KR102500525B1 - Moving robot - Google Patents

Abstract

A mobile robot according to an aspect of the present invention includes a traveling unit for moving a main body, a light emitting unit disposed below the main body, and a light emitting unit for outputting light and a light receiving unit for receiving reflected light, and a sensor disposed spaced apart from the light emitting unit and the light receiving unit. A cliff sensor including a window, and, if the data obtained from the cliff sensor satisfies a set dust contamination condition, increase the dust contamination count by 1, and if the dust contamination count is greater than a threshold value, control to stop the movement It may include a control unit to.

Description

Moving robot {Moving robot}

The present invention relates to a mobile robot and a control method thereof, and more particularly, to a mobile robot capable of recognizing a precipice and driving safely, and a control method thereof.

Robots have been developed for industrial use and have been in charge of a part of factory automation. In recent years, the field of application of robots has been further expanded, and medical robots, space robots, etc. have been developed, and household robots that can be used at home are also being made. Among these robots, those capable of driving on their own are called mobile robots.

A representative example of a mobile robot used in a home is a robot cleaner. The robot cleaner is a device that cleans a corresponding area by sucking in dust or foreign substances while traveling in a certain area by itself.

The mobile robot is equipped with various sensors to detect various environments while driving and to respond appropriately thereto. Among the sensors, a cliff sensor detects and avoids a cliff to prevent the mobile robot from falling.

However, if the sensor window of the cliff sensor is contaminated, it becomes difficult to detect the cliff. In particular, due to the nature of the robot cleaner, dust is generated in the process of sweeping and inhaling dust, so the sensor window is often contaminated. If the sensor window is contaminated by dust, a cliff may not be detected and the robot cleaner may fall.

Therefore, there is a need for a method capable of detecting when the cliff sensor for detecting a cliff at the lower end of the robot cleaner is contaminated and preventing the robot cleaner from driving in a dangerous environment.

In the prior literature (US Patent Publication No. 9446521), the cliff sensor transmitter and receiver are separated from the instrument, and if the light emitted from the cliff sensor transmitter does not enter the receiver, it is judged as a cliff and avoided.

On the other hand, in the Cliff sensor module, in which the receiver and transmitter are not separated by equipment and one sensor window is disposed in front of the receiver and transmitter, when the sensor window is contaminated, the light emitted from the transmitter is reflected inside the sensor window and Since it is received, sensor contamination cannot be detected by the algorithm of the prior art literature.

An object of the present invention is to provide a technique for detecting contamination of a sensor window of a cliff sensor provided in a mobile robot.

An object of the present invention is to provide a mobile robot capable of preventing a fall due to entry into a cliff when a sensor window is contaminated.

An object of the present invention is to provide a mobile robot capable of preventing malfunction due to erroneous detection of contamination of a sensor window.

An object of the present invention is to provide an efficient mobile robot by preventing unnecessary avoidance driving.

In order to achieve the above or other object, the mobile robot according to one aspect of the present invention counts the number of times of satisfaction of the contamination condition when reflected light due to contamination of the sensor window is received, and when a certain value is reached, the contamination of the sensor window occurs. By discriminating, contamination of the sensor window can be accurately detected.

In order to achieve the above or other objects, the mobile robot according to one aspect of the present invention can detect contamination of the sensor window using the measurement distance and light intensity value of the cliff sensor even when reflected light due to contamination of the sensor window is received. .

In order to achieve the above or other objects, a mobile robot according to an aspect of the present invention includes a driving unit for moving a main body, a light emitting unit disposed below the main body and outputting light, a light receiving unit for receiving reflected light, and the light emitting unit. and a cliff sensor including a sensor window spaced apart from the light receiving unit, and when the data obtained from the cliff sensor satisfies a set dust contamination condition, the dust contamination count is increased by 1, and the dust contamination count is greater than the threshold value. If it is large, a control unit controlling to stop the movement may be included.

In addition, if the dust contamination count is greater than the threshold value, a dust contamination error may be generated.

The dust contamination condition is set based on the measurement distance value of the cliff sensor and the received light intensity value, and the control unit may increase the dust contamination count by 1 when both the measurement distance value condition and the light intensity value condition are satisfied. .

The control unit may decrease the dust contamination count by 1 when both the measurement distance value condition and the light intensity value condition are not satisfied.

When the dust contamination condition is satisfied, avoidance driving may be performed in which the driver changes direction so as not to move in the current driving direction.

In addition, the avoidance driving may be performed only when the maximum value among the latest N measured distance values satisfies the dust contamination condition.

The controller may increase the time count by 1 as time elapses until the first time period elapses.

The control unit may initialize the dust contamination count and the time count when the first time period elapses.

The controller may generate a dust contamination error when the dust contamination count is greater than a first threshold value before the first time period elapses.

The dust contamination count may increase by only 1 after the lapse of the second time period even if the dust contamination condition is satisfied a plurality of times during the second time period.

The controller may generate a dust contamination error when the dust contamination count is greater than a second threshold value before the first time period elapses.

The control unit may set a contamination detection flag to true when the dust contamination condition is satisfied.

The control unit may increase the dust contamination count by 1 when the second time period elapses and the contamination detection flag is true.

The control unit may set the contamination detection flag to false when the dust contamination error occurs.

If the dust contamination count is greater than a threshold value during a predetermined time period, the movement is stopped, and the threshold value may be set differently according to an increasing method of the dust contamination count.

If the dust contamination count is greater than a threshold value during a predetermined time period, the movement may be stopped, and the threshold value may be set differently according to the predetermined time period.

In order to achieve the above or other objects, a mobile robot according to an aspect of the present invention includes a driving unit for moving a main body, a light emitting unit disposed below the main body and outputting light, a light receiving unit for receiving reflected light, and the light emitting unit. and a cliff sensor including a sensor window spaced apart from the light receiving unit, and when the measured distance value and the received light amount value of the cliff sensor satisfy set conditions, the dust contamination count is increased by one, and the dust contamination count is a threshold value If it is larger than that, it may include a control unit that controls to stop the movement.

According to at least one of the embodiments of the present invention, contamination of the sensor window can be accurately detected.

In addition, according to at least one of the embodiments of the present invention, it is possible to prevent a fall due to entering a cliff in a state in which the sensor window is contaminated.

In addition, according to at least one of the embodiments of the present invention, malfunction due to erroneous detection of contamination of the sensor window can be prevented.

In addition, according to at least one of the embodiments of the present invention, efficient driving is possible by preventing unnecessary avoidance driving.

Meanwhile, other various effects will be disclosed directly or implicitly in detailed descriptions according to embodiments of the present invention to be described later.

1 is a perspective view showing a mobile robot and a charging stand for charging the mobile robot according to an embodiment of the present invention.
FIG. 2 is a view showing an upper surface of the mobile robot shown in FIG. 1;
FIG. 3 is a view showing the front of the mobile robot shown in FIG. 1;
FIG. 4 is a diagram illustrating a bottom portion of the mobile robot shown in FIG. 1;
5 is a block diagram showing a control relationship between major components of a mobile robot according to an embodiment of the present invention.
6 is a view referenced for description of a cliff sensor of a mobile robot according to an embodiment of the present invention.
7 is a view referenced for description of contamination of the sensor window of the cliff sensor.
8 is a flowchart illustrating a control method of a mobile robot according to an embodiment of the present invention.
9 to 11 are diagrams referenced for description of sensor window contamination according to an embodiment of the present invention.
12 is a flowchart illustrating a control method of a mobile robot according to an embodiment of the present invention.
13 is a diagram referenced for description of excessive avoidance driving.
14 is a flowchart illustrating a control method of a mobile robot according to an embodiment of the present invention.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings. However, it goes without saying that the present invention is not limited to these embodiments and can be modified in various forms.

On the other hand, the suffixes "module" and "unit" for components used in the following description are simply given in consideration of the ease of preparation of this specification, and do not themselves give a particularly important meaning or role. Accordingly, the “module” and “unit” may be used interchangeably.

Also, in this specification, terms such as first and second may be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish one element from another.

The mobile robot 100 according to an embodiment of the present invention refers to a robot capable of moving by itself using wheels, etc., and may be a home helper robot or a robot vacuum cleaner. Hereinafter, a robot cleaner having a cleaning function among mobile robots will be described as an example with reference to the drawings, but the present invention is not limited thereto.

1 is a perspective view showing a mobile robot and a charging stand for charging the mobile robot according to an embodiment of the present invention.

FIG. 2 is a view showing the top of the mobile robot shown in FIG. 1, FIG. 3 is a view showing the front of the mobile robot shown in FIG. 1, and FIG. 4 shows the bottom of the mobile robot shown in FIG. it is one degree

5 is a block diagram showing a control relationship between major components of a mobile robot according to an embodiment of the present invention.

Referring to FIGS. 1 to 5 , the mobile robot 100 includes a driving unit 160 that moves the main body 110 . The driving part 160 includes at least one driving wheel 136 for moving the main body 110 . The driving unit 160 includes a driving motor (not shown) connected to the driving wheel 136 to rotate the driving wheel. For example, the driving wheels 136 may be provided on the left and right sides of the main body 110, respectively, and are hereinafter referred to as a left wheel 136(L) and a right wheel 136(R), respectively.

The left wheel 136(L) and the right wheel 136(R) may be driven by a single drive motor, but the left wheel drive motor and the right wheel 136(R) driving the left wheel 136(L) as needed ) may be provided respectively with a right wheel drive motor for driving. The running direction of the main body 110 can be switched to the left or right by making a difference between the rotation speeds of the left wheel 136 (L) and the right wheel 136 (R).

The mobile robot 100 includes a service unit 150 for providing predetermined services. In FIGS. 1 to 5 , the present invention is described as an example in which the service unit 150 performs cleaning work, but the present invention is not limited thereto. For example, the service unit 150 may be provided to provide users with housekeeping services such as cleaning (sweeping, suction cleaning, mopping, etc.), dishwashing, cooking, laundry, garbage disposal, and the like. As another example, the service unit 150 may perform a security function of detecting a surrounding external intruder or dangerous situation.

The mobile robot 100 may clean the floor by the service unit 150 while moving in the driving area. The service unit 150 includes a suction device for sucking foreign substances, brushes 184 and 185 for sweeping, a dust bin (not shown) for storing foreign substances collected by the suction device or brush, and/or a mop for wiping. A part (not shown) may be included.

A suction port 150h through which air is sucked may be formed on the bottom of the main body 110, and a suction device (not shown) provides a suction force so that air can be sucked through the suction hole 150h in the main body 110. ), and a dust bin (not shown) for collecting dust sucked together with air through the suction port 150h may be provided.

The main body 110 may include a case 111 forming a space in which various parts constituting the mobile robot 100 are accommodated. An opening for inserting and removing the dust bin may be formed in the case 111 , and a dust bin cover 112 that opens and closes the opening may be rotatably provided with respect to the case 111 .

A roll-type main brush 154 having brushes exposed through the suction port 150h, and an auxiliary brush 155 located on the front side of the bottom surface of the main body 110 and having a brush made of a plurality of wings extending radially may be provided. The rotation of the brushes 154 and 155 separates dust from the floor in the driving area, and the dust separated from the floor is sucked through the suction port 150h and collected in a dust bin.

The battery 138 supplies power necessary for the overall operation of the mobile robot 100 as well as the driving motor. When the battery 138 is discharged, the mobile robot 100 may return to the charging station 200 for charging, and during this return driving, the mobile robot 100 moves to the charging station 200 by itself can detect

The charging station 200 may include a signal transmission unit (not shown) that transmits a predetermined return signal. The return signal may be an ultrasonic signal or an infrared signal, but is not necessarily limited thereto.

The mobile robot 100 may include a signal detector (not shown) for receiving a return signal. The charging station 200 may transmit an infrared signal through a signal transmission unit, and the signal detection unit may include an infrared sensor for detecting the infrared signal. The mobile robot 100 moves to the position of the charging station 200 according to the infrared signal transmitted from the charging station 200 and docks with the charging station 200 . Due to this docking, charging is performed between the charging terminal 133 of the mobile robot 100 and the charging terminal 210 of the charging base 200.

The mobile robot 100 may include a sensing unit 170 that senses internal/external information of the mobile robot 100 .

For example, the sensing unit 170 may include one or more sensors 171 and 175 to detect various types of information about the driving area and an image acquisition unit 120 to acquire image information about the driving area. Depending on the embodiment, the image acquisition unit 120 may be provided separately outside the sensing unit 170 .

The mobile robot 100 may map a driving area through information detected by the sensing unit 170 . For example, the mobile robot 100 may perform vision-based location recognition and map creation based on the ceiling image of the driving area acquired by the image acquisition unit 120 . In addition, the mobile robot 100 may perform location recognition and map generation based on a LiDAR (Light Detection And Ranging) sensor 175 using a laser.

More preferably, the mobile robot 100 according to the present invention effectively combines vision-based position recognition using a camera and lidar-based position recognition technology using a laser to recognize a position that is robust against environmental changes such as changes in illumination and position of an object. and map creation.

Meanwhile, the image acquisition unit 120 captures a driving area and may include one or more camera sensors that acquire an image of the outside of the main body 110 .

Also, the image acquisition unit 120 may include a camera module. The camera module may include a digital camera. A digital camera includes an image sensor (eg, CMOS image sensor) including at least one optical lens and a plurality of photodiodes (eg, pixels) forming images by light passing through the optical lens; A digital signal processor (DSP) for composing an image based on signals output from the photodiodes may be included. The digital signal processor can create not only still images, but also moving images composed of frames composed of still images.

In this embodiment, the image acquisition unit 120 is provided with a front camera sensor 120a provided to acquire an image of the front of the main body 110 and an upper surface of the main body 110 to obtain an image of the ceiling in the driving area. An upper camera sensor 120b is provided, but the location and shooting range of the image acquisition unit 120 are not necessarily limited thereto.

For example, the mobile robot 100 may include only an upper camera sensor 120b that acquires an image of a ceiling in a driving area, and may perform vision-based location recognition and driving.

Alternatively, the image acquisition unit 120 of the mobile robot 100 according to an embodiment of the present invention includes a camera sensor (not shown) disposed obliquely with respect to one surface of the main body 110 and configured to capture both the front and the upper directions. can include In other words, it is possible to photograph both the front and the upper direction with a single camera sensor. In this case, the controller 140 may separate a front image and an upper image from the image captured by the camera based on the angle of view. The separated front image may be used for vision-based object recognition like the image acquired by the front camera sensor 120a. In addition, the separated upper image can be used for vision-based location recognition and driving like the image acquired by the upper camera sensor 120b.

The mobile robot 100 according to the present invention may perform vision slam recognizing a current location by comparing a surrounding image with image-based pre-stored information or by comparing obtained images.

Meanwhile, the image acquisition unit 120 may also include a plurality of front camera sensors 120a and/or upper camera sensors 120b. Alternatively, the image acquisition unit 120 may also include a plurality of camera sensors (not shown) configured to photograph both the front and the upper directions.

In the case of this embodiment, a camera is installed in some parts (eg, front, rear, bottom) of the mobile robot, and captured images can be continuously acquired during cleaning. Several such cameras may be installed for each part for shooting efficiency. The image captured by the camera can be used to recognize the type of material such as dust, hair, floor, etc. present in the space, whether to clean, or to confirm the cleaning time.

The front camera sensor 120a may capture a situation of an obstacle or a cleaning area existing in front of the driving direction of the mobile robot 100 .

According to an embodiment of the present invention, the image acquisition unit 120 may acquire a plurality of images by continuously photographing the surroundings of the main body 110, and the obtained plurality of images may be stored in the storage unit 130. can

The mobile robot 100 may increase the accuracy of obstacle recognition by using a plurality of images or by selecting one or more images from among the plurality of images and using effective data.

The sensing unit 170 may include a lidar sensor 175 that obtains topographical information of the outside of the main body 110 using a laser.

The lidar sensor 175 outputs a laser to provide information such as the distance, position direction, and material of an object reflecting the laser, and may obtain topographical information of a driving area. The mobile robot 100 may obtain 360-degree geometry information using the lidar sensor 175.

The mobile robot 100 according to an embodiment of the present invention may generate a map by recognizing distances, positions, and directions of objects sensed by the lidar sensor 175.

The mobile robot 100 according to an embodiment of the present invention may obtain terrain information of a driving area by analyzing a laser reception pattern, such as a time difference or signal strength of a laser reflected and received from the outside. In addition, the mobile robot 100 may generate a map using terrain information acquired through the lidar sensor 175.

For example, the mobile robot 100 according to the present invention compares the surrounding terrain information obtained from the current location through the lidar sensor 175 with previously stored terrain information based on the lidar sensor or compares the obtained terrain information. It can perform lidar slam recognizing the current location.

More preferably, the mobile robot 100 according to the present invention effectively combines vision-based position recognition using a camera and lidar-based position recognition technology using a laser to provide a position that is robust to environmental changes such as changes in illumination and position of objects. It can perform recognition and map creation.

Meanwhile, the sensing unit 170 may include sensors 171, 172, and 179 that sense various data related to the operation and state of the mobile robot.

For example, the sensing unit 170 may include an obstacle detection sensor 171 that detects a forward obstacle. In addition, the sensing unit 170 may further include a lower camera sensor 179 that acquires an image of the floor.

In addition, the sensing unit 170 may include a cliff sensor 172 disposed below the main body 110 and detecting the presence or absence of a cliff on the floor within the driving area.

The cliff sensor 172 may include a light output unit (see 610 in FIG. 6 ) that outputs light toward the floor and a light receiver (see 620 in FIG. 6 ) that receives light reflected from the floor. The cliff sensor 172 may measure the distance by the time difference between the signals returned to the light receiving unit 620 .

The cliff sensor 172 detects the distance from the floor, and the control unit 140 determines that the distance from the floor is greater than the set distance or the reflected light is not received, and controls the corresponding operation to be performed. there is.

Also, the cliff sensor 172 may detect a reflection amount of light reflected from the floor. Specifically, the light receiving unit 620 may measure the amount of light returned, the illuminance, and the like, and obtain the reflectance compared to the light emitted from the light exiting unit.

In addition, the cliff sensor 172 may include a sensor window (see 630 in FIG. 6 ) spaced apart from the light emitting part 610 and the light receiving part 620 . The sensor window 630 may be disposed in front of the light emitting part 610 and the light receiving part 620 at a predetermined interval.

The sensor window 630 is made of a transparent material, and light emitted from the light emitting unit 610 and light reflected from the floor may pass therethrough.

The cliff sensor 172 may transmit acquired data to the controller 140 . For example, the measured distance value and the received light intensity value may be transmitted to the controller 140 . The control unit 140 may determine whether or not there is a cliff, a distance from the floor, and the material of the floor based on the sensing data of the cliff sensor 172 and control the driving unit 160 .

In addition, the control unit 140 may determine whether the sensor window 630 is contaminated based on the sensing data of the cliff sensor 172 . Determination of contamination of the sensor window 630 will be described later in detail with reference to FIGS. 6 to 14 .

Referring to FIGS. 1 and 3 , the obstacle detection sensor 171 may include a plurality of sensors installed at regular intervals on the outer circumferential surface of the mobile robot 100 .

The obstacle detection sensor 171 may include an infrared sensor, an ultrasonic sensor, an RF sensor, a geomagnetic sensor, a Position Sensitive Device (PSD) sensor, and the like.

Meanwhile, the location and type of sensors included in the obstacle detection sensor 171 may vary depending on the type of mobile robot, and the obstacle detection sensor 171 may include more various sensors.

The obstacle detection sensor 171 is a sensor that detects a distance to an indoor wall or an obstacle, and the present invention is not limited to that type, but an ultrasonic sensor will be described below as an example.

The obstacle detection sensor 171 detects an object, particularly an obstacle, existing in the traveling (moving) direction of the mobile robot and transmits obstacle information to the control unit 140. That is, the obstacle detection sensor 171 can detect the moving path of the mobile robot, protruding objects in front or on the side, furniture, furniture, wall surfaces, wall corners, etc., and transmit the information to the control unit.

In this case, the controller 140 may detect the position of the obstacle based on at least two or more signals received through the ultrasonic sensor, and control the movement of the mobile robot 100 according to the detected position of the obstacle.

Depending on the embodiment, the obstacle detection sensor 131 provided on the outer surface of the case 110 may include a transmitter and a receiver.

For example, in the ultrasonic sensor, at least one transmitting unit and at least two receiving units may be provided so that they are crossed with each other. Accordingly, signals can be radiated at various angles and signals reflected by obstacles can be received at various angles.

Depending on the embodiment, the signal received from the obstacle detection sensor 171 may undergo signal processing such as amplification and filtering, and then the distance and direction to the obstacle may be calculated.

Meanwhile, the sensing unit 170 may further include a driving detection sensor that detects a driving motion of the mobile robot 100 according to driving of the main body 110 and outputs motion information. As the driving detection sensor, a gyro sensor, a wheel sensor, an acceleration sensor, or the like may be used. Data detected by at least one of the driving sensors or data calculated based on data detected by at least one of the driving sensors may constitute odometry information.

The gyro sensor detects a rotation direction and a rotation angle when the mobile robot 100 moves according to the driving mode. The gyro sensor detects the angular velocity of the mobile robot 100 and outputs a voltage value proportional to the angular velocity. The controller 140 calculates the rotation direction and rotation angle using the voltage value output from the gyro sensor.

The wheel sensor is connected to the left wheel 136 (L) and the right wheel 136 (R) to detect the number of revolutions of the wheel. Here, the wheel sensor may be an encoder. The encoder detects and outputs the number of revolutions of the left wheel 136(L) and the right wheel 136(R).

The controller 140 may calculate the rotation speed of the left and right wheels using the number of revolutions. In addition, the controller 140 may calculate the rotation angle using a difference in rotation speeds between the left wheel 136 (L) and the right wheel 136 (R).

The acceleration sensor detects a change in speed of the mobile robot 100, for example, a change in the mobile robot 100 according to start, stop, direction change, collision with an object, and the like. The acceleration sensor is attached to a location adjacent to the main wheel or the auxiliary wheel, and can detect slippage or idling of the wheel.

In addition, the acceleration sensor may be built into the control unit 140 to detect a change in speed of the mobile robot 100 . That is, the acceleration sensor detects the amount of impact according to the speed change and outputs a corresponding voltage value. Accordingly, the acceleration sensor may perform a function of an electronic bumper.

The control unit 140 may calculate a change in position of the mobile robot 100 based on motion information output from the driving sensor. This position becomes a relative position corresponding to the absolute position using image information. The mobile robot can improve the performance of location recognition using image information and obstacle information through relative location recognition.

Meanwhile, the mobile robot 100 may include a power supply unit (not shown) having a rechargeable battery 138 to supply power to the robot cleaner.

The power supply unit supplies driving power and operating power to each component of the mobile robot 100, and when the remaining power is insufficient, it can be charged by receiving charging current from the charging station 200.

The mobile robot 100 may further include a battery detector (not shown) that detects the state of charge of the battery 138 and transmits the detection result to the control unit 140 . The battery 138 is connected to the battery detection unit, and the battery remaining amount and charging state are transmitted to the controller 140 . The remaining battery capacity may be displayed on a screen of an output unit (not shown).

In addition, the mobile robot 100 includes a control unit 137 capable of inputting on/off or various commands. Various control commands necessary for the overall operation of the mobile robot 100 may be received through the control unit 137 . In addition, the mobile robot 100 may display reservation information, battery status, operation mode, operation status, error status, etc., including an output unit (not shown).

Referring to FIG. 5 , the mobile robot 100 includes a controller 140 that processes and determines various types of information, such as recognizing a current location, and a storage unit 130 that stores various types of data. In addition, the mobile robot 100 may further include a communication unit 190 that transmits and receives data with other devices.

Among the devices communicating with the mobile robot 100, an external terminal has an application for controlling the mobile robot 100, displays a map of a driving area to be cleaned by the mobile robot 100 through the execution of the application, and displays a map on the map. You can specify an area to clean in a specific area. The external terminal may include, for example, a remote control, a PDA, a laptop, a smart phone, a tablet, etc. loaded with an application for setting a map.

The external terminal can communicate with the mobile robot 100 to display the current location of the mobile robot along with a map, and information on a plurality of areas can be displayed. In addition, the external terminal updates and displays its location according to the movement of the mobile robot.

The control unit 140 controls the overall operation of the mobile robot 100 by controlling the sensing unit 170, the manipulation unit 137, and the traveling unit 160 constituting the mobile robot 100.

The storage unit 130 records various information necessary for controlling the mobile robot 100, and may include a volatile or non-volatile recording medium. The recording medium stores data that can be read by a microprocessor, and is not limited to its type or implementation method.

Also, a map for a driving area may be stored in the storage unit 130 . The map may be input by an external terminal or server capable of exchanging information with the mobile robot 100 through wired or wireless communication, or may be generated by the mobile robot 100 through self-learning.

The location of rooms within the driving area may be indicated on the map. Also, the current location of the mobile robot 100 may be displayed on a map, and the current location of the mobile robot 100 on the map may be updated in the process of driving. The external terminal stores the same map as the map stored in the storage unit 130 .

The storage unit 130 may store cleaning history information. Such cleaning history information may be generated whenever cleaning is performed.

The map for the driving area stored in the storage unit 130 includes a navigation map used for driving during cleaning, a simultaneous localization and mapping (SLAM) map used for location recognition, an obstacle, etc. It may be a learning map used for learning and cleaning by storing corresponding information when collided with, a global location map used for global location recognition, an obstacle recognition map in which information on recognized obstacles is recorded, and the like.

Meanwhile, as described above, maps may be stored and managed by dividing them into the storage unit 130 for each purpose, but the maps may not be clearly classified for each purpose. For example, a plurality of pieces of information may be stored in one map so that they can be used for at least two purposes.

The control unit 140 may include a driving control module 141, a location recognition module 142, a map generation module 143, and an obstacle recognition module 144.

1 to 5, the driving control module 141 controls driving of the mobile robot 100, and controls driving of the driving unit 160 according to driving settings. In addition, the driving control module 141 may determine the driving path of the mobile robot 100 based on the operation of the driving unit 160 . For example, the driving control module 141 can determine the current or past moving speed of the mobile robot 100, the distance traveled, etc. based on the rotational speed of the driving wheels 136, and each driving wheel 136 ( According to the rotation direction of L) and 136(R)), the current or past direction change process can also be grasped. Based on the driving information of the mobile robot 100 identified in this way, the location of the mobile robot 100 on the map may be updated.

The map generation module 143 may generate a map of the driving area. The map generation module 143 may create a map by processing the image obtained through the image acquisition unit 120 . For example, a map corresponding to a driving area and a cleaning map corresponding to a cleaning area can be created.

In addition, the map generation module 143 can recognize the global location by processing the image acquired through the image acquisition unit 120 at each location and linking it with a map.

In addition, the map generation module 143 may create a map based on information obtained through the lidar sensor 175 and recognize a location based on the information obtained through the lidar sensor 175 at each location. there is.

More preferably, the map generation module 143 may create a map based on information obtained through the image acquisition unit 120 and the LIDAR sensor 175 and perform location recognition.

The location recognition module 142 estimates and recognizes the current location. The location recognition module 142 estimates the current location even when the location of the mobile robot 100 suddenly changes by identifying the location in conjunction with the map generation module 143 using the image information of the image acquisition unit 120. can be recognized.

The mobile robot 100 can recognize the location during continuous driving through the location recognition module 142, and also includes the driving control module 141, the map generation module 143, and the obstacle recognition module (without the location recognition module 142). 144), the map can be learned and the current location can be estimated.

While the mobile robot 100 is driving, the image acquisition unit 120 acquires images around the mobile robot 100 . Hereinafter, an image acquired by the image acquisition unit 120 is defined as an 'obtained image'.

The obtained image includes various features such as lights located on the ceiling, edges, corners, blobs, and ridges.

The map generating module 143 detects features from each of the acquired images. Various methods (Feature Detection) of detecting a feature from an image are well known in the field of computer vision technology. Several feature detectors suitable for detection of these features are known. For example, Canny, Sobel, Harris & Stephens/Plessey, SUSAN, Shi & Tomasi, Level curve curvature, FAST, Laplacian of Gaussian, Difference of Gaussians, Determinant of Hessian, MSER, PCBR, Gray-level blobs detector, etc.

The map generating module 143 calculates a descriptor based on each feature point. The map generating module 143 may convert a feature point into a descriptor using a Scale Invariant Feature Transform (SIFT) technique for feature detection. The descriptor may be expressed as an n-dimensional vector.

SIFT can detect features that are invariant with respect to changes in scale, rotation, and brightness of the subject to be photographed, so that even if the same area is photographed with different postures of the mobile robot 100, it is invariant (i.e., rotation invariant (Rotation)). -invariant)) features can be detected. Of course, it is not limited thereto, and various other techniques (eg, HOG: Histogram of Oriented Gradient, Haar feature, Fems, LBP: Local Binary Pattern, MCT: Modified Census Transform) may be applied.

The map generation module 143 classifies at least one descriptor for each acquired image into a plurality of groups according to a predetermined sub-classification rule, based on the descriptor information obtained through the acquired image of each location, and the same group according to a predetermined sub-representative rule. The descriptors included in may be converted into sub-representative descriptors, respectively.

As another example, all descriptors gathered from acquired images in a predetermined area, such as a room, are classified into a plurality of groups according to a predetermined sub-classification rule, and the descriptors included in the same group according to the predetermined sub-representation rule are respectively classified as sub-representative descriptors. can also be converted to

The map generation module 143 may obtain the feature distribution of each location through the above process. Each location feature distribution can be expressed as a histogram or an n-dimensional vector. As another example, the map generating module 143 may estimate an unknown current location based on a descriptor calculated from each feature point without passing through a predetermined subclassification rule and a predetermined subrepresentation rule.

In addition, when the current location of the mobile robot 100 becomes unknown due to location jumping or the like, the current location can be estimated based on data such as a pre-stored descriptor or sub-representative descriptor.

The mobile robot 100 acquires an acquired image through the image acquisition unit 120 at an unknown current location. Through the video, various features such as lights located on the ceiling, edges, corners, blobs, and ridges are confirmed.

The location recognition module 142 detects features from the acquired image. Descriptions of various methods for detecting features from images in the field of computer vision technology and various feature detectors suitable for detecting these features have been described above.

The location recognition module 142 calculates a recognition descriptor through a recognition descriptor calculation step based on each recognition feature point. At this time, the recognition feature point and the recognition descriptor are for explaining the process performed by the location recognition module 142, and are intended to be distinguished from terms describing the process performed by the map generation module 143. However, it is merely that the characteristics of the external world of the mobile robot 100 are defined in different terms.

The location recognition module 142 may convert the recognition feature point into a recognition descriptor using a Scale Invariant Feature Transform (SIFT) technique to detect this feature. The recognition descriptor may be expressed as an n-dimensional vector.

As described above, SIFT selects easily identifiable feature points such as corner points in the acquired image, and then determines the distribution characteristics of the brightness gradient of pixels belonging to a certain area around each feature point (direction of brightness change and rapid degree of change). ), it is an image recognition technique that obtains an n-dimensional vector with the rapid degree of change in each direction as a numerical value for each dimension.

The location recognition module 142 is based on at least one recognition descriptor information obtained through an acquired image of an unknown current location, and location information (eg, feature distribution of each location) to be compared according to a predetermined sub-conversion rule. It is converted into information (lower recognition feature distribution) that can be compared with .

According to a predetermined subcomparison rule, each location feature distribution may be compared with each recognition feature distribution to calculate each degree of similarity. A similarity (probability) may be calculated for each location corresponding to each location, and a location where the highest probability is calculated among them may be determined as the current location.

In this way, the controller 140 may divide the driving area and create a map composed of a plurality of areas, or recognize the current location of the main body 110 based on a previously stored map.

In addition, the control unit 140 may combine information obtained through the image acquisition unit 120 and the LIDAR sensor 175 to create a map and perform location recognition.

When a map is generated, the control unit 140 may transmit the generated map to an external terminal or server through the communication unit 190 . Also, as described above, the controller 140 may store the map in the storage unit when the map is received from an external terminal or server.

Also, when the map is updated while driving, the controller 140 transmits the updated information to the external terminal so that the external terminal and the map stored in the mobile robot 100 are the same. As the maps stored in the external terminal and the mobile robot 100 remain the same, the mobile robot 100 can clean the designated area in response to a cleaning command from the mobile terminal, and the current location of the mobile robot is displayed in the external terminal. so that it can be displayed.

In this case, the map may divide the cleaning area into a plurality of areas, include a connection passage connecting the plurality of areas, and include information about obstacles in the area.

When a cleaning command is input, the controller 140 determines whether the location on the map matches the current location of the mobile robot. The cleaning command may be input from a remote control, a control panel, or an external terminal.

If the current location does not match the location on the map, or if the current location cannot be confirmed, the controller 140 recognizes the current location and recovers the current location of the mobile robot 100, and then based on the current location The driving unit 160 may be controlled to move to a designated area.

If the current location does not match the location on the map or if the current location cannot be confirmed, the location recognition module 142 uses the acquired image input from the image acquisition unit 120 and/or the LIDAR sensor 175. The current location can be estimated based on the map by analyzing the obtained terrain information. In addition, the obstacle recognition module 144 or the map generation module 143 may also recognize the current location in the same way.

After recognizing the position and restoring the current position of the mobile robot 100, the driving control module 141 calculates a driving route from the current position to the designated area and controls the driving unit 160 to move to the designated area.

When receiving cleaning pattern information from the server, the driving control module 141 may divide the entire driving area into a plurality of areas and set one or more areas as a designated area according to the received cleaning pattern information.

In addition, the driving control module 141 may calculate a driving route according to the received cleaning pattern information, drive along the driving route, and perform cleaning.

The controller 140 may store the cleaning record in the storage unit 130 when cleaning of the set designated area is completed.

In addition, the control unit 140 may transmit the operating state or cleaning state of the mobile robot 100 to an external terminal or server through the communication unit 190 at predetermined intervals.

Accordingly, the external terminal displays the location of the mobile robot along with a map on the screen of the running application based on the received data, and also outputs information on the cleaning status.

The mobile robot 100 according to an embodiment of the present invention moves in one direction until an obstacle or wall is detected, and when the obstacle recognition module 144 recognizes the obstacle, the mobile robot 100 travels in a straight line, turns, etc. according to the attributes of the recognized obstacle. patterns can be determined.

For example, if the attribute of the recognized obstacle is a type of obstacle that can be crossed, the mobile robot 100 can continue to move forward. Alternatively, if the attribute of the recognized obstacle is a type of obstacle that cannot be crossed, the mobile robot 100 rotates and moves a certain distance, moves again in the opposite direction to the initial movement direction to the distance at which the obstacle is detected, and travels in a zigzag pattern. can

The mobile robot 100 according to an embodiment of the present invention may perform machine learning-based human and object recognition and avoidance.

The controller 140 controls the driving of the driving unit 160 based on the obstacle recognition module 144 for recognizing an obstacle pre-learned by machine learning in an input image and the attribute of the recognized obstacle. A driving control module 141 for controlling may be included.

The mobile robot 100 according to an embodiment of the present invention may include an obstacle recognition module 144 in which attributes of obstacles are learned through machine learning.

Machine learning means that a computer learns through data without a person directly instructing the computer with logic, and through this, the computer solves the problem on its own.

Deep Learning is. Based on artificial neural networks (ANNs) to construct artificial intelligence, it is an artificial intelligence technology that allows computers to learn like humans on their own without being taught by humans as a method of teaching computers how to think.

The artificial neural network (ANN) may be implemented in the form of software or hardware such as a chip.

The obstacle recognition module 144 may include an artificial neural network (ANN) in the form of software or hardware in which attributes of obstacles are learned.

For example, the obstacle recognition module 144 uses a deep neural network (DNN) such as a convolutional neural network (CNN), a recurrent neural network (RNN), and a deep belief network (DBN) learned by deep learning. can include

The obstacle recognition module 144 may determine attributes of obstacles included in input image data based on weights between nodes included in the deep neural network (DNN).

The control unit 140 may determine the attribute of the obstacle existing in the moving direction by using only a part of the image, not the entire image acquired by the image acquisition unit 120, particularly the front camera sensor 120a. .

Also, the driving control module 141 may control the driving of the driving unit 160 based on the attribute of the recognized obstacle.

Meanwhile, the storage unit 130 may store input data for obstacle attribute determination and data for learning the deep neural network (DNN).

The storage unit 130 may store the original image obtained by the image acquisition unit 120 and the extracted images from which a predetermined area is extracted.

Also, depending on the embodiment, weights and biases constituting the deep neural network (DNN) structure may be stored in the storage unit 130 .

Alternatively, according to embodiments, weights and biases constituting the deep neural network structure may be stored in an embedded memory of the obstacle recognition module 144 .

On the other hand, whenever the obstacle recognition module 144 extracts a partial region of the image acquired by the image acquisition unit 120, it performs a learning process using the extracted image as training data, or a predetermined number of After the above extracted images are obtained, a learning process may be performed.

That is, the obstacle recognition module 144 updates a deep neural network (DNN) structure such as a weight by adding a recognition result whenever an obstacle is recognized, or obtained after a predetermined number of training data is secured. A deep neural network (DNN) structure such as weights may be updated by performing a learning process with training data.

Alternatively, the mobile robot 100 may transmit the original image or the extracted image acquired by the image acquisition unit 120 to a predetermined server through the communication unit 190, and receive data related to machine learning from the predetermined server. there is.

In this case, the mobile robot 100 may update the obstacle recognition module 141 based on data related to machine learning received from the predetermined server.

Meanwhile, the mobile robot 100 may further include an output unit 180 to display predetermined information as an image or output sound.

The output unit 180 may include a display (not shown) that displays information corresponding to a user's command input, a processing result corresponding to the user's command input, an operation mode, an operation state, an error state, and the like as images.

Depending on the embodiment, the display may be configured as a touch screen by forming a mutual layer structure with a touch pad. In this case, a display composed of a touch screen may be used as an input device capable of inputting information by a user's touch in addition to an output device.

Also, the output unit 180 may include an audio output unit (not shown) that outputs an audio signal. Under the control of the control unit 140, the sound output unit outputs a notification message such as a warning sound, an operation mode, an operation state, an error state, information corresponding to a user's command input, a processing result corresponding to a user's command input, and the like as sound. can The audio output unit 181 may convert an electrical signal from the control unit 140 into an audio signal and output the converted audio signal. To this end, a speaker or the like may be provided.

FIG. 6 is a diagram referred to in the description of the cliff sensor of the mobile robot according to an embodiment of the present invention, and FIG. 7 is a diagram referred to in the description of contamination of the sensor window of the cliff sensor.

Referring to FIG. 6 , the cliff sensor 172 disposed below the main body 110 may include a light emitting unit 610 that outputs light and a light receiving unit 620 that receives reflected light.

The cliff sensor 172 measures the distance based on the time until the light output from the light output unit 610 is reflected from the floor 700 and received by the light receiver 620, and the measured distance value may be transmitted to the control unit 140. In addition, the cliff sensor 172 may transmit the amount of reflected light received from the light receiving unit 620 to the controller 140 .

On the other hand, the arrangement angle and light path of the cliff sensor 172 shown in FIGS. 6 and 7 are exemplary, and the measurement distance value measured by the cliff sensor 172 is measured in a direction perpendicular to the floor 700 It may be a measured value or a value measured in an inclined direction.

The control unit 140 may control the driving unit 160 for moving the main body 110 based on the sensing data of the cliff sensor 172 . For example, if reflected light is not received by the light receiving unit 620, the control unit 140 determines that it is a cliff. The driving unit 160 may be controlled to avoid driving so that the main body 100 does not enter the cliff. Here, avoidance driving may be performed in various patterns. For example, the mobile robot 100 can avoid a precipice area by temporarily stopping its movement, changing its direction and moving.

Meanwhile, the cliff sensor 172 may include a sensor window 630 spaced apart from the light emitting part 610 and the light receiving part 620 . The sensor window 630 is formed of a transparent material to transmit light while protecting the light emitting part 610 and the light receiving part 620 . The sensor window 630 is disposed in front of the light emitting part 610 and the light receiving part 620, and a body is disposed in the rear surface of the light emitting part 610 and the light receiving part 620, and inside the body Various circuits capable of supplying power to the light emitting unit 610 and the light receiving unit 620 and processing data may be accommodated. In some cases, the sensor window 630 may be independently disposed outside the front surface of the cliff sensor 172 .

The sensor window 630 is disposed in front of the light emitting unit 610 to output light, and may pass the light output by the light emitting unit 610 . The sensor window 630 may be disposed in front of the light receiving unit 620 to pass reflected light.

Referring to FIG. 6 , the light emitting unit 610 and the light receiving unit 620 are not separated by a mechanism such as a partition wall, and the light output from the light emitting unit 610 through one sensor window 630 and the light receiving unit The reflected light received at 620 passes through.

Referring to FIG. 7 , when the sensor window 730 is contaminated by small substances such as dust or sawdust, light may be reflected from the contaminated sensor window 730 instead of the floor 700 . Accordingly, the light emitted from the light emitting unit 610 is reflected by the contaminated sensor window 730 and the reflected light is received by the light receiving unit 620 .

If the light receiving unit 620 does not receive the reflected light, the controller 140 may determine that a cliff is a cliff and control stopping and/or avoiding driving. However, as shown in FIG. 7 , when light is reflected from the contaminated sensor window 730 instead of the floor 700 and the light receiver 620 receives the reflected light, it is not recognized as a cliff area and the mobile robot 100 continues and will move If the sensor window 730 is contaminated even in an actual cliff area, the mobile robot 100 may fall onto the cliff.

In addition, when the sensor window 630 is contaminated by small substances such as dust or sawdust, the measurement distance of the cliff sensor 172 is not normally output, and the mobile robot 100 may fall.

In order to prevent this, according to an embodiment of the present invention, the mobile robot 100 detects a state in which the sensor window 630 is contaminated, and travels in the corresponding direction when the sensor window 630 is contaminated. may not

If the light emitting part 610 and the light receiving part 620 are separated as an appliance, even if the light output from the light emitting part 610 is reflected from the sensor window protecting the light emitting part 610, the light receiving part ( 620, the mobile robot 100 can determine it as a cliff and avoid it. That is, the same avoidance logic can be performed because the reflected light is not received the same as the contamination of the sensor window and the cliff. However, since it is not possible to discriminate between a case of sensor window contamination and not an actual cliff, driving may be performed inefficiently.

Therefore, the present invention proposes a method for preventing a fall accident and efficiently driving without changing the structure of the cliff sensor 172 .

The mobile robot 100 according to one aspect of the present invention counts the number of times the contamination condition is satisfied when the reflected light due to contamination of the sensor window 630 is received, and when a certain value is reached, the contamination of the sensor window 630 By determining as , contamination of the sensor window 630 can be accurately detected.

The mobile robot 100 according to one aspect of the present invention, even when reflected light due to the contamination of the sensor window 630 is received, uses the measured distance and the amount of light of the cliff sensor 172 to avoid contamination of the sensor window 630. can detect

The controller 140 may increase the dust contamination count by 1 when the data obtained from the cliff sensor 172 satisfies the set dust contamination condition. The controller 140 may increase the dust contamination count by 1 whenever a dust contamination condition is satisfied.

In this specification, contamination of the sensor window 630 is described centering on dust, which is a representative cause of contamination of the sensor window 630, but the present invention is not limited thereto. Therefore, in the present specification, terms such as 'dust contamination condition' and 'dust contamination count' may be replaced with 'contamination condition' and 'contamination count', respectively, and the present invention is a sensor due to various causes such as dust, sawdust, mud, etc. It goes without saying that it can be applied to window 630 contamination.

Meanwhile, the dust contamination condition may be set based on a measurement distance value of the cliff sensor 172 and a received light intensity value. That is, the dust contamination condition may include a measurement distance value condition and a received light intensity value condition.

When the sensor window 630 is contaminated, light output from the light emitting unit 610 is reflected from the contaminated sensor window 630 and the reflected light is received by the light receiving unit 620 . Accordingly, when the sensor window 630 is contaminated, the moving distance and required time are shorter than when the light goes to the floor 700 and is reflected and returned. Therefore, when the sensor window 630 is contaminated, since the measurement distance value of the cliff sensor 172 becomes small, the measurement distance value can be used as a major factor in determining contamination of the sensor window 630.

Meanwhile, even when a specific obstacle such as a threshold is detected by the cliff sensor 172, the measured distance value may be shorter than that of the floor. When the light is reflected from an obstacle such as a threshold higher than the floor 700 and returns, the moving distance and required time are shorter than when the light travels to the floor 700 and is reflected and returns. Therefore, in order to more accurately determine contamination of the sensor window 630, it is more desirable to further consider other conditions.

The amount of light reflected from the contaminated sensor window 630 is smaller than when the light travels to the floor 700 and is reflected back, and is smaller than when the light travels to the threshold and is reflected back. In addition, the amount of light reflected from the threshold is greater than that of the case where the light goes to the floor 700 and is reflected back. Therefore, the dust contamination condition may include a received light amount value condition.

According to an embodiment of the present invention, the dust pollution condition may include a measurement distance value condition and a received light intensity value condition. Also, the measurement distance value condition and the received light amount value condition may be AND conditions. Therefore, the controller 140 can determine that the dust contamination condition is satisfied only when both the measurement distance value condition and the received light amount value condition are satisfied. Accordingly, the controller 140 may increase the dust contamination count by one only when both the measurement distance value condition and the received light intensity value condition are satisfied.

Meanwhile, the control unit 140 increases the dust contamination count by 1 whenever a dust contamination condition is satisfied, and stops the movement of the mobile robot 100 when the dust contamination count is equal to or greater than a threshold. By doing so, accidents can be prevented.

It is dangerous for the mobile robot 100 to continue moving while the sensor window 630 is contaminated because it cannot detect the cliff. In addition, if the sensor window 630 is contaminated and continues to drive in the area, the sensor Window 630 may be further contaminated. Accordingly, the controller 140 may control the mobile robot 100 to perform avoidance driving avoiding a corresponding area when the dust contamination count is equal to or greater than a threshold.

In addition, the controller 140 may generate a dust contamination error indicating contamination of the sensor window 630 when the dust contamination count is equal to or greater than a threshold. Here, the occurrence of the dust contamination error is to generate a signal indicating contamination of the sensor window 630 . For example, the dust contamination error may be output through the output unit 180 or transmitted to an external device through the communication unit 190. In addition, the controller 140 may stop the movement of the mobile robot 100 according to occurrence of a dust contamination error. In addition, the controller 140 may control avoidance driving in the corresponding area according to occurrence of a dust pollution error.

Meanwhile, according to an embodiment, when the data obtained from the cliff sensor 172 satisfies the set dust pollution condition, the mobile robot 100 may stop once. When contamination of the sensor window 630 is detected, the moving mobile robot 100 avoids entering in the corresponding direction and may generate a contamination detection error if it continues. Accordingly, even if the sensor window 630 is contaminated, falling due to entry into the cliff area can be prevented.

According to an embodiment of the present invention, contamination of the sensor window 630 may be detected using the measurement distance and light intensity value of the cliff sensor 172 . In case of dust contamination, the distance value is smaller than the normally measured distance (the distance from the sensor to the floor) and the amount of light is also small. In addition, sensor contamination (a lot) and those close to the threshold can be distinguished by the light amount value.

On the other hand, even if the data sensed by the cliff sensor 172 satisfies the dust contamination condition, the control unit 140 does not immediately display an error and increases the dust contamination count. can cause Accordingly, when climbing an obstacle or driving in an environment similar to the contamination detection condition (dust contamination condition), it is possible to prevent an erroneous operation of a contamination detection error caused by temporarily satisfying the contamination detection condition.

On the other hand, even though the sensor window 630 is not contaminated, a contamination detection error may cause a stop situation during driving due to an erroneous operation.

In addition, when the sensor window 630 is completely contaminated and the measured distance value does not change even on a cliff, a fall may occur if an error occurs late.

Therefore, the mobile robot 100 performs a cliff avoidance motion so as not to travel in the corresponding direction when the dust contamination condition is satisfied, thereby preventing falling due to entry into the cliff in a contaminated state in which it is unknown whether it is a cliff area. .

For example, if raw data sensed by the cliff sensor 172 is generated at intervals of 20 ms, 3000 (50*60) raw data are generated during one minute. At this time, when one piece of raw data satisfies the dust contamination condition, avoidance driving is performed, and if generation of raw data satisfying the dust contamination condition continues for a predetermined time, an error may be generated.

8 is a flowchart illustrating a control method of a mobile robot according to an embodiment of the present invention.

Referring to FIG. 8 , the controller 140 may increase the dust contamination count by 1 whenever a dust contamination condition is satisfied (S810). As described above, the dust contamination condition may include a measurement distance value condition and a light intensity value condition. The controller 140 may increase the dust contamination count by 1 when both the measurement distance value condition and the light intensity value condition are satisfied (S810).

According to an embodiment, the control unit 140 may decrease the dust contamination count by 1 when both the measurement distance value condition and the light intensity value condition are not satisfied (S830). Accordingly, the dust contamination count is not initialized.

Meanwhile, if the dust contamination count is equal to or greater than a predetermined threshold (S840), the control unit 140 may generate an error and stop the movement (S850). If the cumulative dust contamination count does not reach the threshold value, no error occurs.

For example, if raw data sensed by the cliff sensor 172 is generated at intervals of 20 ms, 3000 (50*60) raw data are generated during one minute. At this time, if the generation of raw data continues for several seconds (S840), an error may occur (S850).

Meanwhile, the occurrence of an error and the stop of movement (S850) may be performed simultaneously, or one may be performed first and then the other may be performed.

Meanwhile, according to an embodiment, when the dust pollution condition is satisfied (S810), the controller 140 may control the vehicle to avoid traveling by changing direction so as not to move in the current driving direction. Accordingly, it is possible to prevent an accident from occurring even in a state where no dust contamination error occurs.

9 to 11 are diagrams referenced for description of sensor window contamination according to an embodiment of the present invention.

9 illustrates an experimental example of measured distance values measured by the cliff sensor 172 according to various types of dust and sawdust.

Referring to FIG. 9 , when the sensor window 630 is contaminated with dust or sawdust, the measured distance value decreases to a value smaller than the value a. However, since some data is greater than the value a, the measurement distance value condition can be set based on the value s including some data greater than the value a. If the value of b is set based on the value of b, which is greater than the value of s, since it is more difficult to satisfy the condition, contamination detection sensitivity may decrease.

On the other hand, due to the sensor measurement deviation, it may occur that a cliff is detected even under the same conditions.

It may be more desirable to modify the conditions so that the measurement distance of the cliff sensor 172 does not excessively satisfy the measurement distance value condition before the dust contamination error occurs.

For example, the condition may be modified to use only the maximum value among the last N measured distance values. Only the maximum value among the latest N measured distance values can be used, and only when the maximum value is less than the s value, it can be determined as a cliff caused by sensor contamination. Accordingly, the control unit 140 may control the avoidance driving to be performed only when the maximum value among the latest N measured distance values satisfies the dust pollution condition.

10 illustrates a case where raw data sensed by the cliff sensor 172 is generated at intervals of 20 ms and compared with the s value for each raw data, and FIG. 11 shows raw data sensed by the cliff sensor 172 When (Rawdata) occurs at 20 ms intervals, the case in which the maximum value among the latest N measured distance values is compared with the s value is exemplified. Since FIG. 11 uses the maximum value in a predetermined section, it can be seen that the measurement distance of the data used for contamination detection increases overall, and the section falling below the s value in FIG. 10 is greater than the s value in FIG. 11 . Accordingly, it is possible to reduce the excessive number of times of condition satisfaction determination.

When only the cliff avoidance motion is performed without the contamination error occurring in the cliff sensor 172, a large amount of cliff areas where the avoidance motion has been performed are registered in the map. In this case, since all areas around the registered cliff area are recognized as cliffs, driving may become impossible. If a cliff avoidance motion due to dust contamination of the sensor window 630 frequently occurs, a path to be driven is recognized as a cliff area and blocked, so that driving may be impossible.

According to an embodiment, a max filter is applied to a distance value to avoid going in the corresponding direction by detecting a cliff when a contamination detection condition is met in the sensor window.

A value deviation may occur whenever the distance of the cliff sensor 172 is measured, and a cliff avoidance motion may frequently occur because the distance condition is satisfied due to the deviation even if there is no contamination. At this time. It is possible to reduce the distance measurement deviation and reduce the occurrence frequency of condition satisfaction by applying a max filter using the maximum value among a plurality of recent distance values. Accordingly, it is possible to minimize the cliff avoidance motion caused by sensor contamination before an error occurs.

When the dust pollution condition is satisfied (S810), the dust pollution count is increased (S820), and if not satisfied (S810), when the dust pollution count is set to 0 or reduced, a dust pollution error may not occur depending on the situation. .

Like the raw data of the cliff sensor 172, cliff detection by sensor contamination (satisfying the dust contamination condition) occurs intermittently, but if there is data that does not satisfy the dust contamination condition, the dust contamination count decreases, so it is left unattended for a considerable period of time Even if you do, the error may not occur. If this problem is solved by greatly reducing the error condition threshold, a false detection error may occur when crossing a dark floor obstacle.

12 is a flowchart illustrating a control method of a mobile robot according to an embodiment of the present invention.

Referring to FIG. 12, the controller 140 increases the time count by 1 according to the elapse of time (eg, 20 ms when one raw data is received) until the first time period elapses (S910). It can (S920).

Also, when the first time period elapses (S910), the control unit 140 may initialize the dust contamination count and the time count (S930).

On the other hand, if the dust contamination condition is satisfied (S940), the controller 140 may increase the dust contamination count by 1 (S950). As described above, the dust contamination condition may include a measurement distance value condition and a light intensity value condition. The controller 140 may increase the dust contamination count by 1 when both the measurement distance value condition and the light quantity condition are satisfied (S940). For example, the controller 140 may increase the dust contamination count by 1 when the distance value is less than the S value and the light intensity value is less than the X value (S940).

Meanwhile, if the dust contamination count is greater than or equal to a first threshold value th1 before the first time period elapses (S960), the control unit 140 may generate a dust contamination error (S970).

That is, the first time period is a time period for determining a dust contamination error, and when the first time period elapses (S910), count values are initialized (S930), and then the next first time period starts again , When the dust contamination count reaches the first threshold value th1 during the first time period (S960), a dust contamination error may be generated (S970).

The mobile robot 100 according to an embodiment may stop moving when the dust contamination count is greater than a threshold value during a predetermined time period. Also, the threshold value may be set differently according to the predetermined time period. The first time period and the first threshold value th1 may be set in association with each other. For example, when the first time period is set to be long, the first threshold value th1 is also set to be large, and when the first time period is set to be short, the first threshold value th1 is also set to be small. In addition, the threshold value may be set differently depending on how the dust contamination count increases.

According to one embodiment of the present invention, it is possible to prevent the mobile robot 100 from driving in a dangerous environment by detecting when the sensor window 630 is contaminated.

According to an embodiment of the present invention, the distance value and the light intensity value of the cliff sensor 172 may be used to detect contamination of the sensor window 630 .

Depending on the embodiment, by applying the above-described Max filter to the distance value, when the dust pollution condition is satisfied, the cliff may be detected and avoided.

According to an embodiment of the present invention, the dust contamination count may be initialized at regular time intervals. Even in this case, if the dust contamination count is greater than the set threshold value, the mobile robot 100 may be stopped and then a dust error may be generated.

Meanwhile, there may be cases in which errors do not occur accurately depending on the driving environment, and the mobile robot 100 may perform excessive avoidance driving.

13 is a diagram referenced for description of excessive avoidance driving.

Referring to FIG. 13 , the floor may include a light floor 1310 and a dark floor 1320 . In the case of a dark floor 1320 , a distance value of the cliff sensor 172 may be detected smaller than a distance value of a bright floor 1310 . In a state in which the sensor window 630 is contaminated, the dust contamination count does not rise on the light floor 1310 and the dust contamination counter increases only on the dark floor 1320.

In the dust contamination condition of the sensor window 630, when the light floor 1310 does not satisfy the dust contamination condition and the black floor 1320 satisfies the dust contamination condition, the sensor window 630 is in a cliff avoidance motion caused by contamination. When the floor 1320 and the bright floor 1310 are moved back and forth (1331, 1332, 1333, 1334) may occur.

In this case, if the threshold value is set high, a situation that should generate an error may be missed. Conversely, if the threshold value is set small, there is a possibility of error malfunction.

When driving on the black floor 1320, the distance value tends to come in small, so dust contamination conditions can be easily satisfied. Accordingly, after entering the black floor 1320, the mobile robot 100 recognizes it as a dust-contaminated state and can avoid a cliff. This situation is repeated depending on the floor environment, and at this time, if the time in the black floor 1320 is short, the dust contamination error does not occur.

Therefore, as shown in FIG. 13 , whenever the mobile robot 100 enters the black floor 1320 , a cliff avoiding motion may be taken, and an area in which it may travel may disappear. Therefore, there is a need for a dust detection and counting method capable of preventing malfunction depending on the floor environment.

The control unit 140 may increase the dust pollution count by 1 by reflecting only one condition even if the dust pollution condition is satisfied a plurality of times during a predetermined time. In addition, if the dust detection counting method is changed in this way, the threshold value may be set differently.

14 is a flowchart illustrating a control method of a mobile robot according to an embodiment of the present invention.

Referring to FIG. 14, the controller 140 increases the time count by 1 according to the elapse of time (eg, 20 ms when one raw data is received) until the first time period elapses (S1410). It can (S1420).

Also, when the first time period elapses (S1410), the control unit 140 may initialize the dust contamination count and the time count (S1430).

Meanwhile, the dust contamination count may increase by only 1 after the lapse of the second time period even if the dust contamination condition is satisfied a plurality of times during the second time period.

The controller 140 may determine whether the dust contamination condition is satisfied during the second time period (S1460). In some cases, the controller 140 may count the number of times the dust contamination condition is satisfied during the second time period.

Meanwhile, even if the dust pollution condition is satisfied a plurality of times during the second time period, the control unit 140 may increase the dust pollution count by 1 after the second time period elapses (S1460) (S1480). .

For example, if 50 pieces of raw data per second, raw data per 0.02 seconds = 20 ms can be counted, but during the second time period, even if some raw data satisfying the dust contamination condition is generated, the dust contamination occurs at intervals of the second time period. The count can be incremented by 1.

According to an embodiment, the dust contamination count is increased when the dust contamination condition is reached without applying the max filter to the distance value of the cliff sensor 172 . At this time, even if the raw data satisfies the dust contamination condition several times during the second time period, the dust contamination count is increased only once. Accordingly, the dust contamination count is increased by 1 even when staying on the black floor 1320 for a short time, and the dust contamination count does not increase rapidly even if the floors 1310 and 1320 are reciprocated, so unnecessary driving, cliff avoidance motion, and cliff area registration can be prevented. can

Meanwhile, if the dust contamination count is greater than the second threshold value th2 before the lapse of the first time period (S1490), the controller 140 may generate a dust contamination error (S1510). When a dust contamination error occurs, the dust contamination count may be initialized to 0 (S1510).

According to an embodiment, the second threshold value th2 may be set smaller than the first threshold value th1.

According to an embodiment, when the dust contamination condition is satisfied (S1440), the control unit 140 may set a contamination detection flag to true (S1450). Meanwhile, the default state of the contamination detection flag may be false. The contamination detection flag information may be stored in the memory of the control unit 140 or stored in the storage unit 130 .

The control unit 140 may set the pollution detection flag information to true when the current (latest) raw data satisfies the dust contamination condition (S1440). The control unit 140 may set the contamination detection flag information to true when the current (latest) raw data satisfies both the measured distance value condition and the light quantity condition (S1440).

In this case, the controller 140 may increase the dust contamination count by 1 after the second time period has elapsed (S1460), and if the contamination detection flag is determined to be true (S1470) (S1480).

In addition, when the dust contamination error occurs (S1510), the control unit 140 may set the contamination detection flag to false (S1520), that is, the control unit 140 may When a dust contamination error occurs (S1510), the contamination detection flag may be restored to a default state.

The mobile robot according to the present invention is not limited to the configuration and method of the embodiments described above, but all or part of each embodiment is selectively combined so that various modifications can be made. may be configured.

Similarly, while actions are depicted in the drawings in a particular order, it should not be construed as requiring that those actions be performed in the specific order shown or in the sequential order, or that all depicted actions must be performed to obtain desired results. . In certain cases, multitasking and parallel processing can be advantageous.

Meanwhile, the method for controlling a mobile robot according to an embodiment of the present invention can be implemented as a processor-readable code on a processor-readable recording medium. The processor-readable recording medium includes all types of recording devices in which data readable by the processor is stored.

In addition, although the preferred embodiments of the present invention have been shown and described above, the present invention is not limited to the specific embodiments described above, and the technical field to which the present invention belongs without departing from the gist of the present invention claimed in the claims. Of course, various modifications are possible by those skilled in the art, and these modifications should not be individually understood from the technical spirit or perspective of the present invention.

Mobile robots: 100
Body: 110
Control: 140
Service Department: 150
Driving part: 160
Sensing unit: 170
Cliff sensor: 172

Claims (17)

a driving unit for moving the main body;
a cliff sensor disposed on a lower side of the main body, including a light emitting unit for outputting light, a light receiving unit for receiving reflected light, and a sensor window spaced apart from the light emitting unit and the light receiving unit; and,
and a controller configured to increase a dust contamination count by 1 when the data obtained from the cliff sensor satisfies a set dust contamination condition, and to stop the movement when the dust contamination count is greater than a threshold value. According to claim 1,
The mobile robot generating a dust contamination error when the dust contamination count is greater than a threshold value. According to claim 1,
The dust contamination condition is set based on a measurement distance value and a received light amount value of the cliff sensor,
The controller increases the dust contamination count by 1 when both the measurement distance value condition and the light quantity condition are satisfied. According to claim 3,
The control unit decreases the dust contamination count by 1 when both the measurement distance value condition and the light intensity value condition are not satisfied. According to claim 1,
When the dust contamination condition is satisfied, a mobile robot that avoids traveling by changing direction so as not to move in the current driving direction. According to claim 5,
The mobile robot performing the avoidance driving only when the maximum value among the latest N measured distance values satisfies the dust contamination condition. According to claim 1,
The controller increases the time count by 1 as time elapses until the first time period elapses. According to claim 7,
The controller initializes the dust contamination count and the time count when the first time period elapses. According to claim 7,
The controller generates a dust contamination error when the dust contamination count is greater than a first threshold value before the first time period elapses. According to claim 7,
The mobile robot of claim 1 , wherein the dust contamination count is increased by only 1 after the second time period has elapsed even if the dust contamination condition is satisfied a plurality of times during the second time period. According to claim 10,
The control unit generates a dust contamination error when the dust contamination count is greater than a second threshold value before the first time period elapses. According to claim 11,
The control unit sets a contamination detection flag to true when the dust contamination condition is satisfied. According to claim 12,
The controller increases the dust contamination count by 1 when the second time period elapses and the contamination detection flag is true. According to claim 13,
The control unit sets the contamination detection flag to false when the dust contamination error occurs. According to claim 1,
Stopping the movement when the dust contamination count is greater than a threshold value during a predetermined time period;
The mobile robot wherein the threshold value is set differently according to a method in which the dust contamination count increases. According to claim 1,
Stopping the movement when the dust contamination count is greater than a threshold value during a predetermined time period;
The mobile robot wherein the threshold value is set differently according to the predetermined time period. a driving unit for moving the main body;
a cliff sensor disposed on a lower side of the main body, including a light emitting unit for outputting light, a light receiving unit for receiving reflected light, and a sensor window spaced apart from the light emitting unit and the light receiving unit; and,
a control unit configured to increase a dust contamination count by 1 when the measured distance value of the cliff sensor and the received light intensity value satisfy set conditions, and to stop the movement when the dust contamination count is greater than a threshold value; robot.

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