JP2004033340A - Robot vacuum cleaner and robot vacuum cleaner control program - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a robot vacuum cleaner and its control program, which restructure a proper cleaning route to restart cleaning when encountering an obstacle. <P>SOLUTION: The robot vacuum cleaner and the robot vacuum cleaner control program includes an obstacle detection sensor for detecting the obstacle, a room map data stored with obstacles in a room, attribute data stored with attribute of the obstacles, and cleaning route data stored with a cleaning route, to detect the presence/absence of the obstacle during cleaning. In the case of presence of an obstacle, the robot vacuum cleaner determines whether the obstacle at the present position is stored in the room map data or not, and in the case of absence of stored data, the detected obstacle is determined as an unknown obstacle to recognize the form of the unknown obstacle and estimate a form corresponding to the recognized form based on the room map data and the attribute data. When the form can be estimated, the obstacle of the estimated form is stored in the room map data to reconstruct the cleaning route data, and when the form cannot be estimated, the unknown obstacle is determined as a new obstacle to be stored in the room map and reconstruct the cleaning route data. <P>COPYRIGHT: (C)2004,JPO

Description

[0001]
TECHNICAL FIELD OF THE INVENTION
The present invention relates to a self-propelled robot cleaner that performs a cleaning operation in accordance with cleaning route data automatically generated based on room map data.
[0002]
[Prior art]
In a robot moving in a space having various obstacles, how to deal with the obstacle when it encounters the obstacle is an important point. As a method of coping with an obstacle encounter, it is common to use stored data on the obstacle.
[0003]
In the example disclosed in Japanese Patent Application Laid-Open No. 2001-129787, a robot holds image data of an obstacle. If there is an obstacle on the moving route, the robot captures the obstacle using an image sensor and determines whether or not the obstacle is a previously registered obstacle. Here, if it is determined that the obstacle is a registered obstacle, an avoidance operation is performed according to an avoidance program corresponding to the obstacle. If the robot determines that it is an unknown obstacle, the robot determines whether or not there is a person around the robot. When confirming the presence of the person, the robot requests assistance by voice or gesture.
[0004]
In the example disclosed in Japanese Patent Application Laid-Open No. Hei 9-222852, room map data is represented as a cognitive map that is spatial knowledge, and component definition information and probability information of the component are stored. Then, a route is generated using this cognitive map. When the robot encounters an obstacle different from the cognitive map on the moving route, the probability information of the corresponding component (route) is changed. As a result, the changed information is used at the time of the next movement route generation.
[0005]
As a sensor provided in the robot to detect an obstacle, an ultrasonic sensor is used in addition to the image sensor described above. The distance between the mobile robot and the surrounding obstacle is measured by an ultrasonic sensor, and a room map generation process and a robot position estimation process are performed (for example, see the US document "IEEE Journal of Robotics and Automation (IEEE Journal)". of Robotics and Automation) "(issued in 1988), RA-3, pp. 249-265.
[0006]
An example of a map storage method for determining a travel route in advance is disclosed in Japanese Patent Application Laid-Open No. H8-16241. In this example, attributes are given to each area of the room, such as a part where the entire work vehicle can travel, a part where only the working part of the work vehicle can be inserted, and a part where travel and insertion are impossible. Each area having the attribute is registered in the room map. A cleaning route is planned using the room map with attributes.
[0007]
[Problems to be solved by the invention]
Regarding the measures to be taken when an obstacle is encountered, in the example of Japanese Patent Application Laid-Open No. 2001-129787, since the image data is a planar information source, it is difficult to estimate the geometric structure of the obstacle and to store the information. It is difficult for a moving robot to capture an image of an obstacle in exactly the same environment as the obstacle image data being moved. Therefore, there is a possibility that an erroneous recognition may be made that the obstacles are different even though they are the same obstacle. Further, since the data update and the learning process are not performed, there is a possibility that the erroneous recognition may be caused in the same manner at the time of the next movement.
[0008]
Further, in the example of Japanese Patent Application Laid-Open No. 9-222852, when an obstacle different from the cognitive map information is encountered, a dynamic map update is performed, but the update content includes probability information indicating the reliability of the corresponding route. It does not change the actual shape of the map / route. Therefore, the route cannot be reconstructed in real time while moving, and if there is an unknown obstacle, the operation cannot be completed. Further, since the shape of the obstacle is not stored in the cognitive map, it is difficult to precisely clean around the obstacle.
[0009]
In a robot that not only moves but also cleans while moving, it is required to precisely grasp the shape of an obstacle. When an image sensor is used as an obstacle detection sensor for this purpose, the image data becomes a planar information source as described above, so that the shape grasp becomes insufficient. When an ultrasonic sensor is used, the distance to an obstacle can be measured to some extent, but the ultrasonic waves have a spread, so that sufficient accuracy cannot be obtained for grasping the shape. In addition, if a relatively expensive sensor such as an ultrasonic sensor is used, it is inevitable that the manufacturing cost of the robot increases. When a robot cleaner for a general household is targeted, it is desirable to manufacture the robot cleaner at the lowest possible cost.
[0010]
In the example of JP-A-8-16241, the cleaning route is generated depending on the attribute of the area in the room map. However, since the dynamic data update is not performed, the environment such as the movement of an obstacle is also considered. If a change occurs, the cleaning operation cannot be completed.
[0011]
A first object of the present invention is to provide a robot cleaner for reconstructing a cleaning route and restarting cleaning properly when an obstacle is encountered, and a robot cleaner control program for controlling the operation of the robot cleaner. It is in.
[0012]
A second object of the present invention is to provide a low-cost robot cleaner that dynamically updates a room map with high accuracy and a robot cleaner control program for controlling the operation thereof.
[0013]
It is a third object of the present invention to provide a robot cleaner for accurately and quickly recognizing an obstacle and performing cleaning, and a robot cleaner control program for controlling the operation of the robot cleaner.
[0014]
[Means for Solving the Problems]
The first object is to provide an obstacle detection sensor for detecting the presence / absence of an obstacle and recognizing its shape, room map data storing a room map in which obstacles in a room are registered, and room map data. And attribute data storing the attributes (existence history, cleaning method, etc.) of the obstacle registered in the storage device, and cleaning route data storing the cleaning route created based on the room map data as the cleaning route, Uses an obstacle detection sensor to detect the presence or absence of an obstacle during cleaning according to the cleaning route, and if an obstacle is detected, whether or not an obstacle is registered at that position on the room map data If an obstacle is not registered, the detected obstacle is determined as an unknown obstacle, the shape of the unknown obstacle is recognized using the obstacle detection sensor, and the shape that matches the recognized shape is set in the room. map If it can be estimated by referring to the data and attribute data, the obstacle having the estimated shape is registered in the room map data, the cleaning route data is reconstructed, and a shape that matches the recognized shape is estimated. If this is not possible, this is achieved by determining the unknown obstacle as a newly appearing obstacle, registering it in the room map data, and reconstructing the cleaning route data.
[0015]
By adopting such means, when the robot cleaner encounters an obstacle, the known or new appearance of the obstacle is clearly distinguished, and the room map is updated accordingly each time an obstacle is encountered. Is done. Therefore, even if an unknown obstacle is encountered, the cleaning route can be properly reconstructed, and the cleaning can be restarted according to the reconstructed cleaning route.
[0016]
The second object is to obtain information from an infrared sensor and a tactile sensor for a robot traveling based on route data stored in a storage unit, and use the information to determine the presence or absence of an obstacle on the route. This is achieved by detecting and recognizing the shape of the obstacle using information from the tactile sensor, and executing the map creating method of creating the route data using the result of the detection and the result of the recognition. .
[0017]
By using the infrared sensor and the tactile sensor tactile sense, it is possible to accurately grasp the shape of the obstacle, and therefore, it is possible to create cleaning route data with high accuracy and create a room map based on the data. Furthermore, both the infrared sensor and the tactile sensor can be configured using inexpensive sensor elements, and a low-cost robot cleaner can be realized.
[0018]
The third object is to provide at least a sensor, obstacle data, and cleaning route data in which a cleaning route is stored as a cleaning route. This is achieved by obtaining information, reconstructing the cleaning route using the information and the data of the obstacle, and, if the cleaning route is reconstructed, continuing cleaning according to the reconstructed cleaning route. You.
[0019]
Obstacle data, which is an attribute unique to the obstacle (existence history, cleaning method, etc.), is recorded in advance and reflected in the reconstruction of the cleaning route, so that the obstacle can be accurately and quickly recognized and cleaned. Can be performed.
[0020]
BEST MODE FOR CARRYING OUT THE INVENTION
Hereinafter, a robot cleaner according to the present invention and a robot cleaner control program for controlling the operation thereof will be described in more detail with reference to the embodiments of the invention illustrated in the drawings.
[0021]
FIG. 1 shows a system configuration example of a robot cleaner using the robot cleaner control program of the present invention. The robot cleaner includes a fixed intelligent processing unit 200 and a robot unit 100 for cleaning. The intelligent processing unit 200 and the robot unit 100 are wirelessly connected via the wireless communication module 225 and the wireless communication module 150 to form two-way wireless communication. The intelligent processing unit 200 wirelessly sends an operation command to the robot unit 100, and the robot unit 100 wirelessly sends sensor data to the intelligent processing unit 200.
[0022]
In the intelligent processing unit 200, a CPU (Central Processing Unit) 221 that operates based on the robot cleaner control program 235, a memory 500 used by the CPU 221 during processing, a wireless communication module 225, a robot cleaner control program 235, and a database 400 are stored. Storage device 222 is connected to the internal bus 224. Each of these units connected to the internal bus 224 forms a computer. The database 400 includes room map data 401, attribute data 402, and cleaning route data 403.
[0023]
Although these data will be described later, the room map data 401 is data indicating where and how obstacles (door cabinets, tables, chairs, walls, columns, etc.) in the room to be cleaned are arranged. Yes, these are represented by vertical and horizontal coordinate values starting from the cleaning start point. Therefore, the position where the obstacle is arranged, the shape and the direction of the obstacle can be known from the coordinate values.
[0024]
The attribute data 402 includes attributes of each obstacle, that is, the type of the obstacle (whether a wall or a general obstacle, a fixed obstacle if a general obstacle, or an object whose position changes every day, etc.), an ID of the obstacle (identification No.), obstacle data indicating the cleaning method (cleaning only around the obstacle or precision cleaning for cleaning the inside of the obstacle), the number of history areas (the number of areas where the obstacle existed in the past), etc. The cleaning route data 403 is data representing the cleaning route determined from the room map data 401 and the attribute data 402 by the coordinate values.
[0025]
In the robot unit 100, the control unit 300 is connected to the wireless communication module 150, and the driving unit 160 and the sensor data acquisition unit 110 are connected to the control unit 300. The driving unit 160 includes a mobile robot driving unit 120 that drives a wheel motor and a cleaning mechanism driving unit 130 that drives a suction unit that performs cleaning. The sensor data acquisition unit 110 includes an obstacle detection sensor 115 that detects the approach of an obstacle and the shape of the obstacle, and a wheel encoder 128 that measures the number of rotations of the wheel.
[0026]
The control unit 300 receives the operation command from the intelligent processing unit 200 and controls the operation of the driving unit 160. The control unit 300 also receives an operation command from the intelligent processing unit 200 and transmits sensor data from the sensor data acquisition unit 110 to the intelligent processing unit 200.
[0027]
The computer determines the operation of the robot unit 100 using each data of the database 400 and the sensor data transmitted from the robot unit 100, and transmits an operation command to the robot unit 100. In this way, the processing according to the robot cleaner control program 235 is executed.
[0028]
In addition, although the case where the robot cleaner is separated into the intelligent processing unit 200 and the robot unit 100 has been described above, the intelligent processing unit 200 can be mounted on the robot unit 100 and integrated therewith. In this case, the wireless communication module 225 and the wireless communication module 150 are omitted, and the control unit 300 is directly connected to the internal bus 224.
[0029]
FIG. 2 is a top view showing the configuration of the robot unit 100. In FIG. 2, a main body 101 of the robot unit 100 is an independent drive wheel type mobile robot including a left drive wheel 121, a right drive wheel 122, and an auxiliary wheel 123, and adjusts the wheel speed to any direction. It is possible to move. Rotary encoders 126 and 127 are installed on both wheels 121 and 122, respectively, so that the number of rotations of both wheels can be measured. The mobile robot driving unit 120 is configured by the wheels 121 and 122 and the respective motors (not shown in FIG. 2) for driving the wheels 121 and 122 (FIG. 1).
[0030]
Infrared sensors 111, 112, and 113 are provided around the robot unit main body 101, and can detect the presence of relatively near obstacles in front, side, and rear. Further, a wireless communication module 150 for realizing wireless communication with the intelligent processing unit 200 is provided. Although not shown, the control unit 300 is installed adjacent to the wireless communication module 150.
[0031]
Further, the robot unit main body 101 is provided with an arm 131 that can move horizontally to the left and right, and a suction unit 132 that can rotate about the distal end of the arm 131 as a central axis, and these constitute a cleaning mechanism. The suction section 132 includes a dust collection unit 136 for storing dust, a suction motor 135 for performing a suction operation by suction, and a tactile sensor 114. The tactile sensor 114 detects that an object has been touched.
[0032]
The arm section 131 and the suction section 132, which are cleaning mechanisms, can operate independently of the robot unit main body 101, and can be removed from the robot unit main body 101. Therefore, it is possible to prepare several cleaning mechanisms having different shapes and operations, and perform the operation by switching the cleaning mechanisms according to the state of the cleaning environment.
[0033]
The obstacle detection sensor 115 is constituted by the infrared sensors 111, 112, 113 and the tactile sensor 114, and the wheel encoder 128 is constituted by the rotary encoders 126, 127 (FIG. 1). The infrared sensors 111, 112, and 113 are realized by a structure in which a light emitting diode and a photodiode are combined, and infrared light reflected from an obstacle, which is emitted from the light emitting diode, is detected by the photodiode. , A piezoelectric element or a small microswitch. All of these elements are very inexpensive, and the obstacle detection sensor 115 can be realized at low cost.
[0034]
FIG. 3 is a block diagram showing a relationship between the control unit 300 in the robot unit 100, the sensor data acquisition unit 110, the mobile robot driving unit 120, and the cleaning mechanism driving unit 130.
[0035]
When the operation command transmitted from the intelligent processing unit 200 is a driving command, the control unit 300 generates a control signal for operating the mobile robot driving unit 120 and the cleaning mechanism driving unit 130 based on the driving command, and periodically generates the control signal. Then, the control signal is transmitted to the mobile robot driving unit 120 and the cleaning mechanism driving unit 130. The control signal controls the rotation of the left wheel motor 124 and the right wheel motor 125 for driving the wheels 121 and 122 in the mobile robot drive unit 120, and further controls the cleaning mechanism drive unit 130. The cleaning mechanism drive unit 130 includes a suction motor 135, an arm slide motor 134 for moving the arm 131, and a suction unit rotation motor 133 for operating a rotation mechanism attached to the suction unit 132, The rotation of each of these motors is controlled.
[0036]
The control unit 300 periodically acquires data from the sensor data acquisition unit 110. When the operation command transmitted from the intelligent processing unit 200 is a sensor data transmission request command, the control unit 300 transmits the sensor data to the intelligent processing unit 200 using the wireless communication module 150.
[0037]
FIG. 4 is a flowchart showing the processing contents of the control unit 300. First, the operation command received by the wireless communication module 150 is analyzed in step 301. Next, the process proceeds to step 302, and if the analyzed operation command is a drive command, the process proceeds to step 308. On the other hand, if the analyzed operation command is a sensor data transmission request command, the process proceeds to step 303 and the latest sensor data stored in the robot unit 100 is transmitted to the intelligent processing unit 200 via the wireless communication module 150. In step 308, the setting of the motor to be driven, the setting of the speed, and the setting of the moving distance are performed based on the contents of the driving command.
[0038]
The control unit 300 periodically executes the interrupt processing 304 at 10-millisecond intervals to drive the motor and acquire sensor data. When the interrupt processing execution time has been reached, in step 305, control signals for the respective drive motors of the wheels, arms, and suction units are generated based on the values set in step 308, and transmitted to the respective drive motors of the drive unit 160. Next, the process proceeds to step 306, where data on the number of obstacle detections and wheel rotations is acquired from the sensor data acquisition unit 110 as sensor data. The acquired sensor data is stored in the memory 307 as the latest sensor data. That is, the latest sensor data in the memory 307 is updated every time the interrupt processing is performed.
[0039]
Next, the contents of processing executed by the computer of the intelligent processing unit 200 according to the robot cleaner control program 235 will be described. In the present invention, when the robot cleaner during cleaning detects that the situation of the room is different from the situation shown in the room map data 401, the robot cleaner updates the room map data 401 on the spot and performs cleaning. The route data 403 is reconstructed. That is, each time there is a difference, the room map data 401 is updated and the cleaning route data 403 is reconstructed. There are differences when an obstacle is moved away from the room or when a new obstacle that is not in the room is brought into the room.
[0040]
The respective updating of the room map data 401 and the respective rebuilding of the cleaning route data 403 are performed by copying the room map data 401 and the cleaning route data 403 into the memory 500, respectively. 501 and the temporary cleaning route data 502, and when cleaning is completed, the temporary room map data 501 and the temporary cleaning route data 502 are updated by overwriting the room map data 401 and the cleaning route data 403. The next cleaning is performed using the updated room map data 401 and the cleaning route data 403.
[0041]
FIG. 5 is a flowchart showing a processing procedure by the computer of the intelligent processing unit 200. When an obstacle is registered on the temporary room map data 501 at the place where the robot cleaner is moving, or when an obstacle is encountered while moving, first, at step 201, sensor data acquisition of the robot unit 100 is performed. The sensor data from the unit 110 is analyzed. Specifically, the current position of the robot unit 101 is calculated based on data from the wheel encoder 128, and it is detected whether the tactile sensor 114 has touched an obstacle. In addition, the infrared sensors 111 to 113 can roughly detect the presence of a nearby obstacle and detect that the obstacle is approaching the obstacle, and in advance, the touch sensor 114 touches the obstacle. I can foresee.
[0042]
Next, in step 202, the temporary room map data 501 is compared with the sensor data analysis result, and the difference is first determined by the presence or absence of an obstacle. The determination of the presence or absence of the obstacle is performed based on the data from the tactile sensor 114 as described above. When there is an obstacle, the process proceeds to step 203, and it is determined whether or not an obstacle is registered on the temporary room map data 501 at a position where the tactile sensor 114 touches the obstacle. If an obstacle has been registered, it is determined that there is no difference, the process proceeds to step 211, and cleaning is performed according to the temporary cleaning route data 502. Subsequently, in step 212, if all the cleaning of the room has not been completed, the process returns to step 201 to continue the processing.
[0043]
In step 202, if there is no obstacle at the current position, the process proceeds to step 204, and the obstacle registered as being moving on the temporary room map data 501 is removed. In step 208, the temporary room map data 501 is updated by removing the obstacle, and subsequently, in step 210, the cleaning route is reconstructed based on the updated temporary room map data 501. Next, in step 211, cleaning is performed according to the reconstructed temporary cleaning route data 502.
[0044]
In step 203, if no obstacle is registered on the temporary room map data 501 at the position where the tactile sensor 114 touched the obstacle, the obstacle touched by the tactile sensor 114 is regarded as an unknown obstacle, and the process proceeds to step 205. , The shape of the unknown obstacle is recognized using the tactile sensor 114.
[0045]
Subsequently, in step 206, it is determined whether or not the temporary room map data 501 has an obstacle whose shape at least partially matches the recognized shape of the unknown obstacle. If there is an obstacle having a matching shape, the process proceeds to step 207, in which the entire shape is estimated using the attribute data 402, and in step 208, the obstacle having the estimated shape is registered in the temporary room map data 501. . Thereby, the temporary room map data 501 is updated.
[0046]
If there is no obstacle having the matching shape in Step 206, the process proceeds to Step 209, where the unknown obstacle is set as a newly appearing obstacle, and the entire shape of the unknown obstacle or the area where the unknown obstacle exists is grasped. The attribute data of the newly appearing obstacle is added to the attribute data 402. In step 208, a newly appearing obstacle whose shape or region is grasped is registered in the temporary room map data 501, and the temporary room map data 501 is updated.
[0047]
When all cleaning is completed in step 212, the attribute data 402 is updated in step 213 (for example, when an obstacle that has been fixed is moved, the attribute is changed to an obstacle whose position changes, etc.), and processing is performed. finish.
[0048]
As described above, in the present invention, the temporary room map data 501 and the temporary cleaning route data 502 are dynamically updated during the cleaning, so that the cleaning can be completed even when the situation inside the room changes.
[0049]
The function realized by the above steps 202 to 208 as a whole is (1) an obstacle shape estimation function, the function realized by step 213 is (2) an attribute data learning function, and the function realized by step 210 is {Circle around (3)} becomes a cleaning route reconstruction function, and in addition, the function realized by steps 209 and 208 becomes [4] a new registration function of unregistered data. The details of each will be described below.
[0050]
FIG. 6 is a flowchart showing the processing performed by the (1) obstacle shape estimation function. As described above, there are two situations where a difference occurs between the sensor data and the temporary room map data 501. One is when an unknown obstacle is encountered, and the other is when there is no obstacle present in the temporary room map data.
[0051]
Which of the two factors has occurred is checked in step 601 using the sensor data analysis result, and if there is no registered obstacle, the process proceeds to step 605 to search for the corresponding obstacle from the attribute data 402. Then, the shape of the obstacle is deleted from the temporary room map data 501. Thereby, the temporary room map data 501 is updated at the same time. Then, the processing is shifted to (3) the cleaning route reconstruction function.
[0052]
On the other hand, if it is determined in step 601 that the factor of the data difference is an encounter with an unknown obstacle, the process proceeds to step 602, where the shape of the unknown obstacle is recognized by the tactile sensor 114 installed in the suction unit 132, and the recognized shape is temporarily stored. Write to the room map data 501. Then, in step 603, by referring to the room map data 401 and the short-term storage attribute data 900, shape data that at least partially matches the shape of the unknown obstacle is searched.
[0053]
Here, the short-term memory attribute data 900 is shape data and attribute data of an obstacle that previously existed on the room map data 401 and the attribute data 402 but has disappeared. It is held in the storage device 222 only for a period.
[0054]
If there is no shape data that at least partially matches the shape of the unknown obstacle in the room map data 401 or the short-term memory attribute data 900, the unknown obstacle is regarded as a newly appearing object, and (4) Move the process to new registration of unregistered data.
[0055]
On the other hand, if there is a shape that at least partially matches the shape of the unknown obstacle, the number of obstacles that partially match the shape is checked. -Priorities are set by referring to information other than shapes such as knowledge, and matching shapes are narrowed down to one. As described above, if the number of matching shapes is reduced to one, the process proceeds to step 608, where information on the obstacle whose shape matches the above is read from the attribute data 402 or the short-term memory attribute data 900, and the overall shape is estimated. Then, in step 609, the temporary room map data 501 is updated based on the estimation result, and the processing shifts to (3) the cleaning route reconstruction function.
[0056]
Next, (4) new registration of unregistered data will be described. FIG. 7 shows the processing contents. In step 1001, it is determined whether the entire shape of the obstacle has been grasped or not. When the entire shape is to be grasped, the obstacle TYPE of the attribute data is registered as a fixed obstacle, and the other items are registered as invalid in the attribute data 402, and (3) the processing shifts to the cleaning route reconstruction function.
[0057]
On the other hand, if the shape is only partially grasped, the process proceeds to step 1003, where a rectangular area is formed from the partially grasped shape range, and this is registered in the temporary room map data 501 as an obstacle. Then, the obstacle TYPE of the attribute data is newly registered in the attribute data 402 as a fixed obstacle, and the other items are invalidated, and the processing shifts to the (3) cleaning route reconstruction function.
[0058]
Next, (3) the cleaning route reconstruction function will be described. FIG. 8 shows the processing contents. When it is estimated that the obstacle existing in the room map data 401 has disappeared as a result of the processing by the obstacle shape estimation function, in step 702, a route is reconstructed for the unswept area of the temporary room map data 501, The temporary cleaning route data 502 is written. This is shown in FIG.
[0059]
FIG. 9A shows obstacles 414 and 415, cleaning route data 403, and a swept area 411 represented by coordinate values in the room map data 401. If the obstacles 414 and 415 in FIG. 9A disappear, the temporary room map data 501 is updated to a state without the obstacles 414 and 415 as shown in FIG. 9B, and the cleaning route 512 is reconstructed for the unswept area. Is done. Thereafter, the robot cleaner performs work according to the restructuring route 512.
[0060]
On the other hand, a process in the case where an unknown obstacle is detected as a result of the process in the obstacle shape estimation function and the entire shape thereof is estimated will be described. First, in step 703, area division is performed on the unswept area of the temporary room map data.
[0061]
FIG. 10 shows the processing contents of the area division. First, in step 1101, a vertical line is dropped from the horizontal and vertical vertices of the room map data 401 to perform region division. Next, in step 1102, a rectangular region is formed by integrating regions sharing sides, and only the region having the maximum area is selected. By performing the processing in step 1102 until there is no unselected area, area division is realized.
[0062]
After the area is divided in this manner, the process proceeds to step 704, where a straight route is created for each area. A cleaning route is generated by integrating these straight routes, and is written to the temporary cleaning route data 502.
[0063]
FIG. 11 is a schematic diagram of steps 703 and 704. FIG. 11 a shows obstacles 416 and 417, cleaning route data 403, and a swept area 411 represented by coordinate values in the room map data 401. If an unknown obstacle is detected on the cleaning path and its overall shape is estimated to be the shape of the known obstacle 416, 417, it is assumed that the obstacle 416, 417 has moved to the position where the unknown obstacle was detected, and a temporary room map is displayed. The data 501 is updated so that the obstacles 416 and 417 have moved as shown in FIG. 11B. Then, a perpendicular line is drawn down from the vertex in the horizontal and vertical directions to the unswept area to divide the area. Further, as shown in FIG. 11c, adjacent areas are integrated to form a rectangular area. Then, a straight route 418 is created for each area (FIG. 11D), and these are integrated to reconstruct the cleaning route 512 (FIG. 11E).
[0064]
Although not shown, if the shape of the unknown obstacle does not match the shape of the registered obstacle, and it is determined that the unknown obstacle is a newly appearing obstacle, the obstacle shown in FIG. Objects 416 and 417 are left as they are without moving. Therefore, in that case, a cleaning route different from the cleaning route 512 of FIG. 11e is reconstructed.
[0065]
Subsequently, in step 705, if a cleaning method that requires precision cleaning is registered in the attribute data of the obstacle, the process proceeds to step 706, where the cleaning method that requires precision cleaning is registered with the temporary cleaning route data 502. Integrate. FIG. 12 shows a cleaning method that requires precision cleaning, and FIG. 13 shows a method of integrating with the temporary cleaning route data 502. In step 705, when the cleaning method requiring the precise cleaning is not registered in the attribute data of the obstacle, the cleaning route 512 is written in the temporary cleaning route data 502.
[0066]
The suction part 132 of the robot cleaner shown in the present embodiment can operate independently of the robot unit main body 101, so that it can enter a region where the robot unit main body 101 cannot pass. The cleaning method registered in the attribute data 402 is a method of cleaning the inside of an obstacle by the suction unit 132.
[0067]
For example, as shown in FIG. 12A, in an obstacle 419 having a region (inside an obstacle) 420 through which the robot unit main body 101 cannot pass (a convex hull shape 421 wrapping the obstacle 419 on the outside is formed), the suction part 132 is inserted. A possible line (suction insertion line) 422 is obtained, and a point 423 (robot position / direction indicating point) indicating the position / direction at which the robot cleaner inserts the suction unit 132 is calculated as shown in FIG. 12B. From these points, the suction part 132 is inserted for cleaning. The cleaning method registered in the attribute data 402 is the robot position / direction indication point 423.
[0068]
Further, as shown in FIG. 13A, the cleaning method is connected to the cleaning route 403 closest to the robot position / direction indication point 423, and when the working robot cleaner reaches the connection point as shown in FIG. The direction is changed according to the direction indication point 423, and the inside of the obstacle 419 is cleaned by the suction unit 132.
[0069]
Next, (2) the processing contents of the attribute data learning function will be described. FIG. 14 shows a flowchart of the processing contents. {Circle around (2)} The attribute data learning function is executed every time the cleaning operation is completed. First, the difference between the room map data 401 and the temporary room map data 501 is compared, and the obstacle in the room map data 401 created last time is checked for a moved obstacle or an obstacle that has disappeared (step 801). .
[0070]
When performing this obstacle confirmation, it is necessary to consider the characteristics of the obstacle. For example, a chair or a table leg constitutes one obstacle by a plurality of elements (obstacles). Unless this feature is managed by the attribute data 402, accurate obstacle confirmation cannot be performed. In this manner, obstacles are grouped in order to search for one obstacle composed of a plurality of elements (step 802). FIG. 15 shows a specific example.
[0071]
In FIG. 15, FIG. 15A shows room map data 401 generated in the previous cleaning operation, and FIG. 15B shows temporary room map data 501 generated in the current cleaning operation. When these two data are coincident, the five obstacles shown in the group 424 in FIG. 15B are inconsistent with the data in FIG. 15A, so that the obstacle in FIG. It can be seen that the obstacle is an obstacle.
[0072]
Here, it is first assumed that the group 424 forms one obstacle. Then, parallel movement and rotational movement are performed while maintaining the relative coordinate value of the group 424, and a search is made for an obstacle that matches the data of FIG. 15A.
[0073]
Subsequently, as shown in FIG. 15C, the group 424 is further divided, and it is assumed that the group 425 and the group 426 each constitute one obstacle, and a search is performed from the data of FIG. 15A. This grouping work is performed as long as the combination of obstacles permits. Then, by selecting an obstacle that matches the data in FIG. 15A and has the largest number of elements in the group, one obstacle including a plurality of elements can be found (step 803).
[0074]
In addition, a wall is one of the characteristic obstacles. The shape of the wall changes each time an obstacle near the wall moves. Therefore, unless the wall and the obstacle near the wall are accurately distinguished, accurate obstacle confirmation cannot be performed. FIG. 16 shows a method of searching for such a wall shape.
[0075]
16, FIG. 16a shows wall shape data 427 in the room map data 401 generated in the previous cleaning operation, and FIG. 16b shows wall shape data 428 in the temporary room map data 501 generated in the current cleaning operation. ing. By integrating these two data, the shape having the maximum area is defined as the wall shape. By using the shape obtained from the difference between the wall shape and the wall shape data in the temporary room map data 501 in FIG. 16B as an obstacle, the wall 429 and the obstacle 430 can be distinguished as shown in FIG. 16C ( Step 803).
[0076]
The obstacle (2) attribute data learning function is executed in consideration of such characteristics of the obstacle. When the obstacle search is completed, the process proceeds to step 804. As a result of the obstacle search in steps 801, 802, and 803, if there is an obstacle that does not exist in the temporary room map data 501 but exists only in the room map data 401, This is regarded as a lost obstacle, and the attribute data of this obstacle is moved to the short-term storage attribute data 900 (step 805). If there is an obstacle that exists only in the temporary room map data 501 but not in the room map data 401, this obstacle is a newly appearing obstacle, and (4) has already been registered by new registration of unregistered data. The attribute data is registered in the attribute data 402.
[0077]
For an obstacle that does not correspond to step 804, the process proceeds to step 806 to narrow down the obstacle. That is, as a result of the obstacle search in steps 801, 802, and 803, if one obstacle in the temporary room map data 501 matches a plurality of obstacles in the room map data 401, the process proceeds to step 807. By examining the existence history and knowledge of each obstacle based on the attribute data 402 corresponding to the plurality of obstacles, matching obstacles are narrowed down to one. If one obstacle can be searched in this way, the position where the searched obstacle has moved and the knowledge data calculated from the moved position are registered in the attribute data 402. By updating the attribute data 402 in this manner, more accurate obstacle estimation and dynamic map updating can be performed in the next and subsequent sweeping operations.
[0078]
FIG. 17 shows a specific example of the attribute data 402, and FIG. 18 shows a classification of the example of the obstacle TYPE. FIG. 17 is a diagram showing attribute data 402 of three obstacles, each of which is a wall, a fixed obstacle (cance), and a daily position changing obstacle (foot of a chair). The attribute data 402 includes items of ID, integrated ID, obstacle type, number of history areas, history area shape data, cleaning method, and knowledge data.
[0079]
Each item is described as follows.
ID: Number for associating with each obstacle stored in the room map data
Integrated ID: With respect to one obstacle (eg, chair legs, etc.) composed of a plurality of elements (obstacles), respective ID numbers associated with these elements
Obstacle TYPE: Indicates the type of obstacle. (See FIG. 18)
Number of history areas, history area shape data: Summarizes the number and shape coordinate values of areas where this obstacle existed in the past in room map data
Cleaning method: Memorize the robot position and direction indication points when cleaning the inside of obstacles
Knowledge data: Describes knowledge and conditions specific to obstacles.
[0080]
In FIG. 17, the wall is ID1, the fixed obstacle (dance) is ID2, and the daily position changing obstacle is ID7, which is an integration of chair legs ID3, 4, 5, and 6.
[0081]
19 and 20 show specific examples of the (2) attribute data learning function. 19 shows an example of updating the room map data 401, and FIG. 20 shows an example of updating the attribute data 402. As shown in FIGS. 20 and 21, in the first cleaning, all obstacles (walls of ID1, obstacles of ID2 and 3, and obstacles of ID4) are fixed obstacles. In the second cleaning, the data is updated by analyzing how the obstacle has changed (moved or disappeared) by searching using the grouping of obstacles described above.
[0082]
Here, the obstacles of ID2 and ID3 are determined as one obstacle, and ID5 is assigned. Further, since the positions of the obstacles with IDs 2, 3, and 4 are changing, the obstacle TYPE is a position change obstacle every day, and the areas 6, 7 where the obstacles existed before (the shaded portions in FIG. 19). ) Is stored as a history area.
[0083]
As for the cleaning method, the obstacles are not grouped in the first cleaning, so that the inside of the obstacle does not exist, and the cleaning method is invalid. Since the inside is included, the robot position / direction indicating point, which is the cleaning method, is calculated and registered in the attribute data 402. Further, knowledge data determined from the state of the obstacle is stored.
[0084]
As described above, in the first data update, all obstacles are set as a single fixed obstacle, and thus it takes time to update the data. However, if the attribute data 402 is constructed by repeating the number of data updates, the update time becomes longer. Be shortened.
[0085]
As described above, according to the present embodiment, the room map can be updated during cleaning, and the cleaning route can be reconstructed. Thus, it is possible to complete the cleaning of the room whose situation has changed. In addition, since an infrared sensor and a tactile sensor are used to detect obstacles, the shape of obstacles can be recognized with high accuracy, and room maps can be updated and cleaning routes can be reconstructed with high accuracy. Was. Furthermore, since the attribute data is provided for each obstacle, it is possible to perform the shape recognition of the obstacle with high accuracy and at high speed.
[0086]
【The invention's effect】
According to the present invention, when an obstacle is encountered during cleaning, it is possible to reconstruct the cleaning route while dynamically updating the room map, so that it can immediately respond to environmental changes. A robot cleaner for cleaning can be realized. Further, by providing the infrared sensor and the tactile sensor, it is possible to perform the shape recognition of the obstacle with high accuracy and at high speed, and to realize a precise cleaning operation. Further, the cost of the robot cleaner can be reduced.
[Brief description of the drawings]
FIG. 1 is a system configuration diagram for describing an embodiment of a robot cleaner according to the present invention.
FIG. 2 is a top view for explaining a robot unit of the robot cleaner shown in FIG. 1;
FIG. 3 is a configuration diagram for explaining a relationship between a control unit of the robot unit shown in FIG. 2 and a sensor system / drive system.
FIG. 4 is a flowchart for explaining processing contents of the robot unit shown in FIG. 2;
FIG. 5 is a flowchart for explaining processing executed by a computer according to the robot cleaner control program of the present invention.
FIG. 6 is a flowchart for explaining an obstacle shape estimation function in the processing content shown in FIG. 5;
FIG. 7 is a flowchart for explaining a new registration process of unregistered data in the processing contents shown in FIG. 5;
FIG. 8 is a flowchart for explaining a cleaning route reconstruction function in the processing contents shown in FIG. 5;
FIG. 9 is a diagram for explaining an example of cleaning route reconstruction.
FIG. 10 is a flowchart for explaining area division when a cleaning route is reconstructed.
FIG. 11 is a view for explaining another example of the cleaning route reconstruction.
FIG. 12 is a diagram for explaining an example of a method of cleaning the inside of an obstacle.
FIG. 13 is a view for explaining an example of a cleaning route integrating attribute data (cleaning method).
FIG. 14 is a flowchart for explaining an attribute data learning function in the processing contents shown in FIG. 5;
FIG. 15 is a diagram for explaining an example of grouping in obstacle search.
FIG. 16 is a view for explaining an example of a method for identifying a wall and an obstacle.
FIG. 17 is a diagram illustrating an example of attribute data.
FIG. 18 is a diagram for describing an example of classification of obstacles TYPE.
FIG. 19 is a diagram for explaining an example of an obstacle attribute learning function in room map data.
FIG. 20 is a view for explaining an example of an obstacle attribute learning function in attribute data.
[Explanation of symbols]
Reference Signs List 100 robot unit, 101 robot unit main body, 110 sensor data acquisition unit, 111 to 113 infrared sensor, 114 tactile sensor, 126, 127 encoder, 131 arm unit, 132 suction unit, 150, 225 Wireless communication module, 160: drive unit, 200: intelligent processing unit, 221: CPU, 222: storage device, 224: internal bus, 235: robot cleaner control program, 300: control unit, 400: database, 401: room map Data, 402: attribute data, 403: cleaning route data, 500: memory.

Claims (10)

A control program for controlling the operation of the robot cleaner by a computer,
The robot cleaner includes an obstacle detection sensor that detects the presence or absence of an obstacle and recognizes the shape of the obstacle, room map data that stores a room map in which obstacles in a room are registered, Attribute data that stores the attribute of the obstacle registered in the room map data, and cleaning route data that stores a cleaning path created based on the room map data as a cleaning route,
The control program causes the computer to:
A procedure for detecting the presence or absence of an obstacle using the obstacle detection sensor while performing cleaning according to the cleaning route;
When detecting the presence of an obstacle, a procedure of determining whether or not an obstacle is registered at the position on the room map data,
A procedure for determining the detected obstacle as an unknown obstacle if no obstacle is registered;
Recognizing the shape of the unknown obstacle using an obstacle detection sensor;
Estimating a shape that matches the recognized shape with reference to the room map data and the attribute data,
If the shape can be estimated, register the obstacle having the estimated shape in the room map data and reconstruct the cleaning route data,
And determining the unknown obstacle as a newly appearing obstacle, registering the unknown obstacle in the room map data, and reconstructing the cleaning route data when the shape cannot be estimated. Robot vacuum cleaner control program. 2. The computer-readable storage medium according to claim 1, wherein the obstacle detection sensor includes an infrared sensor and a tactile sensor. The room map data and the cleaning route data have temporary room map data and temporary cleaning route data, respectively.
The above procedure of registering the obstacle of the estimated shape in the room map data and reconstructing the cleaning route data includes registering the obstacle of the estimated shape in the temporary room map data and reconstructing the temporary cleaning route data. A step of overwriting the temporary room map data and the temporary cleaning route data with the room map data and the cleaning route data at the end of cleaning, respectively.
The procedure of determining the unknown obstacle as a newly appearing obstacle, registering the unknown obstacle in the room map data, and reconstructing the cleaning route data includes determining the unknown obstacle as a newly appearing obstacle, and The appearing obstacle is registered in the temporary room map data and the temporary cleaning route data is re-registered. At the end of cleaning, the temporary room map data and the temporary cleaning route data are stored in the room map data and the cleaning route data, respectively. The program according to claim 1, wherein the program is an overwriting procedure. The method according to claim 3, further comprising a step of learning and updating the attribute data by comparing a difference between the room map data before overwriting and the temporary room map data at the end of cleaning. Robot vacuum cleaner control program. The method according to claim 1, wherein in the step of registering the obstacle having the estimated shape in the room map data and reconstructing the cleaning route data, a method of cleaning the obstacle stored in the attribute data is used. Robot vacuum cleaner control program. A program for causing a traveling robot to create a map based on the route data stored in the storage means,
The above map creation method
A procedure for obtaining information from the infrared sensor and the tactile sensor;
A procedure for detecting the presence or absence of an obstacle on the route using the information;
A procedure for recognizing the shape of the obstacle using information from the tactile sensor;
A program having a procedure for creating the route data using a result of the detection and a result of the recognition. A control program for controlling the operation of the robot cleaner by a computer,
The robot cleaner has at least a sensor, data of an obstacle, and cleaning route data storing a cleaning path as a cleaning route,
The control program causes the computer to:
A procedure for acquiring information from the sensor when performing cleaning according to the cleaning route;
Reconstructing the cleaning route using the information and the obstacle data;
When the cleaning route is reconstructed, a procedure for continuing the cleaning in accordance with the reconstructed cleaning route. An obstacle detection sensor that detects the presence or absence of an obstacle and recognizes the shape of the obstacle;
Room map data storing a room map formed by registering obstacles in a room, attribute data storing attributes of obstacles registered in the room map data, and cleaning created based on the room map data A storage device for storing cleaning route data storing the route of the cleaning route as a cleaning route;
A processing unit for controlling cleaning,
The processing device comprises:
When performing cleaning according to the cleaning route, the presence or absence of an obstacle is detected using the obstacle detection sensor,
When detecting the presence of an obstacle, it is determined whether or not an obstacle is registered at the position on the room map data,
If no obstacle is registered, the detected obstacle is determined as an unknown obstacle,
Recognize the shape of the unknown obstacle using an obstacle detection sensor,
Estimating a shape that matches the recognized shape with reference to the room map data and the attribute data,
If the shape can be estimated, register the obstacle of the estimated shape in the room map data and reconstruct the cleaning route data,
When the shape cannot be estimated, the unknown obstacle is determined as a newly appearing obstacle, registered in the room map data, and the cleaning route data is reconstructed. The robot cleaner according to claim 8, wherein the obstacle detection sensor includes an infrared sensor and a tactile sensor. The robot cleaner according to claim 9, wherein the infrared sensor is provided in a robot unit having wheels for moving, and the tactile sensor is provided in a suction unit for performing a cleaning operation.

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