Patent classifications
G05B2219/40126
Sharing neighboring map data across devices
A computing device and method are provided for transmitting a relevant subset of map data, called a neighborhood, to enable mutual spatial understanding by multiple display devices around a target virtual location to display a shared hologram in the same exact location in the physical environment at the same moment in time. The computing device may comprise a processor, a memory operatively coupled to the processor, and an anchor transfer program stored in the memory and executed by the processor.
Engineering autonomous systems with reusable skills
A computer-implemented method of engineering autonomous system with reusable skills includes displaying a graphical user interface simulating a physical environment. The graphical user interface depicts one or more simulated objects corresponding to one or more physical objects. Graphical markers are created on the simulated objects based on instructions provided by a user via the graphical user interface. The position and orientation of each graphical marker is determined with respect to the simulated objects. A skill function is created which comprises a functional description for using a controllable physical device to interact with the physical objects based on the position and orientation of each graphical marker. Executable code operable to perform the skill function is created and used to actuate the controllable physical device.
SHARING NEIGHBORING MAP DATA ACROSS DEVICES
A computing device and method are provided for transmitting a relevant subset of map data, called a neighborhood, to enable mutual spatial understanding by multiple display devices around a target virtual location to display a shared hologram in the same exact location in the physical environment at the same moment in time. The computing device may comprise a processor, a memory operatively coupled to the processor, and an anchor transfer program stored in the memory and executed by the processor.
Environment Replicator for Proxy Robot Handlers
A method for replicating the terrain of a remote environment by a terrain replicator with a plurality of extendable elements is disclosed. The method includes mounting the plurality of extendable elements in close proximity on the surface of a flat stage to form a matrix of extendable elements; connecting each extendable element in the matrix to a control node in an array of control nodes, wherein each control node in the array is assigned to control one extendable element in the matrix of extendable elements; sending data of an actual physical terrain in the remote environment to a terrain analysis computer in the terrain replicator; converting the remote terrain data by the terrain analysis computer into a terrain-generating data stream for driver electronics; producing by the driver electronics a plurality of control signals from the terrain-generating data stream, wherein each signal in the plurality of control signals addresses a control node in the array of control nodes to extend or retract each extendable element in the matrix of extendable elements; and generating by the matrix of extendable elements a topography precisely corresponding to the topography of the actual physical terrain at the remote environment.
Sharing neighboring map data across devices
A computing device and method are provided for transmitting a relevant subset of map data, called a neighborhood, to enable mutual spatial understanding by multiple display devices around a target virtual location to display a shared hologram in the same exact location in the physical environment at the same moment in time. The computing device may comprise a processor, a memory operatively coupled to the processor, and an anchor transfer program stored in the memory and executed by the processor.
Human-robot collaborative flexible manufacturing system and method
An exemplary method and system are disclosed to flexibly and adaptably manufacture and assemble a workpiece by using recordings of a user in machine learning/artificial intelligence algorithms to train a robot for subsequent automated manufacture. Machine learning and artificial intelligence learning can generate libraries of generalized dynamic motion primitives that can be subsequently combined for any type of manufacturing or assembling activity. The exemplary method and system can flexibly generate a model of an existing workpiece as a template or primer workpiece that can then be used in conjunction with the DMP operations to fabricate subsequent workpieces.