WO2021043287A1 - Procédé et appareil de détection de tapis, robot de balayage et support de stockage informatique - Google Patents

Procédé et appareil de détection de tapis, robot de balayage et support de stockage informatique Download PDF

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Publication number
WO2021043287A1
WO2021043287A1 PCT/CN2020/113586 CN2020113586W WO2021043287A1 WO 2021043287 A1 WO2021043287 A1 WO 2021043287A1 CN 2020113586 W CN2020113586 W CN 2020113586W WO 2021043287 A1 WO2021043287 A1 WO 2021043287A1
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WO
WIPO (PCT)
Prior art keywords
carpet
regression coefficient
sweeping robot
probability value
detection data
Prior art date
Application number
PCT/CN2020/113586
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English (en)
Chinese (zh)
Inventor
杨勇
吴泽晓
罗治佳
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深圳市杉川机器人有限公司
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Publication of WO2021043287A1 publication Critical patent/WO2021043287A1/fr

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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
    • 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

Definitions

  • the present disclosure relates to the field of sweeping robots, and in particular to a carpet detection method, device, sweeping robot, and computer storage medium.
  • the present disclosure provides a carpet detection method, device, sweeping robot, and computer storage medium to improve the reliability of carpet detection, ensure the accuracy of carpet detection, and increase the speed of carpet detection.
  • a carpet detection method, applied to a sweeping robot includes:
  • the sensor includes an optical flow image sensor, and the ground detection data includes optical flow image quality;
  • the "inputting the ground detection data into the corresponding pre-established regression coefficient model to obtain at least one regression coefficient” includes:
  • optical flow image quality is smoothed and then input into a pre-established optical flow image regression coefficient model to obtain the optical flow regression coefficient.
  • the sensor includes an infrared sensor, and the ground detection data further includes an infrared signal;
  • the "inputting the ground detection data into the corresponding pre-established regression coefficient model to obtain at least one regression coefficient” also includes:
  • the infrared signal is oversampled to obtain the infrared signal sampling value and then input into the pre-established infrared regression coefficient model to obtain the infrared regression coefficient.
  • the carpet detection method further includes:
  • the working motor includes a rolling brush motor, a walking motor, and a side brush motor
  • the regression coefficient model includes a rolling brush current regression coefficient model, a walking current regression coefficient model, and a side brush current regression coefficient model.
  • the "using the at least one regression coefficient to perform an operation according to a preset algorithm to obtain a carpet probability value" includes:
  • the probability value of the corresponding weight of the regression coefficient is obtained
  • the probability value of each weight obtained is added to obtain the probability value of the carpet.
  • the carpet detection method further includes:
  • the suction power of the vacuum cleaner of the sweeping robot is increased.
  • the carpet detection method further includes:
  • control the sweeping robot When it is determined that the sweeping robot is located on the carpet and it is determined that the sweeping robot is in the mopping mode, control the sweeping robot to retreat from the carpet area, mark the carpet area and/or send the location of the carpet area to the customer end.
  • the present disclosure also provides a carpet detection device, which is applied to the sweeping robot, and includes:
  • the detection data acquisition module is configured to acquire ground detection data detected by at least one sensor
  • a regression coefficient calculation module configured to input the ground detection data into a corresponding pre-established regression coefficient model to obtain at least one regression coefficient
  • the carpet probability calculation module is configured to use the at least one regression coefficient to perform calculations according to a preset algorithm to obtain a carpet probability value
  • the carpet detection and determination module is configured to determine that the cleaning robot is located on the carpet when it is determined that the probability value of the carpet is greater than or equal to the preset probability value.
  • the present disclosure also provides a sweeping robot, including a memory, a processor, an optical flow image sensor, and an infrared sensor.
  • the memory is configured to store a computer program, and the processor runs the computer program to make the sweeping robot execute the The carpet detection method.
  • the present disclosure also provides a computer storage medium, which stores the computer program used in the sweeping robot.
  • the present disclosure provides a carpet detection method, which is applied to a sweeping robot, including: acquiring ground detection data detected by at least one sensor; inputting the ground detection data into a corresponding pre-established regression coefficient model to obtain at least one Regression coefficient; using the at least one regression coefficient to perform operations according to a preset algorithm to obtain a carpet probability value; when it is determined that the carpet probability value is greater than or equal to the preset probability value, it is determined that the sweeping robot is located on the carpet.
  • the carpet detection method of the present disclosure can simultaneously obtain ground detection data by using multiple sensors, and obtain multiple regression coefficients through the regression coefficient model for carpet detection, thereby improving the reliability of carpet detection and ensuring the accuracy of carpet detection. And improve the speed of carpet detection.
  • FIG. 1 is a flowchart of a carpet detection method provided in Embodiment 1 of the present disclosure
  • FIG. 2 is a flowchart of a carpet detection method provided in Embodiment 2 of the present disclosure
  • FIG. 3 is a flowchart of a carpet detection method provided in Embodiment 3 of the present disclosure.
  • FIG. 4 is a schematic structural diagram of a carpet detection device provided in Embodiment 4 of the present disclosure.
  • Fig. 1 is a flowchart of a carpet detection method provided in Embodiment 1 of the present disclosure, and the method includes the following steps:
  • Step S11 Obtain ground detection data detected by at least one sensor.
  • various sensors may be provided in the sweeping robot, for example, current sensors including infrared sensors, ultrasonic sensors, and various motors.
  • current sensors including infrared sensors, ultrasonic sensors, and various motors.
  • multiple sensors can be used to perform ground detection at the same time, and ground detection data from multiple sensors can be obtained and analyzed.
  • the above-mentioned ground detection data includes at least one of optical flow image quality and infrared signals
  • the sweeping robot includes at least one sensor such as a streamer sensor and an infrared sensor, and the sweeping robot is Automatically turn on the above-mentioned sensors when working and obtain various ground detection data.
  • Step S12 Input the ground detection data into the corresponding pre-established regression coefficient model to obtain at least one regression coefficient.
  • various ground data can be input into the corresponding regression coefficient model to obtain the output of the corresponding regression coefficient model.
  • the sensor includes an optical flow image sensor
  • the ground detection data includes optical flow image quality
  • obtaining the regression coefficient includes: smoothing the optical flow image quality and inputting it into a pre-established optical flow image regression coefficient model, Obtain the optical flow regression coefficient.
  • the above-mentioned sensor further includes an infrared sensor, and the ground detection data also includes an infrared signal.
  • the regression coefficient includes: over-sampling the infrared signal to obtain an infrared signal sample value and then input it to a pre-established infrared regression In the coefficient model, the infrared regression coefficient is obtained.
  • the sweeping robot be analyzed by the sensor whether it is on the carpet, but also the carpet detection can be carried out by the current of the working motor.
  • the specific principle is that the resistance received by the sweeping robot on the carpet is greater than the smooth floor, so the working motor is The current will increase significantly, so the above step of obtaining the regression coefficient may further include: obtaining the current value of at least one working motor, and inputting the current value of the working motor into the corresponding pre-established regression coefficient model to obtain For at least one regression coefficient; wherein the working motor includes a rolling brush motor, a walking motor, and a side brush motor, and the regression coefficient model includes a rolling brush current regression coefficient model, a walking current regression coefficient model, and a side brush current regression coefficient model.
  • the above-mentioned regression coefficient models established in advance in the sweeping robot are based on a large amount of test data, that is, a large amount of detection data can be obtained after carpet detection with corresponding sensors, and generated based on a large amount of detection data.
  • a machine learning module can also be provided in the sweeping robot, and a regression coefficient model corresponding to the sensor is generated through machine learning when the sweeping robot is working.
  • Step S13 Use the at least one regression coefficient to perform calculations according to a preset algorithm to obtain a carpet probability value.
  • each regression coefficient model can generate a corresponding regression coefficient according to the input ground detection data, and compare the regression coefficient with the corresponding coefficient range stored in advance.
  • the method includes: obtaining the probability value of the corresponding weight of the regression coefficient when determining that the regression coefficient is within the corresponding preset regression coefficient range; adding the obtained probability values of the weights to obtain the carpet probability value.
  • the carpet probability value is obtained as described above
  • the process includes: determining that the optical flow regression coefficient is within the preset optical flow coefficient range, obtaining the corresponding probability value of the first weight; and/or determining that the rolling brush current regression coefficient is within the preset rolling brush current
  • the coefficient is within the range, obtain the probability value of the corresponding second weight; and/or when it is determined that the walking current regression coefficient is within the preset walking current coefficient range, obtain the probability value of the corresponding third weight; and/or determine
  • the side brush current regression coefficient is within the preset side brush current coefficient range, obtain the corresponding probability value of the fourth weight; and/or when it is determined that the infrared regression coefficient is within the preset infrared coefficient range, obtain the corresponding The probability value of the fifth weight of, and the probability value of each weight obtained
  • the probability values of the weights corresponding to the various regression coefficients mentioned above are also the reliability of detecting the carpet based on the regression coefficients.
  • the sweeping robot mainly uses optical flow image sensors to detect carpets, the probability of the first weight can be set to 50%, and the probability values of the second weight, the third weight, the fourth weight, and the fifth weight can be set to It is 20%, 10%, 10%, and 10%.
  • the probability value of the carpet obtained is 70%. Among them, when the regression coefficient is not in the corresponding coefficient range, the preset probability value of the corresponding weight will not be obtained.
  • Step S14 When it is determined that the probability value of the carpet is greater than or equal to the preset probability value, it is determined that the sweeping robot is located on the carpet.
  • a preset probability value is also set in the cleaning robot, which is configured to judge with the carpet probability value, so as to determine whether the cleaning robot is located on the carpet.
  • the preset probability value can be set to 50%, that is, when the carpet probability value is higher than 50%, it can be determined that the sweeping robot is located on the carpet.
  • multiple sensors can be used to obtain ground detection data at the same time, and multiple regression coefficients with different weights can be obtained through the regression coefficient model for carpet detection, thereby improving the reliability of detection and ensuring the accuracy of carpet detection , And improve the speed of carpet detection.
  • Fig. 2 is a flowchart of a carpet detection method provided in Embodiment 2 of the present disclosure, and the method includes the following steps:
  • Step S21 Obtain ground detection data detected by at least one sensor.
  • Step S22 Input the ground detection data into the corresponding pre-established regression coefficient model to obtain at least one regression coefficient.
  • Step S23 Use the at least one regression coefficient to perform calculations according to a preset algorithm to obtain a carpet probability value.
  • Step S24 When it is determined that the probability value of the carpet is greater than or equal to the preset probability value, it is determined that the sweeping robot is located on the carpet.
  • Step S25 When it is determined that the cleaning robot is located on the carpet, and it is determined that the cleaning robot is in the cleaning mode, increase the suction power of the vacuum cleaner of the cleaning robot.
  • the vacuum cleaner of the sweeping robot can be controlled to increase the suction power of the vacuum cleaner to effectively clean the carpet.
  • Some cleaning robots equipped with a carpet cleaning mode can switch to the carpet cleaning mode.
  • FIG. 3 is a flowchart of a carpet detection method provided in Embodiment 3 of the present disclosure, and the method includes the following steps:
  • Step S31 Obtain ground detection data detected by at least one sensor.
  • Step S32 Input the ground detection data into the corresponding pre-established regression coefficient model to obtain at least one regression coefficient.
  • Step S33 Use the at least one regression coefficient to perform calculations according to a preset algorithm to obtain a carpet probability value.
  • Step S34 When it is determined that the probability value of the carpet is greater than or equal to the preset probability value, it is determined that the sweeping robot is located on the carpet.
  • Step S35 When it is determined that the sweeping robot is located on the carpet and it is determined that the sweeping robot is in the mopping mode, control the sweeping robot to retreat from the carpet area, and mark the carpet area and/or move the carpet area The location is sent to the client.
  • the sweeping robot is located on the carpet, and when the sweeping robot is in the mopping mode, the sweeping robot can retreat from the carpet area in the opposite direction, and at the same time can mark the area where the carpet is detected It is a virtual carpet, it can automatically avoid this area when it is in mopping mode next time, and when it is in normal cleaning mode, it can enter this area after increasing the suction power of the vacuum cleaner in advance.
  • the sweeping robot can also send the location information of the area where the carpet is detected to the client, so that the user can clear the corresponding virtual carpet mark in the sweeping robot through the client after removing the carpet.
  • FIG. 4 is a schematic structural diagram of a carpet detection device provided in Embodiment 4 of the present disclosure.
  • the carpet detection device 400 includes:
  • the detection data acquisition module 410 is configured to acquire ground detection data detected by at least one sensor
  • the regression coefficient calculation module 420 is configured to input the ground detection data into a corresponding pre-established regression coefficient model to obtain at least one regression coefficient
  • the carpet probability calculation module 430 is configured to use the at least one regression coefficient to perform calculations according to a preset algorithm to obtain a carpet probability value
  • the carpet detection and determination module 440 is configured to determine that the sweeping robot is located on the carpet when it is determined that the probability value of the carpet is greater than or equal to the preset probability value.
  • the present disclosure also provides a cleaning robot.
  • the cleaning robot includes a memory, a processor, an optical flow image sensor, and an infrared sensor.
  • the memory can be configured to store a computer program.
  • the processor runs the computer program to make the cleaning robot Perform the above method or the function of each module in the above carpet detection device.
  • the memory may include a storage program area and a storage data area.
  • the storage program area may store an operating system, an application program required by at least one function (such as a sound playback function or an image playback function, etc.), etc.; the storage data area may store according to the sweeping robot Use the created data (such as audio data or phone book, etc.) and so on.
  • the memory may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one magnetic disk storage device, a flash memory device, or other volatile solid-state storage devices.
  • This embodiment also provides a computer storage medium configured to store the computer program used in the above-mentioned sweeping robot.
  • each block in the flowchart or block diagram may represent a module, program segment, or part of code, and the module, program segment, or part of code includes one or more Executable instructions.
  • the functions marked in the block may also occur in a different order from the order marked in the drawings.
  • each block in the structure diagram and/or flowchart, and the combination of the blocks in the structure diagram and/or flowchart can be used as a dedicated hardware-based system that performs specified functions or actions. , Or can be realized by a combination of dedicated hardware and computer instructions.
  • the functional modules or units in the various embodiments of the present disclosure may be integrated together to form an independent part, or each module may exist alone, or two or more modules may be integrated to form an independent part.
  • the function is implemented in the form of a software function module and sold or used as an independent product, it can be stored in a computer readable storage medium.
  • the technical solution of the present disclosure essentially or the part that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including Several instructions are used to make a computer device (which may be a smart phone, a personal computer, a server, or a network device, etc.) execute all or part of the steps of the methods described in the various embodiments of the present disclosure.
  • the aforementioned storage media include: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disks or optical disks and other media that can store program codes. .

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Abstract

La présente invention concerne un procédé et un appareil de détection de tapis, un robot de balayage et un support de stockage informatique. Le procédé de détection de tapis comprend les étapes suivantes : acquisition de données de détection de sol détectées par au moins un capteur ; application des données de détection de sol à l'entrée d'un modèle de coefficient de régression préétabli correspondant pour obtenir au moins un coefficient de régression ; utilisation dudit coefficient de régression pour effectuer des calculs conformément à un algorithme prédéfini afin d'obtenir une valeur de probabilité de tapis ; et lorsqu'il est déterminé que la valeur de probabilité de tapis est égale ou supérieure à une valeur de probabilité prédéfinie, détermination qu'un robot de balayage se trouve sur un tapis. Le procédé de détection de tapis peut utiliser une pluralité de capteurs pour obtenir simultanément des données de détection de sol et, au moyen du modèle de coefficient de régression, obtenir une pluralité de coefficients de régression pour la détection de tapis, ce qui peut ainsi améliorer la fiabilité de détection, garantir la précision de détection de tapis et augmenter la vitesse de détection de tapis.
PCT/CN2020/113586 2019-09-05 2020-09-04 Procédé et appareil de détection de tapis, robot de balayage et support de stockage informatique WO2021043287A1 (fr)

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CN201910838509.6A CN110522360A (zh) 2019-09-05 2019-09-05 地毯检测方法、装置、扫地机器人和计算机存储介质
CN201910838509.6 2019-09-05

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CN110522360A (zh) * 2019-09-05 2019-12-03 深圳市杉川机器人有限公司 地毯检测方法、装置、扫地机器人和计算机存储介质
CN111035327B (zh) * 2019-12-31 2024-01-30 上海飞科电器股份有限公司 清洁机器人、地毯检测方法及计算机可读存储介质
CN115227149B (zh) * 2020-08-31 2023-08-08 追觅创新科技(苏州)有限公司 一种自动清洁设备识别地面特征的方法及装置
CN114587210B (zh) * 2021-11-16 2023-06-20 北京石头创新科技有限公司 清洁机器人控制方法及控制装置

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