet by receiving the information on the carpet from an external device including at least one sensor 140. The robot vacuum cleaner 100 may determine the path for the robot vacuum cleaner 100 to clean the carpet based on the information on the carpet (S1320). Specifically, the robot vacuum cleaner 100 may determine the first path for the robot vacuum cleaner 100 to enter the carpet and the second path for the robot vacuum cleaner 100 to move on the carpet based on at least one of the information on the thickness of the carpet, the information on the size of the carpet, the information on the shape of the carpet, and the information on the type of carpet fur included in the carpet.
The robot vacuum cleaner 100 may differently determine, based on the information on the carpet, a first suction force for cleaning the first area corresponding to the center of the carpet and a second suction force for cleaning the second area different from the first area (S1330). The robot vacuum cleaner 100 may control the robot vacuum cleaner 100 to operate according to the determined first suction force and second suction force while the robot vacuum cleaner 100 moves along the determined path (S1340).
In particular, the first suction force may be greater than the second suction force. In other words, the robot vacuum cleaner 100 may perform the cleaning by reducing the suction force of the robot vacuum cleaner 100 when the robot vacuum cleaner 100 moves from the first area to the second area.
Meanwhile, the controlling method of the robot vacuum cleaner 100 according to the above-described embodiment may be implemented as a program and provided to the robot vacuum cleaner 100. In particular, a program including the controlling method of the robot vacuum cleaner 100 may be provided by being stored in a non-transitory computer readable medium.
Specifically, in a non-transitory computer-readable recording medium including a program for executing a controlling method of a robot vacuum cleaner 100, the controlling method of a robot vacuum cleaner 100 may include: acquiring information on a carpet placed in a cleaning space, determining a path for the robot vacuum cleaner 100 to clean the carpet based on the information on the carpet, differently determining, based on the information on the carpet, a first suction force for cleaning a first area corresponding to a center of the carpet and a second suction force for cleaning a second area different from the first area, and controlling the robot vacuum cleaner 100 to operate according to the determined first suction force and second suction force while the robot vacuum cleaner 100 moves along the determined path.
In the above description, the controlling method of the robot vacuum cleaner 100 and the computer-readable recording medium including the program for executing the controlling method of the robot vacuum cleaner 100 have been briefly described, but this is only for omitting redundant description, and it goes without saying that various embodiments of the robot vacuum cleaner 100 are also applicable to the computer-readable recording medium including the controlling method of the robot vacuum cleaner 100 and the program for executing the controlling method of the robot vacuum cleaner 100.
A function related to artificial intelligence according to the present disclosure is operated through the processor 130 and the memory 120 of the robot vacuum cleaner 100. The processor 130 may include one or a plurality of processors 130. In this case, one or more processors 130 may include at least one of a central processing unit (CPU), a graphics processing unit (GPU), and a neural processing unit (NPU), but are not limited to the examples of the processors 130 described above.
The CPU is a general-purpose processor 130 that may perform not only general operations but also artificial intelligence operations, and may efficiently execute complex programs through a multi-layer cache structure. The CPU is advantageous for a serial processing method, which allows organic connection between previous and next operation results through sequential operations. The general-purpose processor 130 is not limited to the above-described examples, except where specified as the above-described CPU.
The GPU is a processor 130 for large-scale operations such as floating-point operations used in graphics processing, and may perform the large-scale operations in parallel by integrating a large number of cores. In particular, the GPU may be more advantageous than the CPU in a parallel processing method such as a convolution operation. In addition, the GPU may be used as a co-processor 130 to supplement the functions of the CPU. The processor 130 for the large-scale operation is not limited to the above-described example, except for the case specified as the above-described GPU.
The NPU is a processor 130 specialized in the artificial intelligence operations using the artificial neural network, and each layer that constitutes the artificial neural network may be implemented in hardware (e.g., silicon). In this case, the NPU is specifically designed according to the company's requirements, so it has a lower degree of freedom than the CPU or GPU, but may efficiently process the artificial intelligence operations requested by the company. Meanwhile, as the processor 130 specialized for the artificial intelligence operations, the NPU may be implemented in various forms such as a tensor processing unit (TPU), an intelligence processing unit (IPU), and a vision processing unit (VPU). The artificial intelligence processor 130 is not limited to the examples described above, except where specified as the NPU described above.
In addition, one or more processors 130 may be implemented as a System on Chip (SoC). In this case, in addition to one or more processors 130, the SoC may further include a memory 120 and a network interface such as a bus for data communication between the processor 130 and the memory 120.
When the System on Chip (SoC) included in the robot vacuum cleaner 100 includes the plurality of processors 130, the robot vacuum cleaner 100 may use some of the plurality of processors 130 to perform the artificial intelligence-related operations (e.g., artificial intelligence operations related to model learning or inference). For example, the robot vacuum cleaner 100 may perform the artificial intelligence-related operations using at least one of the GPU, NPU, VPU, TPU, or hardware accelerator specialized for the artificial intelligence operations, such as the convolution operation and the matrix multiplication operation, among the plurality of processors 130. However, this is only an example, and it goes without saying that the artificial intelligence-related operations may be processed using the general-purpose processors 130 such as the CPU.
In addition, the robot vacuum cleaner 100 may perform the operations on the functions related to the artificial intelligence using multi cores (e.g., dual core, quad core, etc.) included in one processor 130. In particular, the robot vacuum cleaner 100 may perform the artificial intelligence operations, such as the convolution operation and the matrix multiplication operation, in parallel using the multi-cores included in the processor 130.
One or more processors 130 perform control to process input data according to a predefined operation rule or artificial intelligence model stored in the memory 120. The predefined operation rule or the AI model is characterized by being made through training.
Here, being created through learning means that a predefined motion rule or an artificial intelligence model of a desired characteristic is created by applying a learning algorithm to a plurality of learning data. Such training may be made in the device itself in which the AI according to the present disclosure is performed, or may be made through a separate server/system.
The AI model may include a plurality of neural network layers. At least one layer has at least one weight value, and a calculation of the layers is performed based on a calculation result of a previous layer and at least one defined calculation. Examples of neural networks may include models such as a convolutional neural network (CNN), a deep neural network (DNN), a recurrent neural network (RNN), a restricted Boltzmann machine (RBM), a deep belief network (DBN), a bidirectional recurrent deep neural network (BRDNN), deep Q-networks, and a transformer, and the neural networks in the present disclosure are not limited to the above-described examples except for the case specified.
A learning algorithm is a method of training a predetermined target device (e.g., a robot) using a large number of training data so that the predetermined target device may make decisions or make predictions on its own. Examples of the learning algorithms include supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning, but are not limited to the above examples, and the learning algorithm in the present disclosure is not limited to the examples described above except where explicitly stated.
The machine-readable storage medium may be provided in a form of a non-transitory storage medium. Here, the “non-transitory storage medium” means that the storage medium is a tangible device, and does not include a signal (for example, electromagnetic waves), and the term does not distinguish between the case where data is stored semi-permanently on a storage medium and the case where data is temporarily stored thereon. For example, the “non-transitory storage medium” may include a buffer in which data is temporarily stored.
According to an embodiment, the methods according to various embodiments disclosed in the present document may be included and provided in a computer program product. The computer program product may be traded as a product between a seller and a purchaser. The computer program product may be distributed in the form of a machine-readable storage medium (for example, compact disc read only memory (CD-ROM)), or may be distributed (for example, download or upload) through an application store (for example, Play Store™) or may be directly distributed (for example, download or upload) between two user devices (for example, smart phones) online. In a case of the online distribution, at least some of the computer program products (for example, downloadable app) may be at least temporarily stored in a machine-readable storage medium such as the memory 120 of a server of a manufacturer, a server of an application store, or a relay server, or may be temporarily generated.
Each of components (for example, modules or programs) according to the diverse embodiments of the disclosure as described above may include a single entity or a plurality of entities, and some of the corresponding sub-components described above may be omitted or other sub-components may be further included in the diverse embodiments. Alternatively or additionally, some of the components (e.g., the modules or the programs) may be integrated into one entity, and may perform functions performed by the respective corresponding components before being integrated in the same or similar manner.
Operations performed by the modules, the programs, or other components according to the diverse embodiments may be executed in a sequential manner, a parallel manner, an iterative manner, or a heuristic manner, at least some of the operations may be performed in a different order or be omitted, or other operations may be added.
Meanwhile, the term “unit” or “module” used in the disclosure may include units configured by hardware, software, or firmware, and may be used compatibly with terms such as, for example, logics, logic blocks, components, circuits, or the like. The term “˜er/or” or “module” may be an integrally configured component or a minimum unit performing one or more functions or a part thereof. For example, the module may be configured by an application-specific integrated circuit (ASIC).
The diverse embodiments of the disclosure may be implemented by software including instructions stored in a machine-readable storage medium (for example, a computer-readable storage medium). A machine may be a device that invokes the stored instruction from the storage medium and may be operated depending on the invoked instruction, and may include the electronic device (for example, robot vacuum cleaner 100) according to the disclosed embodiments.
In a case where a command is executed by the processor, the processor may directly perform a function corresponding to the command or other components may perform the function corresponding to the command under a control of the processor. The command may include codes created or executed by a compiler or an interpreter.
Hereinafter, although exemplary embodiments of the present disclosure have been illustrated and described, the present disclosure is not limited to the above-described specific exemplary embodiments, but may be variously modified by those skilled in the art to which the present disclosure pertains without departing from the gist of the present disclosure as disclosed in the accompanying claims. These modifications should also be understood to fall within the scope and spirit of the present disclosure.
| Number | Date | Country | Kind |
|---|---|---|---|
| 10-2023-0197116 | Dec 2023 | KR | national |
This application is a continuation of International Application No. PCT/KR2024/021400, filed on Dec. 30, 2024, with the Korean Intellectual Property Office, which claims priority from Korean Patent Application No. 10-2023-0197116 filed on Dec. 29, 2023, with the Korean Intellectual Property Office, the disclosures of which are incorporated herein in their entireties.
| Number | Date | Country | |
|---|---|---|---|
| Parent | PCT/KR2024/021440 | Dec 2024 | WO |
| Child | 19042856 | US |
