Abstract: According to an embodiment, an electronic device comprises: a moving member comprising a motor, an operation performance member including an arm, a communication module comprising communication circuitry, a camera and at least one processor operatively connected to the moving member, the operation performance member, the communication module and the camera, wherein one or more of the at least one processor is configured to: control the moving member so that the electronic device moves to a position related to operation performance confirmed based on an operation performance command based on the operation performance command being received through the communication module, acquire a first marker image through the camera based on moving to the position, set a reference coordinate system based on the first marker image, set an operation area boundary within the reference coordinate system based on operation area boundary information acquired through a second marker image based on the second marker image being ac

Abstract: A robot device according to various embodiments comprises a camera, a robot arm, and a control device electrically connected to the camera and the robot arm, wherein the control device can be configured to collect a robot arm control record about a random operation, acquire, from the camera, a camera image in which a working space of the robot arm is photographed, implement an augmented reality model by rendering a virtual object corresponding to an object related to objective work in the camera image, and update a control policy for the objective work by performing image-based policy learning on the basis of the augmented reality model and the control record.

Abstract: A robot device according to various embodiments comprises a camera, a robot arm, and a control device electrically connected to the camera and the robot arm, wherein the control device can be configured to collect a robot arm control record about a random operation, acquire, from the camera, a camera image in which a working space of the robot arm is photographed, implement an augmented reality model by rendering a virtual object corresponding to an object related to objective work in the camera image, and update a control policy for the objective work by performing image-based policy learning on the basis of the augmented reality model and the control record.

Abstract: A hand-eye calibration system and method are provided. The system includes a robot on which a small pattern is mounted, a camera configured to photograph the robot, a memory, and a processor configured to move the robot, acquire posture information of the moved robot, acquire an image from the camera, move the camera after performing the robot movement, the posture information acquisition, and the image acquisition a first predetermined number of times, and perform hand-eye calibration for the robot based on the posture information and the images, which are obtained by repeatedly performing of the robot movement, the posture information acquisition, the image acquisition, and the camera movement.

Abstract: A hand-eye calibration system and method are provided. The system includes a robot on which a small pattern is mounted, a camera configured to photograph the robot, a memory, and a processor configured to move the robot, acquire posture information of the moved robot, acquire an image from the camera, move the camera after performing the robot movement, the posture information acquisition, and the image acquisition a first predetermined number of times, and perform hand-eye calibration for the robot based on the posture information and the images, which are obtained by repeatedly performing of the robot movement, the posture information acquisition, the image acquisition, and the camera movement.

Abstract: Disclosed are a robot, which builds a map using a surface data of a three-dimensional image, from which a dynamic obstacle is removed, and a method of building a map for the robot. The method includes sequentially acquiring first and second surface data of a route on which the robot moves; matching the first and second surface data with each other to calculate a difference between the first and second surface data; detecting a dynamic obstacle from the first and second surface data according to the difference between the first and second surface data; generating a third surface data by removing the dynamic obstacle from at least one of the first and second surface data; and matching the third surface data and any one of the first and second surface data with each other to build the map.

Abstract: A localization method of a moving robot is disclosed in which the moving robot includes: capturing a first omni-directional image by the moving robot; confirming at least one node at which a second omni-directional image having a high correlation with the first omni-directional image is captured; and determining that the moving robot is located at the first node when the moving robot reaches a first node, at which a second omni-directional image having a highest correlation with the first omni-directional image is captured, from among the at least one node.

Abstract: A method and apparatus to build a 3-dimensional grid map and a method and apparatus to control an automatic traveling apparatus using the same. In building a 3-dimensional map to discern a current location and a peripheral environment of an unmanned vehicle or a mobile robot, 2-dimensional localization and 3-dimensional image restoration are appropriately used to accurately build the 3-dimensional grid map more rapidly.

Abstract: A method to determine the location of a robot using an omni-directional image, the method including acquiring an omni-directional image from a robot, extracting a predetermined current line from the acquired omni-directional image, calculating a correlation coefficient between the extracted current line of the robot and each landmark line of pre-stored nodes using a Fast Fourier Transform (FFT), and performing a stochastic approach method of a particle filtering process on a basis of the calculated correlation coefficient to recognize a location of the robot.

Abstract: A simultaneous localization and map building (SLAM) method and medium for a moving robot is disclosed. The SLAM method includes extracting a horizontal line from an omni-directional image photographed at every position where the mobile robot reaches during a movement of the mobile robot, correcting the extracted horizontal line, to create a horizontal line image, and simultaneously executing a localization of the mobile robot and building a map for the mobile robot, using the created horizontal line image and a previously-created horizontal line image.

Abstract: An apparatus and a method of locating a moving robot are disclosed. The apparatus includes a storage unit storing information on straight lines of wall on a map, a state quantity detection unit detecting quantity of state of the robot running along the wall, and a control unit estimating an interior position of the robot by obtaining straight line information based on the detected state quantity and matching the obtained straight line information with the stored straight line information.

Abstract: An omni-directional stereo camera and a control method thereof. The omni-directional stereo camera includes two or more omni-directional cameras, and a supporting member installed within a shooting range between the omni-directional cameras to interconnect the omni-directional cameras and including compensation patterns formed at the surfaces.

Abstract: A method and apparatus to build a 3-dimensional grid map and a method and apparatus to control an automatic traveling apparatus using the same. In building a 3-dimensional map to discern a current location and a peripheral environment of an unmanned vehicle or a mobile robot, 2-dimensional localization and 3-dimensional image restoration are appropriately used to accurately build the 3-dimensional grid map more rapidly.

Abstract: Disclosed are a walking robot and a method of controlling the same, in which one method is selected from a ZMP control method and a FSM control method. Based on characteristics of a motion to be performed, the current control mode of the walking robot is converted into a different control mode, and the motion is performed based on the converted control mode, to enhance the efficiency and performance of the walking robot. The method includes receiving an instruction to perform a motion; selecting any one mode, which is determined to be more proper to perform the instructed motion, out of a position-based first control mode and a torque-based second control mode; and performing the instructed motion according to the selected control mode.

Abstract: Disclosed herein are a device and method of controlling a manipulator. The device includes a device to control a manipulator, including a sensing unit to sense a joint position and joint torque of the manipulator a disturbance estimator to estimate disturbance torque using a state space equation with respect to the manipulator having the sensed joint position and joint torque as input; and a controller to control the manipulator based on the estimated disturbance torque.

Abstract: An apparatus and a method of locating a moving robot are disclosed. The apparatus includes a storage unit storing information on straight lines of wall on a map, a state quantity detection unit detecting quantity of state of the robot running along the wall, and a control unit estimating an interior position of the robot by obtaining straight line information based on the detected state quantity and matching the obtained straight line information with the stored straight line information.

Abstract: Disclosed are a robot, which builds a map using a surface data of a three-dimensional image, from which a dynamic obstacle is removed, and a method of building a map for the robot. The method includes sequentially acquiring first and second surface data of a route on which the robot moves; matching the first and second surface data with each other to calculate a difference between the first and second surface data; detecting a dynamic obstacle from the first and second surface data according to the difference between the first and second surface data; generating a third surface data by removing the dynamic obstacle from at least one of the first and second surface data; and matching the third surface data and any one of the first and second surface data with each other to build the map.

Abstract: A simultaneous localization and map building (SLAM) method and medium for a moving robot is disclosed. The SLAM method includes extracting a horizontal line from an omni-directional image photographed at every position where the mobile robot reaches during a movement of the mobile robot, correcting the extracted horizontal line, to create a horizontal line image, and simultaneously executing a localization of the mobile robot and building a map for the mobile robot, using the created horizontal line image and a previously-created horizontal line image.

Abstract: A moving robot and moving object detecting method and medium thereof is disclosed. The moving object detecting method includes transforming an omni-directional image captured in the moving robot to a panoramic image, comparing the panoramic image with a previous panoramic image and estimating a movement region of the moving object based on the comparison, and recognizing that a movement of the moving object exist in the estimated movement region when the area of the estimated movement region exceeds the reference area.

Abstract: A localization method of a moving robot is disclosed in which the moving robot includes: capturing a first omni-directional image by the moving robot; confirming at least one node at which a second omni-directional image having a high correlation with the first omni-directional image is captured; and determining that the moving robot is located at the first node when the moving robot reaches a first node, at which a second omni-directional image having a highest correlation with the first omni-directional image is captured, from among the at least one node.