Method for determining backwashing timing of cleaning robot and cleaning robot

Abstract

The application provides a backwashing opportunity determination method for a cleaning robot and the cleaning robot. The cleaning robot comprises a mop for mopping the ground. The method comprises: acquiring ground dirt information; and determining a backwashing opportunity of the cleaning robot based on the ground dirt information. The technical scheme provided by the embodiment of the application can improve the cleaning effect.

Description

Technical Field

[0001] This application relates to the field of robotics technology, and in particular to a method for determining the rewashing timing of a cleaning robot and the cleaning robot itself. Background Technology

[0002] With the development of technology and the improvement of living standards, hands-free smart homes have appeared in ordinary households and are gradually changing people's lifestyles. Daily household cleaning often involves many people burdened with chores, leading to the development of intelligent cleaning robots. These robots alleviate the burden of housework for this group and bring a better experience to the whole family.

[0003] For cleaning robots with mopping functions, they all follow a fixed rewash time, such as returning to the base station to clean the mop every 10 or 15 minutes of mopping, or every 20 or 25 square meters of mopping.

[0004] However, when cleaning according to a fixed rewash time or area, after cleaning liquid dirt, the robot may continue to clean other floors until the fixed rewash time or area is reached. Liquid dirt will affect the cleaning ability of the mop, making the cleaning effect of the floors that continue to be cleaned worse. Summary of the Invention

[0005] This application provides a method for determining the rewashing timing of a cleaning robot and a cleaning robot, in order to improve the cleaning effect.

[0006] This application provides a method for determining the rewashing timing of a cleaning robot, wherein the cleaning robot includes a mopping component for mopping the floor, and the method includes:

[0007] Obtain information on ground dirt and grime;

[0008] Based on the information about the dirt on the ground, the timing for the cleaning robot to return to its original cleaning position is determined.

[0009] In one embodiment, the ground dirt information includes the type of dirt in the dirt area;

[0010] Based on the information about the dirt on the ground, the timing of the cleaning robot's rewash is determined, including:

[0011] If the type of dirt is the first type, then the timing for rewashing is determined as follows: immediately return to the base station to rewash the mop after mopping the dirty area on the ground;

[0012] And / or,

[0013] If the type of dirt is the second type, then the timing for rewashing is determined as follows: after mopping the dirty area on the floor, reduce the original rewash area / rewash time.

[0014] In one embodiment, the dirt type characterizes the intensity of dirt contamination and / or the volume of water contamination.

[0015] In one embodiment, the ground dirt information includes: ground dirt information representing the overall dirt status of the area where the cleaning robot is currently located;

[0016] The step of determining the rewashing time of the cleaning robot based on the dirt information of the ground includes: determining the rewashing time of the cleaning robot in the zone based on the overall dirt information of the zone.

[0017] In one embodiment, the overall dirt information of the partition is the dirt level information representing the partition input by the user. The higher the dirt level, the lower the rewash time / rewash area.

[0018] In one embodiment, the soiled area on the ground is a liquid soiled area.

[0019] In one embodiment, obtaining ground dirt information includes:

[0020] Obtain ground images using a camera;

[0021] Identify areas of liquid contamination in the ground image;

[0022] Based on the area and / or color information of the liquid contaminated area, the type of contamination in the liquid contaminated area is determined according to a preset mapping relationship.

[0023] In one embodiment, identifying liquid-contaminated areas in the ground image includes:

[0024] Edge detection is performed on the ground image to determine the initial dirty area image;

[0025] If the brightness value of the initial dirty area image is greater than the first threshold, the texture feature is non-granular, and the clarity of the edge information is greater than the second threshold, the initial dirty area image is determined to be a liquid dirty area.

[0026] In one embodiment, the method further includes:

[0027] When returning to the base station, raise the mop.

[0028] This application embodiment also provides a cleaning robot, the cleaning robot comprising:

[0029] processor;

[0030] Memory used to store processor-executable instructions;

[0031] The processor is configured to execute the cleaning method of the cleaning robot described above.

[0032] The technical solution provided in the above embodiments of this application obtains information on the dirt on the ground; based on the information on the dirt on the ground, it determines the timing of the cleaning robot's return cleaning. Compared with the existing fixed return cleaning area or return cleaning time, the technical solution provided in the embodiments of this application adaptively selects the return cleaning timing, which can avoid the problem of poor mopping effect caused by returning the cleaning timing too late. Attached Figure Description

[0033] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly described below.

[0034] Figure 1 A flowchart illustrating a method for determining the rewashing timing of a cleaning robot, provided in an embodiment of this application;

[0035] Figure 2 yes Figure 1 Detailed flowchart of step S110 in the corresponding embodiment;

[0036] Figure 3 yes Figure 2 Detailed flowchart of step S122 in the corresponding embodiment;

[0037] Figure 4 This is a schematic diagram of the architecture of the cleaning system provided in the embodiments of this application;

[0038] Figure 5 This is a schematic diagram of the internal architecture of the cleaning robot provided in the embodiments of this application. Detailed Implementation

[0039] The technical solutions in the embodiments of this application will now be described with reference to the accompanying drawings.

[0040] Similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0041] Figure 1This is a flowchart illustrating a method for determining the rewashing timing of a cleaning robot, provided in an embodiment of this application. The cleaning robot includes a mopping component for mopping the floor. The mopping component can be a rotary mop, a flat mop, or a roller mop, etc. This method can be executed by the cleaning robot. If necessary, floor images captured by a camera can also be sent to a backend server, which will then execute the method provided in this embodiment to control the cleaning robot to perform its work. The following description uses the cleaning robot's execution as an example. Figure 1 As shown, the method includes the following steps S110-S120.

[0042] Step S110: Obtain information on ground dirt and grime.

[0043] In one embodiment, the cleaning robot may be equipped with a camera, which can capture images of the ground and identify dirt information in dirty areas of the ground based on the captured images.

[0044] or,

[0045] The cleaning robot communicates with a mobile terminal, which displays an environmental map. Users can input information about the dirt on the floor through the mobile terminal, such as marking the location and / or type of dirt on the map. Alternatively, the environmental map can be divided into zones, allowing users to input information about the dirt on the floor corresponding to a specific zone, such as marking the dirt level or cleanliness of a particular zone (e.g., a room).

[0046] Step S120: Based on the information about dirt on the ground, determine when to rewash the cleaning robot.

[0047] Among them, the return washing timing is used to characterize when the cleaning robot returns to the base station to clean the mop.

[0048] In one embodiment, dirt on the ground can affect the cleaning effect of the mop. For example, after the mop wipes the dirt, it becomes contaminated and may contaminate other floors when cleaning them again. Or, after the mop wipes, the amount of wastewater from the mop is high, and it may contaminate other floors when cleaning them again. Therefore, the cleaning robot can determine the timing of its return wash based on the dirt information on the ground, so that the cleaning robot can return to the base station in a timely manner and the base station can clean the mop. This can avoid the problem of poor mopping effect caused by returning the mop too late.

[0049] In one embodiment, the ground dirt information may include the type of dirt in the dirt area, and the dirt area may be a liquid dirt area.

[0050] The type of dirt can be a type that characterizes the ability of liquid dirt to contaminate other areas after the mop has wiped the liquid dirt. Alternatively, the type of dirt can be a type that characterizes the amount of water in the liquid dirt. For example, the type of dirt can be a first type (which can be called Class A), a second type (which can be called Class B), and a third type (which can be called Class C), with Class A indicating the highest contamination ability or the highest amount of water, followed by Class B, and then Class C.

[0051] Accordingly, determining the timing of the cleaning robot's rewash can include one or any combination of the following methods:

[0052] Method 1: If the type of dirt is Type 1, then the timing for rewashing is determined as follows: immediately return to the base station to rewash the mop after mopping the dirty area on the floor.

[0053] Method 2: If the type of dirt is Type 2, then determine the timing of the rewash: after mopping the dirty area of ​​the floor, reduce the original rewash area / rewash time.

[0054] Method 3: If the type of dirt is the third type, then the timing for rewashing is determined as follows: after mopping the dirty area of ​​the floor, rewash according to the original rewash area / rewash time.

[0055] For example, assuming the initial cleaning area is 20 square meters, when the dirt type is A, the cleaning robot returns to the base station immediately after cleaning the liquid dirt to wash the mop. When the dirt type is B, the cleaning robot reduces the cleaning area after cleaning the liquid dirt, for example, from 20 square meters to 10 square meters, and returns to the base station to wash the mop when the cleaning area reaches 10 square meters. When the dirt type is C, the cleaning robot continues to clean the original cleaning area after cleaning the liquid dirt, that is, it returns to the base station to wash the mop when the cleaning area reaches 20 square meters. This allows the robot to return to the base station promptly when the mop is heavily soiled, and to continue cleaning the floor when the mop is not heavily soiled, cleaning as much area as possible. Alternatively, it can return to the base station promptly when there is too much dirty water on the mop, and continue cleaning the floor when there is not too much dirty water, cleaning as much area as possible.

[0056] In one embodiment, such as Figure 2 As shown, the above step S110 specifically includes step S111 and step S113.

[0057] Step S111: Acquire ground images using a camera.

[0058] Step S112: Identify liquid-stained areas in the ground image.

[0059] Liquid contamination areas refer to regions in a ground image containing liquid-like contaminants. In one embodiment, liquid contamination areas in a ground image can be identified using a pre-trained image recognition model.

[0060] Step S113: Determine the type of contamination based on the area and / or color information of the contaminated area according to a preset mapping relationship.

[0061] For example, the types of contamination can be C, B, and A, as mentioned above, with the contamination capacity / volume increasing in that order. The contamination type can be determined based on the size of the contaminated area; a larger area indicates a greater contamination capacity / volume. A mapping relationship can be established between area size and contamination type. For instance, an area less than 0.1 square meters is classified as contamination type C, an area of ​​0.1-0.15 square meters is classified as contamination type B, an area of ​​0.15-0.2 square meters is classified as contamination type A, and so on.

[0062] For example, the type of contamination can be determined based on the color of the contaminated area. The darker the color, the greater the contamination capacity. A mapping relationship can be set between the grayscale value of the color and the type of contamination. For example, a grayscale value of 150-170 is type C, a grayscale value of 170-190 is type B, a grayscale value of 190-210 is type A, and so on.

[0063] For example, the type of contamination can be determined based on the color and area of ​​the liquid contamination area. Color and area can correspond to different weights. For example, y = ax1 + bx2, where x1 represents the grayscale value of the color, x2 represents the area size, a and b represent the weights, a + b = 1, and y represents the overall value. y > Y1 is type A, y > Y2 is type B, y > Y3 is type C, and Y1 > Y2 > Y3. Y1, Y2 > Y3 are set constants. This comprehensive consideration of the color and area of ​​the liquid contamination area allows for a more accurate determination of the contamination type.

[0064] In one embodiment, such as Figure 3 As shown, step S122 specifically includes:

[0065] Step S1221: Perform edge detection on the ground image to determine the initial dirty area.

[0066] Because furniture and other items are at different heights from dirt, the ground image can be converted into a grayscale image. Edge detection operators such as Sobel, Prewitt, and Roberts are then used to detect edges in the grayscale image. Finally, based on the angle range between the vertical and horizontal edges of the items, as well as the height range of the vertical edges, the items and dirt areas are distinguished from the grayscale image. The initial dirt area can be considered the dirt area in the ground image; it is called the initial dirt area to distinguish it from liquid dirt areas, solid dirt areas, etc.

[0067] Step S1222: Based on the shape and color information of the initial dirty area, determine whether the initial dirty area is a liquid dirty area.

[0068] The initial dirty area may be a solid dirty area or a liquid dirty area.

[0069] Morphological information can include texture and shape, while color information can include color and brightness. Solid dirt such as rice and soybeans differ from liquid dirt in color, shape, and brightness due to their distinct granular structure. Liquid dirt, such as soy sauce, has a higher reflectivity (h_0), weaker granular texture, and clearer edge information; while soybeans have distinct granular structure and more fine edge information within the area. Therefore, a cleaning robot can determine whether an initial dirty area is solid or liquid based on its morphological and color information.

[0070] In one embodiment, color information may include brightness values, and shape information may include texture features and edge information. If the brightness value of the initial dirty area is greater than a first threshold, the texture features are non-granular, and the clarity of the edge information is greater than a second threshold, the initial dirty area is determined to be a liquid dirty area. In another embodiment, if the texture features within the initial dirty area are granular, and there are many short-sized edge features within the initial dirty area, then the initial dirty area is a solid dirty area.

[0071] In one embodiment, based on the above-mentioned distinction between solid and liquid dirt, if solid dirt is detected, the robot cleaning mode can be set to vacuuming mode; the suction power of vacuuming mode is determined according to the particle diameter of solid dirt and the mapping relationship between particle diameter and suction power.

[0072] Solid dirt is unsuitable for mopping due to its solid state; it's better suited for vacuuming and sweeping. Therefore, confirm the current status of the cleaning robot and adjust its cleaning mode to vacuuming and sweeping. Edge detection can determine the particle diameter of solid dirt. Based on the particle diameter within the solid dirt area, determine an appropriate suction power. For example, if the robot's suction range is [p_min, p_max] (in kPa), for a particle diameter of s, the suction power is set to...

[0073]

[0074] Common robot suction power ranges from 1.6 kPa to 2.5 kPa, i.e., p_min = 1.6 and p_max = 2.5. Therefore, in areas with solid dirt, the suction power can be set to P to vacuum and sweep solid dirt.

[0075] In one embodiment, the method provided in this application further includes: if the liquid dirt is of the first type, an obstacle avoidance and detour strategy can be adopted to avoid the liquid dirt. After cleaning other areas (excluding the liquid dirt area in the current partition), return to clean the liquid dirt, and immediately return to the base station to clean the mop after cleaning.

[0076] In one embodiment, the ground dirt information includes: ground dirt information representing the overall dirt of the area where the cleaning robot is currently located; determining the cleaning robot's return washing time based on the ground dirt information includes: determining the cleaning robot's return washing time for wiping and mopping in the area based on the overall dirt information of the area.

[0077] Among them, the overall dirt information of the partition is the dirt level information representing the partition input by the user. The higher the dirt level, the lower the rewash time / rewash area.

[0078] For example, the cleaning robot communicates with a mobile terminal, which displays an environmental map. The environmental map is divided into zones, and the user can input the floor dirt information corresponding to a certain zone, such as marking the floor dirt level of a certain zone (e.g., a certain room).

[0079] In one embodiment, the mop is raised upon returning to the base station for cleaning. This avoids secondary contamination during the return journey.

[0080] Figure 4 This is a schematic diagram of the architecture of a cleaning system provided in an embodiment of this application. Figure 4 As shown, the cleaning system includes a base station 410 and a cleaning robot 420. A mop 421 is mounted on the bottom of the cleaning robot 420. The base station 410 is equipped with a mop cleaning device 411. The cleaning robot 420 includes a camera 422, which can capture images of the floor in the environment to be cleaned. Then, the processor inside the cleaning robot identifies the type of liquid dirt and determines when to rewash the mop based on the type of dirt. Therefore, the rewashing timing can be adaptively adjusted according to the different types of liquid dirt, thereby avoiding secondary pollution.

[0081] Figure 5 This is a schematic diagram of the internal architecture of a cleaning robot provided in an embodiment of this application. The cleaning robot 500 includes a processor 510 and a memory 520 for storing executable instructions of the processor 510; wherein, the processor 510 is configured to execute the cleaning method of the cleaning robot described above.

[0082] The apparatuses and methods disclosed in the several embodiments provided in this application can also be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatuses, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0083] In addition, the functional modules in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0084] If a function is implemented as a software module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

Claims

1. A method for determining the rewashing timing of a cleaning robot, characterized in that, The cleaning robot includes a mopping component for mopping the floor, and the method includes: Obtain ground dirt information; the ground dirt information includes the type of dirt in the dirty area; if the dirt type is the first type, then determine the rewashing timing as follows: immediately return to the base station to rewash the mop after mopping the dirty area; And / or, If the type of dirt is the second type, then the timing for rewashing is determined as follows: after mopping the dirty area of ​​the floor, reduce the original rewash area / rewash time. Wherein, when the dirt type characterizes the contamination capacity of liquid dirt, the first type has a higher contamination capacity than the second type. When the type of dirt represents the amount of water in liquid dirt, the amount of water in the first type is greater than that in the second type.

2. The method according to claim 1, characterized in that, The soiled area on the ground is a liquid soiled area.

3. The method according to claim 2, characterized in that, The acquisition of ground dirt information includes: Obtain ground images using a camera; Identify areas of liquid contamination in the ground image; Based on the area and / or color information of the liquid contaminated area, the type of contamination in the liquid contaminated area is determined according to a preset mapping relationship.

4. The method according to claim 3, characterized in that, The process of identifying liquid-contaminated areas in the ground image includes: Edge detection is performed on the ground image to determine the initial dirty area image; If the brightness value of the initial dirty area image is greater than the first threshold, the texture feature is non-granular, and the clarity of the edge information is greater than the second threshold, the initial dirty area image is determined to be a liquid dirty area.

5. The method according to claim 1, characterized in that, The method further includes: When returning to the base station, raise the mop.

6. A cleaning robot, characterized in that, The cleaning robot includes: processor; Memory used to store processor-executable instructions; The processor is configured to perform the cleaning method of the cleaning robot according to any one of claims 1-5.

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