CN110522360A - Carpet detection method, device, sweeping robot and computer storage medium - Google Patents
Carpet detection method, device, sweeping robot and computer storage medium Download PDFInfo
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- CN110522360A CN110522360A CN201910838509.6A CN201910838509A CN110522360A CN 110522360 A CN110522360 A CN 110522360A CN 201910838509 A CN201910838509 A CN 201910838509A CN 110522360 A CN110522360 A CN 110522360A
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- carpet
- regression coefficient
- sweeping robot
- detection data
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- A—HUMAN NECESSITIES
- A47—FURNITURE; DOMESTIC ARTICLES OR APPLIANCES; COFFEE MILLS; SPICE MILLS; SUCTION CLEANERS IN GENERAL
- A47L—DOMESTIC WASHING OR CLEANING; SUCTION CLEANERS IN GENERAL
- A47L11/00—Machines for cleaning floors, carpets, furniture, walls, or wall coverings
- A47L11/24—Floor-sweeping machines, motor-driven
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- A—HUMAN NECESSITIES
- A47—FURNITURE; DOMESTIC ARTICLES OR APPLIANCES; COFFEE MILLS; SPICE MILLS; SUCTION CLEANERS IN GENERAL
- A47L—DOMESTIC WASHING OR CLEANING; SUCTION CLEANERS IN GENERAL
- A47L11/00—Machines for cleaning floors, carpets, furniture, walls, or wall coverings
- A47L11/40—Parts or details of machines not provided for in groups A47L11/02 - A47L11/38, or not restricted to one of these groups, e.g. handles, arrangements of switches, skirts, buffers, levers
- A47L11/4011—Regulation of the cleaning machine by electric means; Control systems and remote control systems therefor
Abstract
The present invention provides carpet detection method, device, sweeping robot and computer storage medium, which includes: the ground detection data for obtaining at least one sensor and detecting；The ground detection data are input in the corresponding regression coefficient model pre-established, are obtained at least one regression coefficient；Operation is carried out according to preset algorithm using at least one described regression coefficient, obtains carpet probability value；When determining that the carpet probability value is greater than or equal to predetermined probabilities value, determine that sweeping robot is located on carpet.Carpet detection method of the invention, it can use multiple sensors while obtaining ground detection data, and the detection that multiple regression coefficients carry out carpet is obtained by regression coefficient model, so as to improve the confidence level of detection, guarantee the precision of carpet detection, and improves the speed of carpet detection.
Description
Technical field
The present invention relates to sweeping robot fields, in particular to a kind of carpet detection method, device, machine of sweeping the floor
People and computer storage medium.
Background technique
With the rapid development of computer technology and artificial intelligence technology, intelligent robot technology is increasingly becoming modern machines
The hot spot of people's research field.Wherein, the one kind of sweeping robot as most practicalization in intelligent robot, can be artificial by it
Intelligence completes the cleaning work on ground.
Current intelligent sweeping robot, general use brush is swept and vacuum mode, and ground sundries is sucked to the dirt box of itself,
To complete the function of land clearing.But general sweeping robot, it is limited to built-in sensor and Processing Algorithm, nothing
Method detects the special articles such as the carpet in room, and carpet is equally cleaned also like conventional ground, can not effectively be removed
Rubbish and sundries on carpet, or with the simple decision procedure of single-sensor, the accuracy of carpet detection is lower, and holds
Erroneous detection easily occurs, and judges that speed is slower, is easy to carry out carpet wrong cleaning operation.
Summary of the invention
In view of the above problems, the present invention provides the storages of a kind of carpet detection method, device, sweeping robot and computer
Medium guarantees the precision of carpet detection to improve the confidence level of carpet detection, and improves the speed of carpet detection.
To achieve the goals above, the present invention adopts the following technical scheme that:
A kind of carpet detection method is applied to sweeping robot, comprising:
Obtain the ground detection data that at least one sensor detects；
The ground detection data are input in the corresponding regression coefficient model pre-established, are obtained at least one
Regression coefficient；
Operation is carried out according to preset algorithm using at least one described regression coefficient, obtains carpet probability value；
When determining that the carpet probability value is greater than or equal to predetermined probabilities value, determine that sweeping robot is located on carpet.
Preferably, in the carpet detection method, the sensor includes light stream imaging sensor, the ground detection
Data include light stream picture quality；
It is described " the ground detection data to be input in the corresponding regression coefficient model pre-established, are obtained to extremely
Lack a regression coefficient " include:
The light stream picture quality is input to the light stream image regression coefficient model pre-established after smoothing processing
In, obtain light stream regression coefficient.
Preferably, in the carpet detection method, the sensor includes infrared sensor, the ground detection data
It further include infrared signal；
It is described " the ground detection data to be input in the corresponding regression coefficient model pre-established, are obtained to extremely
A few regression coefficient " further include:
The infrared recurrence system pre-established is input to after the infrared signal is obtained infrared signal sampled value into over-sampling
In exponential model, infrared regression coefficient is obtained.
Preferably, in the carpet detection method, further includes:
The current value of the work drive motor is input to and corresponding builds in advance by the current value for obtaining at least one work drive motor
In vertical regression coefficient model, obtain at least one regression coefficient；
Wherein, the work drive motor includes round brush motor, movable motor and side brush motor, the regression coefficient model packet
Include round brush electric current regression coefficient model, walking electric current regression coefficient model and side brush current regression coefficient model.
Preferably, described " using at least one described regression coefficient according to preset algorithm in the carpet detection method
Operation is carried out, carpet probability value is obtained " include:
When determining that the regression coefficient is within the scope of corresponding preset regression coefficient, the regression coefficient respective weights are obtained
Probability value；
The probability value of each weight of acquisition is added, the carpet probability value is obtained.
Preferably, in the carpet detection method, further includes:
When determining that the sweeping robot is located on carpet, and determining that the sweeping robot be in cleaning modes, increasing
The suction of the big sweeping robot dust catcher.
Preferably, in the carpet detection method, further includes:
It is determining that the sweeping robot is located on carpet, and is determining that the sweeping robot is in control when mopping floor mode
It makes the sweeping robot and recesses carpet area, and mark the carpet area and/or be sent to the carpet area position
Client.
The present invention also provides a kind of carpet detection devices, are applied to sweeping robot, comprising:
Detection data obtains module, the ground detection data detected for obtaining at least one sensor；
Regression coefficient computing module, for the ground detection data to be input to the corresponding regression coefficient pre-established
In model, obtain at least one regression coefficient；
Carpet probability evaluation entity is obtained for carrying out operation according to preset algorithm using at least one described regression coefficient
Obtain carpet probability value；
Carpet detects determining module, for determining when determining that the carpet probability value is greater than or equal to predetermined probabilities value
Sweeping robot is located on carpet.
The present invention also provides a kind of sweeping robots, including memory, processor, light stream imaging sensor and infrared biography
Sensor, the memory run the computer program so that the sweeper for storing computer program, the processor
Device people executes the carpet detection method.
The present invention also provides a kind of computer storage medium, it is stored with calculating used in the sweeping robot
Machine program.
The present invention provides a kind of carpet detection method, is applied to sweeping robot, comprising: obtains at least one sensor inspection
The ground detection data measured；The ground detection data are input in the corresponding regression coefficient model pre-established, are obtained
It obtains at least one regression coefficient；Operation is carried out according to preset algorithm using at least one described regression coefficient, it is general to obtain carpet
Rate value；When determining that the carpet probability value is greater than or equal to predetermined probabilities value, determine that sweeping robot is located on carpet.This hair
Bright carpet detection method can use multiple sensors while obtain ground detection data, and obtained by regression coefficient model
The detection that multiple regression coefficients carry out carpets is obtained, so as to improve the confidence level of carpet detection, guarantees the accurate of carpet detection
Degree, and improve the speed of carpet detection.
To enable the above objects, features and advantages of the present invention to be clearer and more comprehensible, preferred embodiment is cited below particularly, and cooperate
Appended attached drawing, is described in detail below.
Detailed description of the invention
In order to illustrate more clearly of technical solution of the present invention, letter will be made to attached drawing needed in the embodiment below
It singly introduces, it should be understood that the following drawings illustrates only certain embodiments of the present invention, therefore is not construed as to the present invention
The restriction of protection scope.In various figures, part is similarly comprised using similar number.
Fig. 1 is a kind of flow chart for carpet detection method that the embodiment of the present invention 1 provides；
Fig. 2 is a kind of flow chart for carpet detection method that the embodiment of the present invention 2 provides；
Fig. 3 is a kind of flow chart for carpet detection method that the embodiment of the present invention 3 provides；
Fig. 4 is a kind of structural schematic diagram for carpet detection device that the embodiment of the present invention 4 provides.
Specific embodiment
Below in conjunction with attached drawing in the embodiment of the present invention, technical solution in the embodiment of the present invention carries out clear, complete
Ground description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.
The component of embodiments of the present invention, which are generally described and illustrated herein in the accompanying drawings can be come with a variety of different configurations
Arrangement and design.Therefore, requirement is not intended to limit to the detailed description of the embodiment of the present invention provided in the accompanying drawings below
The scope of the present invention of protection, but it is merely representative of selected embodiment of the invention.Based on the embodiment of the present invention, this field skill
Art personnel every other embodiment obtained without making creative work belongs to the model that the present invention protects
It encloses.
Hereinafter, term " includes ", " having " and its cognate that can be used in various embodiments of the present invention are only
It is intended to mean that special characteristic, number, step, operation, the combination of element, component or aforementioned item, and is understood not to first
Exclude the combined presence or increase by one of one or more other features, number, step, operation, element, component or aforementioned item
A or more feature, number, step, operation, element, component or aforementioned item combination a possibility that.
In addition, term " first ", " second ", " third " etc. are only used for distinguishing description, it is not understood to indicate or imply
Relative importance.
Unless otherwise defined, otherwise all terms (including technical terms and scientific terms) used herein have and this
The identical meaning of the various normally understood meanings of embodiment one skilled in the art of invention.The term (such as exists
The term limited in the dictionary generally used) it is to be interpreted as that there is contain identical with situational meaning in the related technical field
Justice and Utopian meaning or meaning too formal will be interpreted as having, unless in various embodiments of the present invention
It is clearly defined.
Embodiment 1
Fig. 1 is a kind of flow chart for carpet detection method that the embodiment of the present invention 1 provides, and this method comprises the following steps:
Step S11: the ground detection data that at least one sensor detects are obtained.
The present invention is that can be set in embodiment in sweeping robot there are many sensor, for example including there is infrared biography
Sensor, ultrasonic sensor and current sensor of various motors etc..When sweeping robot is worked, can use more
Kind sensor simultaneously detect by face, obtains the ground detection data of multiple sensors and is analyzed.
In the embodiment of the present invention, above-mentioned ground detection data are included at least in light stream picture quality and infrared signal wherein
It include at least one sensor such as streamer sensor and infrared sensor in a kind of data namely the sweeping robot, and
And the sweeping robot automatically turns on the sensor at work and obtains various ground detection data.
Step S12: the ground detection data are input in the corresponding regression coefficient model pre-established, acquisition pair
At least one regression coefficient.
It, can be by various ground datas after getting the ground detection data of multiple sensors in the embodiment of the present invention
It is input in corresponding regression coefficient model, obtains the output of corresponding regression coefficient model.Wherein, sensor includes light stream image
Sensor, the ground detection data include light stream picture quality, include: by the light stream picture quality when obtaining regression coefficient
It is input to after smoothing processing in the light stream image regression coefficient model pre-established, obtains light stream regression coefficient.
During the present invention is implemented, the sensor further includes infrared sensor, and the ground detection data further include infrared letter
Number, then it include: to be input in advance after the infrared signal is obtained infrared signal sampled value into over-sampling when obtaining regression coefficient
In the infrared regression coefficient model established, infrared regression coefficient is obtained.
In the embodiment of the present invention, whether can be not only on carpet by biosensor analysis sweeping robot, it can be with
Carpet detection is carried out by the electric current of work drive motor, and it is smooth that concrete principle is that the resistance that sweeping robot receives on carpet is greater than
Floor, therefore the electric current of work drive motor has apparent increase, therefore the step of above-mentioned acquisition regression coefficient can also include:
The current value of the work drive motor is input to the corresponding recurrence pre-established by the current value for obtaining at least one work drive motor
In Modulus Model, obtain at least one regression coefficient；Wherein, the work drive motor include round brush motor, movable motor and
Side brush motor, the regression coefficient model include round brush electric current regression coefficient model, walking electric current regression coefficient model and side
Brush current regression coefficient model.
In the embodiment of the present invention, the above-mentioned various regression coefficient models pre-established in sweeping robot are based on a large amount of
Test data, namely can use after corresponding sensor carries out carpet detection and obtain a large amount of detection datas, according to a large amount of
Detection data generates.It can also be provided with machine learning module in sweeping robot, lead to when sweeping robot is worked
The mode for crossing machine learning generates the corresponding regression coefficient model of sensor.
Step S13: operation is carried out according to preset algorithm using at least one described regression coefficient, obtains carpet probability value.
In the embodiment of the present invention, every kind of regression coefficient model can generate accordingly according to the ground detection data of input
Regression coefficient is compared, when regression coefficient is accordingly with pre-stored corresponding coefficient range using regression coefficient
When in number range, then it is believed that corresponding sensor detects carpet.Specifically, comprising: determine the regression coefficient in phase
When answering within the scope of preset regression coefficient, the probability value of the regression coefficient respective weights is obtained；By the general of each weight of acquisition
Rate value is added, and obtains the carpet probability value.
In the embodiment of the present invention, if passing through sensor, work drive motor and corresponding regression coefficient mould in sweeping robot
It is above-mentioned after type obtains light stream regression coefficient, movable motor coefficient, round brush coefficient of electrical machinery, side brush motor coefficient and infrared coefficient
When the process of acquisition carpet probability value comprises determining that the light stream regression coefficient is in preset light stream coefficient range, phase is obtained
The probability value for the first weight answered；And/or determine the round brush electric current regression coefficient within the scope of preset round brush current coefficient
When, obtain the probability value of corresponding second weight；And/or determine the walking electric current regression coefficient in preset walking electric current system
When in number range, the probability value of corresponding third weight is obtained；And/or determine it is described in brush current regression coefficient while preset
When in brush current coefficient range, the probability value of corresponding 4th weight is obtained；And/or determine the infrared regression coefficient default
Infrared coefficient range in when, obtain the probability value of corresponding 5th weight；The probability value of each weight of acquisition is added, is obtained
The carpet probability value.
In the embodiment of the present invention, the probability value of the above-mentioned various corresponding weights of regression coefficient namely according to the regression coefficient
Detect the confidence level of carpet.For example, the detection of carpet is mainly carried out in the sweeping robot using light stream imaging sensor, then
The probability of above-mentioned first weight can be set to 50%, the probability of the second weight, third weight, the 4th weight and the 5th weight
Value can be 20%, 10%, 10% and 10% respectively, determine light stream regression coefficient and round brush electric current regression coefficient in phase
When in the coefficient range answered, then the carpet probability value obtained is 70%.Wherein, regression coefficient is in corresponding coefficient range,
The probability value of preset respective weights will not then be obtained.
Step S14: when determining that the carpet probability value is greater than or equal to predetermined probabilities value, determine that sweeping robot is located at
On carpet.
In the embodiment of the present invention, predetermined probabilities value is additionally provided in sweeping robot, for judging with carpet probability value,
So that it is determined that whether sweeping robot is located on carpet.Wherein, which can be set to 50%, namely general in carpet
When rate value is higher than 50%, then it can determine that sweeping robot is located on carpet.
In the embodiment of the present invention, it can use multiple sensors while obtaining ground detection data, and pass through regression coefficient
Multiple regression coefficients that model obtains different weights carry out the detection of carpet, so as to improve the confidence level of detection, guarantee ground
The precision of blanket detection, and improve the speed of carpet detection.
Embodiment 2
Fig. 2 is a kind of flow chart for carpet detection method that the embodiment of the present invention 2 provides, and this method comprises the following steps:
Step S21: the ground detection data that at least one sensor detects are obtained.
This step is consistent with above-mentioned steps S11, and details are not described herein.
Step S22: the ground detection data are input in the corresponding regression coefficient model pre-established, acquisition pair
At least one regression coefficient.
This step is consistent with above-mentioned steps S12, and details are not described herein.
Step S23: operation is carried out according to preset algorithm using at least one described regression coefficient, obtains carpet probability value.
This step is consistent with above-mentioned steps S13, and details are not described herein.
Step S24: when determining that the carpet probability value is greater than or equal to predetermined probabilities value, determine that sweeping robot is located at
On carpet.
This step is consistent with above-mentioned steps S14, and details are not described herein.
Step S25: determining that the sweeping robot is located on carpet, and determines that the sweeping robot is in and clean mould
When formula, increase the suction of the sweeping robot dust catcher.
In the embodiment of the present invention, detect that sweeping robot is located on carpet by the above method, and work as sweeping robot
When in normal cleaning modes, then it can control the dust catcher of sweeping robot, increase the suction of dust catcher, have to carpet
Effect cleans, and in some sweeping robots for being provided with Carpet-cleaning mode, then it can switch to Carpet-cleaning mode.
Embodiment 3
Fig. 3 is a kind of flow chart for carpet detection method that the embodiment of the present invention 3 provides, and this method comprises the following steps:
Step S31: the ground detection data that at least one sensor detects are obtained.
This step is consistent with above-mentioned steps S11, and details are not described herein.
Step S32: the ground detection data are input in the corresponding regression coefficient model pre-established, acquisition pair
At least one regression coefficient.
This step is consistent with above-mentioned steps S12, and details are not described herein.
Step S33: operation is carried out according to preset algorithm using at least one described regression coefficient, obtains carpet probability value.
This step is consistent with above-mentioned steps S13, and details are not described herein.
Step S34: when determining that the carpet probability value is greater than or equal to predetermined probabilities value, determine that sweeping robot is located at
On carpet.
This step is consistent with above-mentioned steps S14, and details are not described herein.
Step S35: determining that the sweeping robot is located on carpet, and determines that the sweeping robot is in and mop floor
It when mode, controls the sweeping robot and recesses carpet area, and mark the carpet area and/or by carpet area position
It sets and is sent to client.
In the embodiment of the present invention, detect that sweeping robot is located on carpet by the above method, and work as sweeping robot
In mop floor mode when, which then can recess carpet area with opposite direction, and at the same time can detect ground at this
The zone marker of blanket is virtual carpet, the region can be avoided automatically when next time is to mop floor mode, when being normal cleaning modes
Enter the region after the suction of dust catcher can be improved in advance.Wherein, which can also will test the area of carpet
The location information in domain is sent to client, so that user can remove phase in sweeping robot by client after removing carpet
The virtual carpet label answered.
Embodiment 4
Fig. 4 is a kind of structural schematic diagram for carpet detection device that the embodiment of the present invention 4 provides.
The carpet detection device 400 includes:
Detection data obtains module 410, the ground detection data detected for obtaining at least one sensor；
Regression coefficient computing module 420, for the ground detection data to be input to the corresponding recurrence pre-established
In Modulus Model, obtain at least one regression coefficient；
Carpet probability evaluation entity 430, for carrying out operation according to preset algorithm using at least one described regression coefficient,
Obtain carpet probability value；
Carpet detect determining module 440, for when determine the carpet probability value be greater than or equal to predetermined probabilities value when, really
Determine sweeping robot to be located on carpet.
In the embodiment of the present invention, above-mentioned modules and each unit more detailed function description can refer to aforementioned
The content of corresponding portion in embodiment, details are not described herein.
In addition, the sweeping robot includes memory, processor, light stream the present invention also provides a kind of sweeping robot
Imaging sensor and infrared sensor, memory can be used for storing computer program, and processor is by running the computer
Program, to make the function of the sweeping robot execution above method or the modules in above-mentioned carpet detection device.
Memory may include storing program area and storage data area, wherein storing program area can storage program area, at least
Application program needed for one function (such as sound-playing function, image player function etc.) etc.；Storage data area can store root
Created data (such as audio data, phone directory etc.) etc. are used according to sweeping robot.In addition, memory may include height
Fast random access memory, can also include nonvolatile memory, a for example, at least disk memory, flush memory device,
Or other volatile solid-state parts.
The present embodiment additionally provides a kind of computer storage medium, for storing calculating used in above-mentioned sweeping robot
Machine program.
In several embodiments provided herein, it should be understood that disclosed device and method can also pass through
Other modes are realized.The apparatus embodiments described above are merely exemplary, for example, flow chart and structure in attached drawing
Figure shows the system frame in the cards of the device of multiple embodiments according to the present invention, method and computer program product
Structure, function and operation.In this regard, each box in flowchart or block diagram can represent a module, section or code
A part, a part of the module, section or code includes one or more for implementing the specified logical function
Executable instruction.It should also be noted that function marked in the box can also be to be different from the implementation as replacement
The sequence marked in attached drawing occurs.For example, two continuous boxes can actually be basically executed in parallel, they are sometimes
It can execute in the opposite order, this depends on the function involved.It is also noted that in structure chart and/or flow chart
The combination of each box and the box in structure chart and/or flow chart, can function or movement as defined in executing it is dedicated
Hardware based system realize, or can realize using a combination of dedicated hardware and computer instructions.
In addition, each functional module or unit in each embodiment of the present invention can integrate one independence of formation together
Part, be also possible to modules individualism, an independent part can also be integrated to form with two or more modules.
It, can be with if the function is realized and when sold or used as an independent product in the form of software function module
It is stored in a computer readable storage medium.Based on this understanding, technical solution of the present invention is substantially in other words
The part of the part that contributes to existing technology or the technical solution can be embodied in the form of software products, the meter
Calculation machine software product is stored in a storage medium, including some instructions are used so that a computer equipment (can be intelligence
Can mobile phone, personal computer, server or network equipment etc.) execute each embodiment the method for the present invention whole or
Part steps.And storage medium above-mentioned include: USB flash disk, mobile hard disk, read-only memory (ROM, Read-Only Memory),
Random access memory (RAM, Random Access Memory), magnetic or disk etc. be various to can store program code
Medium.
The above description is merely a specific embodiment, but scope of protection of the present invention is not limited thereto, any
Those familiar with the art in the technical scope disclosed by the present invention, can easily think of the change or the replacement, and should all contain
Lid is within protection scope of the present invention.Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. a kind of carpet detection method is applied to sweeping robot characterized by comprising
Obtain the ground detection data that at least one sensor detects；
The ground detection data are input in the corresponding regression coefficient model pre-established, are obtained at least one recurrence
Coefficient；
Operation is carried out according to preset algorithm using at least one described regression coefficient, obtains carpet probability value；
When determining that the carpet probability value is greater than or equal to predetermined probabilities value, determine that sweeping robot is located on carpet.
2. carpet detection method according to claim 1, which is characterized in that the sensor includes light stream image sensing
Device, the ground detection data include light stream picture quality；
It is described " the ground detection data to be input in the corresponding regression coefficient model pre-established, are obtained at least one
A regression coefficient " includes:
The light stream picture quality is input in the light stream image regression coefficient model pre-established after smoothing processing, is obtained
Obtain light stream regression coefficient.
3. carpet detection method according to claim 2, which is characterized in that the sensor includes infrared sensor, institute
Stating ground detection data further includes infrared signal；
It is described " the ground detection data to be input in the corresponding regression coefficient model pre-established, are obtained at least one
A regression coefficient " further include:
The infrared regression coefficient mould pre-established is input to after the infrared signal is obtained infrared signal sampled value into over-sampling
In type, infrared regression coefficient is obtained.
4. carpet detection method according to claim 1, which is characterized in that further include:
The current value of the work drive motor is input to corresponding pre-establish by the current value for obtaining at least one work drive motor
In regression coefficient model, obtain at least one regression coefficient；
Wherein, the work drive motor includes round brush motor, movable motor and side brush motor, and the regression coefficient model includes rolling
Brush current regression coefficient model, walking electric current regression coefficient model and side brush current regression coefficient model.
5. carpet detection method according to claim 1, which is characterized in that described " to utilize at least one described recurrence system
Number carries out operation according to preset algorithm, obtains carpet probability value " include:
When determining that the regression coefficient is within the scope of corresponding preset regression coefficient, the general of the regression coefficient respective weights is obtained
Rate value；
The probability value of each weight of acquisition is added, the carpet probability value is obtained.
6. carpet detection method according to claim 1, which is characterized in that further include:
When determining that the sweeping robot is located on carpet, and determining that the sweeping robot be in cleaning modes, increase institute
State the suction of sweeping robot dust catcher.
7. carpet detection method according to claim 1, which is characterized in that further include:
It is determining that the sweeping robot is located on carpet, and is determining that the sweeping robot is in when mopping floor mode, controlling institute
It states sweeping robot and recesses carpet area, and mark the carpet area and/or the carpet area position is sent to client
End.
8. a kind of carpet detection device is applied to sweeping robot characterized by comprising
Detection data obtains module, the ground detection data detected for obtaining at least one sensor；
Regression coefficient computing module, for the ground detection data to be input to the corresponding regression coefficient model pre-established
In, it obtains at least one regression coefficient；
Carpet probability evaluation entity obtains ground for carrying out operation according to preset algorithm using at least one described regression coefficient
Blanket probability value；
Carpet detects determining module, for when determining that the carpet probability value is greater than or equal to predetermined probabilities value, determination to be swept the floor
Robot is located on carpet.
9. a kind of sweeping robot, which is characterized in that including memory, processor, light stream imaging sensor and infrared sensing
Device, the memory run the computer program so that the machine of sweeping the floor for storing computer program, the processor
People executes carpet detection method according to any one of claim 1 to 7.
10. a kind of computer storage medium, which is characterized in that it, which is stored in sweeping robot as claimed in claim 9, is made
Computer program.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
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| CN201910838509.6A CN110522360A (en) | 2019-09-05 | 2019-09-05 | Carpet detection method, device, sweeping robot and computer storage medium |
| PCT/CN2020/113586 WO2021043287A1 (en) | 2019-09-05 | 2020-09-04 | Carpet detection method and apparatus, sweeping robot and computer storage medium |
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| Application Number | Priority Date | Filing Date | Title |
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| CN201910838509.6A CN110522360A (en) | 2019-09-05 | 2019-09-05 | Carpet detection method, device, sweeping robot and computer storage medium |
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| CN110522360A true CN110522360A (en) | 2019-12-03 |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111035327A (en) * | 2019-12-31 | 2020-04-21 | 深圳飞科机器人有限公司 | Cleaning robot, carpet detection method, and computer-readable storage medium |
| CN112137505A (en) * | 2020-08-31 | 2020-12-29 | 追创科技(苏州)有限公司 | Method and device for identifying ground features by automatic cleaning equipment |
| WO2021043287A1 (en) * | 2019-09-05 | 2021-03-11 | 深圳市杉川机器人有限公司 | Carpet detection method and apparatus, sweeping robot and computer storage medium |
| WO2023088063A1 (en) * | 2021-11-16 | 2023-05-25 | 北京石头创新科技有限公司 | Control method and apparatus for cleaning robot |
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| WO2021043287A1 (en) * | 2019-09-05 | 2021-03-11 | 深圳市杉川机器人有限公司 | Carpet detection method and apparatus, sweeping robot and computer storage medium |
| CN111035327A (en) * | 2019-12-31 | 2020-04-21 | 深圳飞科机器人有限公司 | Cleaning robot, carpet detection method, and computer-readable storage medium |
| CN111035327B (en) * | 2019-12-31 | 2024-01-30 | 上海飞科电器股份有限公司 | Cleaning robot, carpet detection method, and computer-readable storage medium |
| CN112137505A (en) * | 2020-08-31 | 2020-12-29 | 追创科技(苏州)有限公司 | Method and device for identifying ground features by automatic cleaning equipment |
| WO2022041886A1 (en) * | 2020-08-31 | 2022-03-03 | 追觅创新科技(苏州)有限公司 | Method and apparatus for identifying ground feature by automatic cleaning device |
| CN112137505B (en) * | 2020-08-31 | 2022-08-12 | 追觅创新科技(苏州)有限公司 | Method and device for identifying ground features by automatic cleaning equipment |
| CN115227149A (en) * | 2020-08-31 | 2022-10-25 | 追觅创新科技(苏州)有限公司 | Method and device for identifying ground characteristics by automatic cleaning equipment |
| CN115227149B (en) * | 2020-08-31 | 2023-08-08 | 追觅创新科技(苏州)有限公司 | Method and device for identifying ground characteristics of automatic cleaning equipment |
| EP4201293A4 (en) * | 2020-08-31 | 2024-02-21 | Dreame Innovation Tech Suzhou Co Ltd | Method and apparatus for identifying ground feature by automatic cleaning device |
| WO2023088063A1 (en) * | 2021-11-16 | 2023-05-25 | 北京石头创新科技有限公司 | Control method and apparatus for cleaning robot |
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