Smart lawn mower

A smart lawn mower comprises a traveling control module configured to control the traveling and steering of the mower, an image capturing module configured to capture the surrounding images of the mower, an operation module configured to provide a surrounding-determination information, and a storage module configured to store the surrounding-determination information. The operation module determines a grass area by analyzing the surrounding images captured by the image capturing module. The mower defines a grass area accurately without a predetermined boundary.

BACKGROUND OF THE INVENTION

1. Field of the Invention

Embodiments disclosed herein relate to a smart lawn mower configured to determine a surrounding image relative to various environments.

2. Description of the Prior Art

Current smart lawnmower is capable to autonomously travel and cut grass on an area within a predetermined boundary, which leads to a limitation that the mower cannot determine a grass area autonomously if the user does not predetermine a boundary. In another respect, the smart lawn mower may determine a grass area based on default datasets. However, real-world environments with respect to such factors as various lighting environments and various types of grass change continuously. It is difficult for the smart lawn mower to determine the grass area accurately based on the default datasets. Therefore there is a continuous need for a new and improved smart lawn mower.

SUMMARY OF THE INVENTION

In one embodiment, a smart lawn mower comprises a traveling control module, an image capturing module, an operation module and may further comprise a storage module. The operation module provides a surrounding-determination information and determines a grass area by analyzing surrounding images based on the surrounding-determination information while the mower operates to cut grass autonomously. The traveling control module controls a driving motor to move the mower on the grass area.

In one embodiment, a smart lawn mower comprises a machine learning module, wherein the machine learning module adjusts the surrounding-determination information by adjusting at least one weight of at least one neural network node based on the surrounding images captured while the mower follows the user to travel on the grass area.

To facilitate understanding, identical reference numerals have been used, where possible, to designate identical elements that are common to the figures. It is contemplated that elements disclosed in one embodiment may be beneficially utilized with other embodiments without specific recitation.

DETAILED DESCRIPTION

FIG. 1shows the smart lawn mower1including an image capturing module11, a traveling control module12, an operation module13, a storage module14, a blade control module15, a power module16, a posture acquiring module17, a distance-aware module18, a locating module19, a display module110and a machine learning module111. The traveling control module12includes a plurality of wheels coupled with a driving motor and is configured to control the traveling and steering of the mower. The image capturing module11is configured to capture the surrounding images of the mower. The operation module13is coupled with the image capturing module11, the traveling control module12and the storage module14. The operation module13provides a surrounding-determination information142based on the surrounding images captured by the image capturing module11and stores the surrounding-determination information142in the storage module14.FIG. 1further shows a user2, a grass area3and non-grass area4.

FIG. 2is a flow chart illustrating the operation of the mower1. At step S11, the user2starts up the mower1to perform a function of cutting grass without a predetermined boundary. At step S12, the mower1decides whether the surrounding-determination information142stored in the storage module14is sufficient to determine the surrounding images captured by the image capturing module11. The surrounding-determination information142includes an image information1421, a travelling information1422and miscellaneous information1423. These information may be entirely or partially processed.

As illustrated inFIG. 3, the surrounding images captured will be broken down into one or more sub-pixels5for analyzing. The contents in sub-pixels5, such as colors, lines, will be compared with the contents in the surrounding-determination information142to get one or more similarities. The similarities range from 0 (“Unlike”) to 1 (“Like”).

At step S13inFIG. 2, if the current surrounding-determination information142is sufficient to determine the grass area3, then go to step S17. If a proportion of desirable sub-pixels5, where the similarities of these desirable sub-pixels exceed a predetermined similarity threshold, to all sub-pixels5is higher than a predetermined proportional threshold, then the surrounding-determination information142is sufficient to determine the grass area3. For example, a predetermined similarity threshold is 0.6 and a predetermined proportional threshold is 70%. If the similarity of one sub-pixel5exceeds 0.6, then the sub-pixel5will be determined as grass. Otherwise, if the similarity of a sub-pixel5is below 0.6, then the sub-pixel5will be determined non-grass. If the proportion of the desirable sub-pixels5to all sub-pixels5is higher than 70%, then the surrounding-determination information142is determined as sufficient to determine the grass area3.

If the surrounding-determination information142is insufficient to determine the grass area3, then go to step S14. At step S14, the display module110will indicate a needing of more surrounding-determination information142. At step S15, the user2presses a button (not shown in the figures) configured to initiate a human image registration process. When the user2appears in the range of view of the mower1, the mower1continues the human image registration process. The image capturing module11obtains human features141of the user2as a registered user's human features and stores the human features141in the storage module14. The human features141include but not limited to body detection information and cloths colors.

At step S16, the human image registration process is completed. The mower1will follow the user2to travel on the grass area3if the image capturing module11identifies the user2as a registered user after matching up the human features141. The surrounding-determination information142is adjusted by integrating the image information1421obtained by the image capturing module11, the travelling information1422obtained by the posture acquiring module17and miscellaneous information1423obtained by the distance-aware module18and the locating module19. Specifically, the image information1421includes but not limited to grass color information, wherein the grass color information preferably includes grass colors relative to various lighting environments and various types of grass. The posture acquiring module17includes but not limited to any combination or number of inertial measurement unit (IMU), gyroscope, speed sensor or accelerometer. The travelling information1422includes but not limited to three-axis attitude angle of the mower during traveling and/or a surface friction calculated based on one or more speed value obtained by the speed sensor while the mower is travelling. The distance-aware module18includes but not limited to any combination or number of ultrasonic distance device, infrared distance device and/or laser detection and measurement device. The locating module19includes but not limited to any combination or number of GPS, WIFI wireless location device (indoor) or Bluetooth beacons wireless location device (indoor). Miscellaneous information1423includes but not limited to location, time and obstacle.

While the foregoing is directed to embodiments of the mower using the image capturing module to register, identify and follow the user, the embodiments may be used with other types of sensors such as distance sensor to register, identify and follow the user.

While the foregoing is directed to embodiments of the mower using the human features to follow a user to travel on the grass area, the embodiments may be used with other types of remote control device to control the mower to traveling on the grass area.

At step S16, having obtained the image information, the travelling information and miscellaneous information under current environment, the mower1determines the grass area accurately based on the real-world grass colors and/or the surface friction. In one embodiment, the operation module13adjusts the surrounding-determination information142based on the grass colors information obtained at a closest time. Then the process returns to step S12and S13. The mower1decides whether the surrounding-determination information142is sufficient to determine the grass area3. If sufficient, go to step S17.

At step S17, the mower1initiates the traveling control module12and the blade control module15. The traveling control module12controls the driving motor to move the mower1on the grass area3and the blade control module15controls the mower1to cut grass.

As shown inFIG. 4A, the image capturing module11is lifted up while the mower1performs a function of cutting grass without a predetermined boundary. Camera on the image capturing module11may be a monocular camera, a binocular camera, a fish-eye camera or a stereo camera. As shown inFIG. 4B, the image capturing module11comprises a lifting cover that is configured to lift up the image capturing module11outside the body of the mower1and retract the image capturing module11inside the body. The image capturing module11will be retracted inside the body to keep the camera lens clean while the mower1does not perform the function of cutting grass without the predetermined boundary. The mower1may be closed, standby or in other status. The image capturing module11is capable to be retracted by manual or when the mower is autonomously navigated with GPS.

FIG. 5is a flow chart of a method of machine learning.FIG. 6is a schematic view of the mower using the method of machine learning. At step S21, the mower1initiates the machine learning module111. The machine learning module111is initiated at a predetermined time, by an activity or by manual direction.

At step S22, the mower1initiates the human image registration process. The image capturing module11obtains the human features141of the user2and stores the human features141in the storage module14. The human features141include but not limited to body detection information, shape information and cloths colors information.

At step S23, the human image registration process is completed. The mower1will follow the user2based on the human features141to travel on the grass area3. The mower1will use the image capturing module11to obtain the image information1421, use the posture acquiring module17to obtain the travelling information1422and use the distance-aware module18and the locating module19to obtain miscellaneous information1423.

At step S24, the machine learning module111adjusts the surrounding-determination information142by adjusting at least one weight of at least one neural network node using the obtained image information1421, the obtained travelling information1422and obtained miscellaneous information1423. As shown inFIG. 6, the mower1adjusts at least one weight of at least one neural network node while the mower1follows the user to travel on the grass area. The image of grass area that the user has traveled on will be broken down into sub pixels51which will become materials for self learning and training of neural network.

Embodiments of the disclosure include a smart lawn mower to determine a grass area accurately and cut grass without predetermining a boundary based on the image information of the grass area under current environment captured by an image capturing module. The machine learning module strengthens outcomes of determining.