Cleaning device

Abstract

To provide a cleaning device capable of determining a degree of contamination of an object to be cleaned without needing to store information on the object to be cleaned when it is not contaminated.SOLUTION: A comparison unit 111 compares images shown by each of pre-cleaning image data and post-cleaning image data that have been supplied and calculates a comparison amount. A determination unit 112 refers to a conversion table 121 stored in a memory 12, converts the calculated comparison amount to a contamination amount, and determines a degree of contamination of an object to be cleaned from the segment of the contamination amount. A determination unit 113 refers to a course DB 122 stored in the memory 12 and determines a cleaning course according to the determined degree of contamination. A commanding unit 114 commands a moving unit 17 to move a cleaning device 1 according to the determined cleaning course. Also, the commanding unit 114 commends a cleaning unit 18 to clean the object to be cleaned by a cleaning condition stipulated in the determined cleaning course.SELECTED DRAWING: Figure 8

Description

[Technical Field]

[0001] The present invention relates to the art of cleaning devices. [Background technology]

[0002] Cleaning robots have been developed that set cleaning courses (referred to as cleaning programs) according to the degree of dirt on the object to be cleaned.

[0003] Patent Document 1 discloses a cleaning robot that acquires dust amount information to determine the amount of dust in at least uncleaned areas of a cleaning area to be cleaned, determines a travel route according to the amount of dust based on this dust amount information so that the greater the amount of dust, the greater the amount of cleaning overlap, and controls a travel drive unit to travel along that travel route.

[0004] Patent Document 2 discloses an autonomous driving work device that can perform autonomous driving work by driving autonomously and working automatically according to a program input in advance, and when there is a plan consisting of two or more driving routes, at the start of the autonomous driving work, the autonomous driving work device determines a recommended plan from those two or more plans that meets specified recommendation conditions and notifies the user of the recommended plan.

[0005] Patent document 3 discloses an information processing device that acquires image information from a first robot, detects floor dirt and its location information based on the image information, determines a cleaning robot that will clean the detected floor dirt as a second robot, determines a cleaning method that this cleaning robot should use, and instructs the cleaning robot on the location information and cleaning method.

[0006] Patent Document 4 discloses a cleaning system that specifies the area to be cleaned in a cleaning target based on dimensional information about the cleaning target. [Prior art documents] [Patent documents]

[0007] [Patent Document 1] Japanese Patent Application Laid-Open No. 2000-353014 [Patent Document 2] Japanese Patent Application Publication No. 2018-139720 [Patent Document 3] WO19 / 171917 publication [Patent Document 4] Japanese Patent Application Laid-Open No. 2014-14456 Summary of the Invention [Problem to be solved by the invention]

[0008] Incidentally, when determining the current degree of dirtiness based on information such as an image of the object to be cleaned when it was not dirty, the cleaning robot must store the information that serves as the reference.

[0009] One of the objects of the present invention is to determine the degree of soiling of an object to be cleaned without storing information about the object when it was not soiled. [Means for solving the problem]

[0010] The present invention photographs an object to be cleaned before and after a predetermined amount of cleaning, and determines the degree of dirtiness of the object to be cleaned based on a comparison between an image of the object to be cleaned before cleaning and an image of the object to be cleaned after cleaning. Then, one cleaning course is determined based on the determined degree of dirtiness from among a plurality of cleaning courses including a moving path and a moving speed that are stored in advance, and the object to be cleaned is cleaned based on the determined one cleaning course. A cleaning device is provided as a first aspect.

[0011] According to the cleaning device of the first aspect, it is possible to determine the degree of dirtiness of the object to be cleaned without storing information about the object to be cleaned when it was not dirty. Furthermore, this cleaning device selects an appropriate cleaning course from among multiple pre-stored courses based on the determined level of dirt, thereby achieving just the right amount of cleaning according to the condition of the surface to be cleaned.

[0012] In the cleaning device of the first aspect, The cleaning result including the degree of dirt is stored as a history, and the next cleaning cycle is determined based on the stored history. may be adopted as a second aspect.

[0013] According to the cleaning device of the second aspect, The tendency of dirt on the object to be cleaned is accumulated as a history and the next cleaning cycle is determined based on that, so cleaning frequency can be optimized and efficient cleaning management can be performed. do.

[0014] No. 1 or 2 In the cleaning device of the embodiment, The plurality of cleaning courses include: The greater the degree of dirtiness of the object to be cleaned, Dense Travel route Yes do It is mapped as follows: This configuration may be adopted as a third aspect.

[0015] According to the cleaning device of the third aspect, the number of times an object to be cleaned increases as the degree of dirtiness of the object increases.

[0016] No. 1st to 3rd In the cleaning device of the embodiment, The plurality of cleaning courses include: The greater the degree of dirtiness of the object to be cleaned, slow Movement speed Yes do It is mapped as follows: This configuration may be adopted as a fourth aspect.

[0017] According to the cleaning device of the fourth aspect, the more soiled the object to be cleaned, the longer it takes to clean it. In the cleaning device of the first to fourth aspects, a configuration may be adopted as a fifth aspect in which, when determining the degree of dirtiness, the conversion formula based on the comparison is switched depending on the material of the object to be cleaned. According to the cleaning device of the fifth aspect, it is possible to absorb differences in the appearance of dirt that arise due to differences in the material of the object to be cleaned, and to more accurately determine the degree of dirt in various environments. [Brief explanation of the drawings]

[0018] [Figure 1] 1 is a diagram showing an example of the configuration of a cleaning device 1. FIG. [Figure 2] 1 is a schematic diagram showing an example of the appearance of a cleaning device 1. FIG. [Figure 3] FIG. 12 shows an example of a conversion table 121. [Figure 4] 10A and 10B are diagrams showing examples of the relationship between the image comparison amount and the amount of dirt expressed by a conversion formula. [Figure 5] FIG. 10 is a diagram showing an example of a course DB 122. [Figure 6] FIG. 10 is a diagram showing an example of a cleaning course. [Figure 7] FIG. 10 is a diagram showing an example of a history DB 123. [Figure 8] FIG. 2 is a diagram showing an example of the functional configuration of the cleaning device 1. [Figure 9] 3 is a flow chart showing an example of the operation flow of the cleaning device 1. DETAILED DESCRIPTION OF THE INVENTION

[0019] <Embodiment> <Cleaning device configuration> Fig. 1 is a diagram showing an example of the configuration of the cleaning device 1. Fig. 2 is a schematic diagram showing an example of the appearance of the cleaning device 1.

[0020] 1 includes a processor 11, a memory 12, an operation unit 14, a display unit 15, an image capturing unit 16, a moving unit 17, and a cleaning unit 18. These components are connected to each other via, for example, a bus so that they can communicate with each other. Note that the cleaning device 1 may also include a communication unit for connecting to other devices via a wired or wireless connection so that they can communicate with each other.

[0021] 1 controls each part of the cleaning device 1 by reading and executing a computer program (hereinafter simply referred to as a program) stored in memory 12. Processor 11 is, for example, a CPU (Central Processing Unit).

[0022] The operation unit 14 is equipped with operation buttons, a touch panel, and other operators for issuing various instructions, and receives an operation and sends a signal corresponding to the operation content to the processor 11. This operation is, for example, pressing an operation button, a gesture on the touch panel, or the like.

[0023] The display unit 15 has a display screen such as a liquid crystal display, and displays images under the control of the processor 11. A transparent touch panel of the operation unit 14 may be placed on top of the display screen. Note that the cleaning device 1 does not have to have either or both of the operation unit 14 and the display unit 15. The cleaning device 1 may be operated from an external device or may present information to an external device, for example, via a communication unit (not shown).

[0024] Cleaning unit 18 is configured to clean an object to be cleaned. For example, cleaning unit 18 shown in FIG. 2 includes a tank that stores cleaning liquid and a pipe that discharges the cleaning liquid from the tank. Cleaning unit 18 may also include a pump that sucks the cleaning liquid from the tank through the pipe. The pipe of cleaning unit 18 may also include a solenoid valve that adjusts the amount of cleaning liquid discharged. The pump and solenoid valve are controlled by processor 11, for example.

[0025] The cleaning unit 18 also includes a brush that distributes the cleaning liquid discharged from the pipe onto the object to be cleaned and that frictionally cleans the surface of the object onto which the cleaning liquid has been dispersed, and a motor that rotates and drives the brush. In the cleaning device 1 shown in Figure 2, the object to be cleaned by the cleaning unit 18 is the floor G. The motor that drives the brush of the cleaning unit 18 is controlled by, for example, the processor 11.

[0026] It should be noted that the object to be cleaned by the cleaning unit 18 is not limited to the floor surface G. For example, the object to be cleaned by the cleaning unit 18 may be a wall surface, a ceiling surface, or the like.

[0027] The moving unit 17 is configured to move the cleaning device 1. For example, the moving unit 17 shown in FIG. 2 has a plurality of tires that contact the floor surface G, and moves the moving unit 17 in the direction of arrow D on the floor surface G, which is the object to be cleaned by the cleaning unit 18, by rotating the tires with a motor (not shown). The motor that drives the tires of the moving unit 17 is controlled by, for example, the processor 11. At least one of the tires of the moving unit 17 has a steering function that changes the direction of movement under the control of the processor 11 shown in FIG. 1.

[0028] The photographing unit 16 is configured to photograph the object to be cleaned. For example, the photographing unit 16 shown in FIG. 2 is a digital still camera having an optical system and an image sensor. The optical system is, for example, a lens, a mirror, etc. that collects light arriving from the object to be cleaned. The image sensor is, for example, a complementary metal oxide semiconductor (CMOS) image sensor or a charge coupled device (CCD) image sensor. The image sensor generates image data representing an image corresponding to the light collected by the optical system. The photographing unit 16 may also have a light blocking member that blocks external light from reaching the object to be cleaned, and an illumination device such as an LED (light emitting diode) that illuminates the object to be cleaned.

[0029] The photographing unit 16 photographs the object to be cleaned before and after the cleaning unit 18, which is moved by the moving unit 17, performs a predetermined amount of cleaning. For example, in the cleaning device 1 shown in FIG. 2, the photographing unit 16 has a front camera 161 and a rear camera 162. The front camera 161 is attached to the front in the direction of arrow D along which the cleaning device 1 moves. As a result, the front camera 161 photographs an area of ​​the floor surface G just before the cleaning unit 18 cleans it. The rear camera 162 is attached to the rear in the direction of arrow D along which the cleaning device 1 moves. As a result, the rear camera 162 photographs an area of ​​the floor surface G just after the cleaning unit 18 has cleaned it.

[0030] The cleaning device 1 shown in Fig. 2 has a housing 10 that includes a bottom surface and cylindrical side surfaces and is open at the top. A lid is attached to the open top surface of the housing 10 so that it can be opened and closed using a hinge or the like. The lid incorporates an operating unit 14. The bottom surface of the housing 10 is also fitted with tires of a moving unit 17 and a brush of a cleaning unit 18. The tank, pipes, etc. of the cleaning unit 18 described above are housed inside the housing 10.

[0031] 1 is a storage means for storing an operating system, various programs, data, etc., which are loaded into the processor 11. The memory 12 includes a RAM (Random Access Memory) and a ROM (Read Only Memory). The memory 12 may also include a solid state drive, a hard disk drive, etc.

[0032] The memory 12 also stores a conversion table 121, a course DB 122, and a history DB 123.

[0033] Fig. 3 is a diagram showing an example of the conversion table 121. The conversion table 121 is a table that stores information for estimating the amount of dirt on an object to be cleaned from the amount obtained by comparing images of the object to be cleaned taken before and after cleaning. The conversion table 121 shown in Fig. 3 has a column for type, a column for type name, a column for image comparison amount, and a column for conversion formula.

[0034] The type column in the conversion table 121 is a column for storing a type ID for identifying the type of the object to be cleaned.

[0035] The type name field is a field for storing the name of the type of object to be cleaned identified by the type ID. The type name field stores the name of the material of the floor G to be cleaned, such as marble, granite, linoleum, vinyl, etc.

[0036] The image comparison amount column is a column for storing the type of amount (called an image comparison amount) determined by comparing images of the object to be cleaned taken before and after a predetermined amount of cleaning has been performed. This image comparison amount is used as an independent variable used to estimate the amount of dirt on the object to be cleaned. The image comparison amount is, for example, a difference in brightness or a difference in lightness. The image comparison amount may also be, for example, the length per unit area of ​​a contour detected by a predetermined algorithm. The image comparison amount may also be a statistical amount based on the difference in each pixel value of the images taken before and after cleaning. This statistical amount may be, for example, a variance, a standard deviation, a difference between the median and the arithmetic mean, or a difference between the maximum and minimum values.

[0037] The conversion formula column is a column for storing a formula for estimating the amount of dirt using the image comparison amount as an independent variable. This formula is an approximation formula derived from accumulated data using the least squares method or the like. Figure 4 is a diagram showing an example of the relationship between the image comparison amount and the amount of dirt expressed by the conversion formula. The horizontal axis in Figure 4 represents the image comparison amount, and the vertical axis represents the amount of dirt. The amount of dirt on the object to be cleaned is estimated from the image comparison amount using a curve corresponding to the conversion formula shown in Figure 4.

[0038] The amount of dirt is expressed, for example, by a dimensionless number, a percentage, or the like according to the amount of dirt. The amount of dirt is classified into a plurality of levels of dirt. The processor 11 of the cleaning device 1 classifies the estimated amount of dirt and determines the corresponding level of dirt. For example, if the level of dirt is classified into three levels, there are three levels of dirt: "large," "medium," and "small."

[0039] Fig. 5 is a diagram showing an example of the course DB 122. The course DB 122 is a database that stores cleaning courses corresponding to the degree of dirt determined from the estimated amount of dirt. The course DB 122 shown in Fig. 5 has a course ID column, a course name column, a dirt level column, a speed column, a route information column, and a cleaning condition column.

[0040] The course ID column in the course DB 122 is a column for storing a course ID, which is identification information for identifying a cleaning course.

[0041] The course name field stores the name of the cleaning course identified by the corresponding course ID. For example, the course name corresponding to the course ID "C01" is "Dense."

[0042] The "Level of Dirt" field stores the level of dirt when a cleaning program identified by a corresponding program ID is applied. Processor 11 estimates the amount of dirt by comparing images of the object to be cleaned taken before and after a predetermined amount of cleaning, determines the level of dirt from the amount of dirt, and then refers to program DB 122 to select a cleaning program that corresponds to the determined level of dirt.

[0043] The "speed" column stores the movement speed used in the cleaning course identified by the corresponding course ID. Once the processor 11 determines a cleaning course, it controls the movement unit 17 to move the cleaning device 1 at the speed associated with that cleaning course. For example, the speed associated with course ID "C01" is "low speed."

[0044] The route information column is a column for storing the travel route adopted in the cleaning course identified by the corresponding course ID.

[0045] The cleaning device 1 cleans the floor surface G, for example, while moving in a direction along one side of the rectangular floor surface G (hereinafter referred to as the main scanning direction). Then, when the cleaning device 1 hits a wall surface, it moves a predetermined distance in a direction along the other side of the floor surface G (hereinafter referred to as the sub-scanning direction), and cleans the floor surface G while moving in the direction opposite to the main scanning direction. That is, the cleaning device 1 moves back and forth in the main scanning direction of the rectangular floor surface G, and moves a predetermined distance in the sub-scanning direction each time it reaches an edge.

[0046] 6A and 6B are diagrams showing examples of cleaning courses. For example, the cleaning course shown in FIG. 6A is a cleaning course with the course name "Dense." This cleaning course has a relatively dense movement path. For example, as shown in FIG. 6A, this cleaning course has a movement path that moves across the floor surface G eight times in the sub-scanning direction.

[0047] The cleaning course shown in Figure 6(b) is a "standard" cleaning course. This cleaning course has a standard degree of density of movement paths. For example, as shown in Figure 6(b), this cleaning course has a movement path that moves across the floor surface G six times in the sub-scanning direction.

[0048] The cleaning course shown in Figure 6(c) is a "coarse" cleaning course. This cleaning course is a coarse course with relatively large gaps between the movement paths. For example, as shown in Figure 6(c), this cleaning course has a movement path that moves across the floor G three times in the sub-scanning direction.

[0049] 5, the cleaning conditions column stores the cleaning conditions adopted for the cleaning course identified by the corresponding course ID. The cleaning conditions include, for example, the amount of cleaning liquid supplied per unit time and the rotation speed of the brush.

[0050] Fig. 7 is a diagram showing an example of the history DB 123. The history DB 123 is a database that stores pairs of the date and time when an object to be cleaned was cleaned and the degree of dirt thereon. The history DB 123 shown in Fig. 7 includes a room ID list 1231 and a history table 1232.

[0051] The room ID list 1231 is a list of room IDs that identify rooms having floor surfaces G, which are objects to be cleaned. Each room ID listed in the room ID list 1231 is associated with and stored in a history table 1232.

[0052] The history table 1232 in the history DB 123 is a table that stores the cleaning date and time for the corresponding room ID and the degree of dirtiness of the object to be cleaned at that time in association with each other as a history. The history table 1232 shown in Fig. 7 has a column for date and time, a column for degree of dirtiness, and a column for cleaning conditions.

[0053] The date and time column in the history table 1232 stores information on the date and time when the floor G, which is the cleaning target, was cleaned in the room ID associated with the history table 1232.

[0054] The dirt level column stores the dirt level determined for the floor G of the room ID associated with the history table 1232 when the floor G was cleaned on the corresponding date and time.

[0055] The cleaning conditions column stores the cleaning conditions when the floor G of the room ID associated with the history table 1232 was cleaned on the corresponding date and time.

[0056] <Functional configuration of cleaning device> 8 is a diagram showing an example of the functional configuration of the cleaning device 1. The processor 11 of the cleaning device 1 executes a program stored in the memory 12 to function as a comparison unit 111, a determination unit 112, a decision unit 113, and an instruction unit 114.

[0057] The photographing unit 16 photographs the object to be cleaned before a predetermined amount of cleaning is performed using the front camera 161 (see FIG. 1). Then, the photographing unit 16 supplies the processor 11 with image data (referred to as "previous image data") representing the image obtained by photographing with the front camera 161.

[0058] Furthermore, the photographing unit 16 photographs the object to be cleaned after a predetermined amount of cleaning has been performed using the rear camera 162 (see FIG. 1). Then, the photographing unit 16 supplies the processor 11 with image data (referred to as rear image data) showing the image obtained by the photographing by the rear camera 162.

[0059] The comparison unit 111 compares the images indicated by the supplied front image data and back image data, and calculates the comparison amount (that is, the image comparison amount).

[0060] The determining unit 112 refers to the conversion table 121 stored in the memory 12, converts the calculated image comparison amount into the amount of dirt, and determines the degree of dirt on the object to be cleaned from the classification of the amount of dirt.

[0061] The determination unit 113 determines a cleaning program according to the determined level of dirtiness by referring to the program DB 122 stored in the memory 12. Furthermore, the determination unit 113 stores the results of cleaning in the history DB 123 each time cleaning is performed.

[0062] 8 determines the interval until the next time the cleaning target is to be cleaned by referring to the history DB 123. This interval can be calculated, for example, by aggregating the interval between the dates and times of two adjacent cleanings stored in the history DB 123 and the difference in the degree of dirt determined in each cleaning. For example, the determination unit 113 can determine the interval until the next time the cleaning target is to be cleaned by using the results of multivariate analysis in which the interval between two consecutive cleanings is used as an explanatory variable and the difference in the degree of dirt at the start of the two cleanings is used as a response variable.

[0063] The instruction unit 114 instructs the movement unit 17 to move the cleaning device 1 along the determined cleaning course. The instruction unit 114 also instructs the cleaning unit 18 to clean the object to be cleaned under the cleaning conditions defined in the determined cleaning course.

[0064] In addition, the instruction unit 114 may identify the next timing for cleaning the object to be cleaned according to the determined cycle, and when that timing arrives, instruct the moving unit 17 and the cleaning unit 18 to start cleaning.

[0065] <Cleaning device operation> Fig. 9 is a flow diagram showing an example of the flow of operations of the cleaning device 1. The processor 11 of the cleaning device 1 starts the operations shown in Fig. 9 when a predetermined condition is met, for example, when the operation unit 14 receives an operation to start cleaning, or when a predetermined period has passed since the previous cleaning. At this time, the cleaning device 1 may specify information about the material of the floor surface G, which is the object to be cleaned, and a room ID that identifies the room that has that floor surface G, etc., through a user's operation or pre-stored information, etc.

[0066] The processor 11 controls the front camera 161 of the photographing unit 16 to photograph an area of ​​the floor G, which is the object to be cleaned, before a predetermined amount of cleaning has been performed (step S101). The front camera 161 generates front image data and supplies it to the processor 11.

[0067] Next, processor 11 controls moving unit 17 to move cleaning device 1 so that cleaning unit 18 is positioned above the area of ​​floor G photographed by front camera 161. Processor 11 then performs a predetermined amount of cleaning on that area (step S102). This predetermined amount is determined in advance by, for example, the amount of cleaning liquid, the rotation speed of the brush, and the rotation time.

[0068] When a predetermined amount of cleaning is completed, processor 11 controls moving unit 17 to move cleaning device 1, and controls photographing unit 16 to have rear camera 162 photograph the cleaned area (step S103). Rear camera 162 generates rear image data and supplies it to processor 11.

[0069] The processor 11 compares the images represented by the before image data and the after image data supplied from the photographing unit 16 (step S104). That is, the processor 11 compares the two images representing the states of the object to be cleaned before and after cleaning.

[0070] Processor 11 then calculates the image comparison amount and converts the image comparison amount into a dirt amount by referring to conversion table 121. Processor 11 determines the dirt level of the object to be cleaned according to the dirt amount obtained by converting the image comparison amount (step S105).

[0071] In other words, the cleaning device 1 having this processor 11 is an example of a cleaning device that photographs an object to be cleaned before and after a predetermined amount of cleaning, and determines the degree of dirtiness of the object to be cleaned based on a comparison of the image of the object to be cleaned before cleaning and the image of the object to be cleaned after cleaning.

[0072] After determining the degree of dirtiness, processor 11 refers to the cleaning program DB 122 and determines a cleaning program according to the degree of dirtiness (step S106).

[0073] For example, when referring to the course DB 122 shown in Fig. 5, processor 11 determines a cleaning course with the course name "standard" if the degree of dirt is "medium." When the degree of dirt is "high," which is greater than "medium," processor 11 determines a cleaning course with a denser movement path than the cleaning course with the course name "standard," i.e., a cleaning course with the course name "dense."

[0074] On the other hand, when the degree of soiling is "light", which is lower than "medium", processor 11 determines a cleaning course with a rougher movement path than the cleaning course with the course name "standard", i.e., a cleaning course with the course name "coarse".

[0075] That is, this processor 11 is an example of a cleaning device that, the greater the degree of dirtiness of the determined object to be cleaned, the denser the movement path for cleaning the object to be cleaned.

[0076] Also, for example, when referring to the course DB122 shown in Figure 5, when the degree of dirt is "large", which is greater than "medium", the processor 11 determines a cleaning course that has a slower movement speed than the cleaning course with the course name "standard", i.e., a cleaning course that moves at a "low speed".

[0077] On the other hand, when the degree of soiling is "light", which is lower than "medium", processor 11 determines a cleaning course with a faster movement speed than the cleaning course with the course name "standard", i.e., a cleaning course with movement at "high speed".

[0078] In other words, the cleaning device 1 having this processor 11 is an example of a cleaning device that, the greater the determined degree of dirtiness of the object to be cleaned, the slower the moving speed for cleaning the object to be cleaned.

[0079] Then, processor 11 instructs moving unit 17 and cleaning unit 18 to clean the object to be cleaned using the determined cleaning course (step S107). Processor 11 also stores the date and time of cleaning, the degree of dirtiness of the object to be cleaned, the amount of dirt, etc. in history DB 123 of memory 12 (step S108).

[0080] Processor 11 determines whether the instructed cleaning has been completed (step S109). If it determines that the instructed cleaning has not been completed (step S109; NO), processor 11 returns the process to step S101. At this time, processor 11 may return the process to step S107.

[0081] On the other hand, if it is determined that the instructed cleaning has been completed (step S109; YES), the processor 11 determines the period until the next cleaning is to be performed, i.e., the cleaning cycle, based on the information stored in the history DB 123 of the memory 12 (step S110), and terminates the processing.

[0082] By performing the operations described above, the cleaning device 1 can determine the degree of dirtiness of the object to be cleaned by photographing the object to be cleaned immediately before and after a predetermined amount of cleaning, without having to store the degree of cleaning of the object to be cleaned when it is not dirty as a standard or reference degree of cleaning.

[0083] The configurations, shapes, sizes, and layout relationships described in the above embodiments are merely schematic illustrations to enable understanding and implementation of the present invention. Therefore, the present invention is not limited to the described embodiments, and can be modified in various forms without departing from the scope of the technical ideas set forth in the claims.

[0084] <Modification> The above is a description of the embodiment, but the contents of this embodiment can be modified as follows. In addition, the following modifications can be combined.

[0085] <1> In the above-described embodiment, the processor 11 is a CPU, but may have other configurations. For example, the processor 11 may be or include an FPGA (Field Programmable Gate Array). The processor 11 may also have an ASIC (Application Specific Integrated Circuit) or other programmable logic device and perform control using these. The processor 11 may also include a GPU (Graphics Processing Unit).

[0086] <2> In the above-described embodiment, the processor 11 of the cleaning device 1 determines the degree of dirtiness of the object to be cleaned and then determines both the movement path and the movement speed for cleaning the object to be cleaned in accordance with the degree of dirtiness, but it may also determine either one of these. In this case, the processor 11 is an example of a cleaning device that determines at least one of the movement path and the movement speed for cleaning the object to be cleaned in accordance with the determined degree of dirtiness of the object to be cleaned.

[0087] <3> The program executed by the processor 11 described above may be provided in a state stored in a computer-readable recording medium such as a magnetic recording medium such as a magnetic tape or a magnetic disk, an optical recording medium such as an optical disk, a magneto-optical recording medium, a semiconductor memory, etc. The program may also be downloaded via a communication line such as the Internet.

[0088] <4> In the above-described embodiment, the processor 11 of the cleaning device 1 made the movement path for cleaning the object to be cleaned denser as the determined degree of dirtiness of the object to be cleaned increased, but it is not necessary to associate the degree of dirtiness with the density of the movement path.

[0089] Furthermore, processor 11 slows the movement speed for cleaning the object to be cleaned as the determined degree of dirtiness of the object to be cleaned increases, but it is not necessary to associate the degree of dirtiness with the movement speed. [Explanation of symbols]

[0090] 1...cleaning device, 10...casing, 11...processor, 111...comparison unit, 112...judgment unit, 113...decision unit, 114...instruction unit, 12...memory, 121...conversion table, 122...course DB, 123...history DB, 1231...room ID list, 1232...history table, 14...operation unit, 15...display unit, 16...photography unit, 161...front camera, 162...rear camera, 17...movement unit, 18...cleaning unit.

Claims

1. A cleaning device that photographs an object to be cleaned before and after a predetermined amount of cleaning, determines the degree of dirtiness of the object to be cleaned based on a comparison between an image of the object to be cleaned before cleaning and an image of the object to be cleaned after cleaning, determines one cleaning course from a plurality of cleaning courses including a movement path and a movement speed that are stored in advance based on the determined degree of dirtiness, and cleans the object to be cleaned based on the determined one cleaning course.

2. The cleaning result including the degree of dirt is stored as a history, and the next cleaning cycle is determined based on the stored history. The cleaning device of claim 1 .

3. The plurality of cleaning courses are associated with each other so that the greater the degree of dirtiness of the object to be cleaned, the denser the movement paths.

3. The cleaning device according to claim 1 or 2.

4. The plurality of cleaning courses are associated with each other so that the greater the degree of dirtiness of the object to be cleaned, the slower the movement speed.

4. A cleaning device according to any one of claims 1 to 3.

5. When determining the degree of dirtiness, the conversion formula based on the comparison is switched depending on the material of the object to be cleaned.

5. A cleaning device according to any one of claims 1 to 4.

Images