A robot task system includes: a robot; a transfer device configured to be driven to transfer a plurality of workpieces thereon by a specific distance at a time, the plurality of workpieces being placed within the specific distance; a driving management unit configured to manage a driving distance and a driving start timing of the transfer device for driving the transfer device each time; a task position generation unit configured to generate a plurality of task positions at the driving start timing managed by the driving management unit, the plurality of task positions being positions for the robot to execute a predetermined task on the plurality of workpieces; a task unit configured to update, according to the driving of the transfer device, the plurality of task positions generated by the task position generation unit and generate a task command to cause the robot to execute the predetermined task on the plurality of workpieces while following the plurality of workpieces; and a control unit configured to control the transfer device based on the driving distance and the driving start timing of the transfer device, and control the robot based on the task command generated by the task unit.

A handling device according to an embodiment includes a manipulator, a normal grid generation unit, a hand kernel generation unit, a calculation unit, and a control unit. The normal grid generation unit converts a depth image into a point cloud, generates spatial data including an object to be grasped that is divided into a plurality of grids from the point cloud, and calculates a normal vector of the point cloud included in the grid using spherical coordinates. The hand kernel generation unit generates a hand kernel of each suction pad. The calculation unit calculates ease of grasping the object to be grasped by a plurality of suction pads based on a 3D convolution calculation using a grid including the spatial data and the hand kernel. The control unit controls a grasping operation of the manipulator based on the ease of grasping the object to be grasped by the plurality of suction pads.

A computer-implemented visual input reconstruction system for enabling selective insertion of content into preexisting media content frames may include at least one processor configured to perform operations. The operations may include accessing a memory storing object image identifiers associated with objects and transmitting, to one or more client devices, an object image identifier. The operations may include receiving bids from one or more client devices and determining a winning bid. The operations may include receiving winner image data from a winning client device and storing the winner image data in the memory. The operations may include identifying, in a preexisting media content frame, an object insertion location. The operations may include generating a processed media content frame by inserting a rendition of the winner image data at the object insertion location in the preexisting media content frame and transmitting the processed media content frame to one or more user devices.

Embodiments of the present disclosure relate to a method and an apparatus for alerting threats to users. The apparatus may capture a plurality of signals including at least one of Electro-Magnetic (E-M) signals and sound signals. The E-M signal and sound signals are used to detect objects around the user. A threat to the user is predicted based on the objects around the user and one or more alerts are generated such that the user avoids the threat. The prediction of the threat enables the user to take an action even before the threat has occurred. Also, the alerts are generated based on the prediction such that the user can avoid the threat well in advance of the occurrence of the threat.

A control device is provided to reliably locate objects and calculate appropriate grasp points for each object to thereby effectively control a robot system. grasp point calculation is based on, at least, properties of a surface of the object to be grasped by the robot system to more effectively calculate a grasp point for the surface of the object. An organised point cloud generator and a robot system having a suction cup end are arranged to generate an organised point cloud of a storage. The robot system can grasp the object from the storage means. Normals of the organised point cloud and principal curvatures of the organised point cloud can be calculated. A grasp point can be calculated for the suction cup end effector of the robot system to grasp an object based on the organised point cloud, the calculated normal, the calculated principal curvatures, and a normal of a lower surface of the storage.

Provided are a method and robot device for sharing object data. The method, performed by a robot device, of sharing object data includes: obtaining sensing data related to objects in a certain space; classifying the obtained sensing data into a plurality of pieces of object data based on properties of the objects; selecting another robot device from among at least one other robot device; selecting object data to be provided to the selected robot device from among the classified plurality of pieces of object data; and transmitting the selected object data to the selected robot device, wherein the classifying of the sensing data into a plurality of pieces object data includes generating a plurality of data layers including the classified plurality of pieces of object data.

A handling device according to an embodiment includes a manipulator, a normal grid generation unit, a hand kernel generation unit, a calculation unit, and a control unit. The normal grid generation unit converts a depth image into a point cloud, generates spatial data including an object to be grasped that is divided into a plurality of grids from the point cloud, and calculates a normal vector of the point cloud included in the grid using spherical coordinates. The hand kernel generation unit generates a hand kernel of each suction pad. The calculation unit calculates ease of grasping the object to be grasped by a plurality of suction pads based on a 3D convolution calculation using a grid including the spatial data and the hand kernel. The control unit controls a grasping operation of the manipulator based on the ease of grasping the object to be grasped by the plurality of suction pads.

A control device is provided to reliably locate objects and calculate appropriate grasp points for each object to thereby effectively control a robot system. grasp point calculation is based on, at least, properties of a surface of the object to be grasped by the robot system to more effectively calculate a grasp point for the surface of the object. An organised point cloud generator and a robot system having a suction cup end are to generate an organised point cloud of a storage. The robot system can grasp the object from the storage means. Normals of the organised point cloud and principal curvatures of the organised point cloud can be calculated. A grasp point can be calculated for the suction cup end effector of the robot system to grasp an object based on the organised point cloud, the calculated normal, the calculated principal curvatures, and a normal of a lower surface of the storage.

A remote control system includes: an imaging unit that shoots an environment in which a device to be operated including an end effector is located; a recognition unit that recognizes objects that can be grasped by the end effector based on a shot image of the environment shot by the imaging unit; an operation terminal that displays the shot image and receive handwritten input information input to the displayed shot image; and an estimation unit that, based on the objects that can be grasped and the handwritten input information input to the shot image, estimates an object to be grasped which has been requested to be grasped by the end effector from among the objects that can be grasped and estimates a way of performing a grasping motion by the end effector, the grasping motion having been requested to be performed with regard to the object to be grasped.

A robot task system includes: a robot; a transfer device configured to be driven to transfer a plurality of workpieces thereon by a specific distance at a time, the plurality of workpieces being placed within the specific distance; a driving management unit configured to manage a driving distance and a driving start timing of the transfer device for driving the transfer device each time; a task position generation unit configured to generate a plurality of task positions at the driving start timing managed by the driving management unit, the plurality of task positions being positions for the robot to execute a predetermined task on the plurality of workpieces; a task unit configured to update, according to the driving of the transfer device, the plurality of task positions generated by the task position generation unit and generate a task command to cause the robot to execute the predetermined task on the plurality of workpieces while following the plurality of workpieces; and a control unit configured to control the transfer device based on the driving distance and the driving start timing of the transfer device, and control the robot based on the task command generated by the task unit.