Abstract: A digital twin modeling method to assemble a robotic teleoperation environment, including: capturing images of the teleoperation environment; identifying a part being assembled; querying the assembly assembling order to obtain a list of assembled parts according to the part being assembled; generating a three-dimensional model of the current assembly from the list and calculating position pose information of the current assembly in an image acquisition device coordinate system; loading a three-dimensional model of the robot, determining a coordinate transformation relationship between a robot coordinate system and an image acquisition device coordinate system; determining position pose information of the robot in an image acquisition device coordinate system from the coordinate transformation relationship; determining a relative positional relationship between the current assembly and the robot from position pose information of the current assembly and the robot in an image acquisition device coordinate syste

Abstract: A digital twin modeling method to assemble a robotic teleoperation environment, including: capturing images of the teleoperation environment; identifying a part being assembled; querying the assembly assembling order to obtain a list of assembled parts according to the part being assembled; generating a three-dimensional model of the current assembly from the list and calculating position pose information of the current assembly in an image acquisition device coordinate system; loading a three-dimensional model of the robot, determining a coordinate transformation relationship between a robot coordinate system and an image acquisition device coordinate system; determining position pose information of the robot in an image acquisition device coordinate system from the coordinate transformation relationship; determining a relative positional relationship between the current assembly and the robot from position pose information of the current assembly and the robot in an image acquisition device coordinate syste

Abstract: The present invention relates to a robot teaching system based on image segmentation and surface electromyography and robot teaching method thereof, comprising a RGB-D camera, a surface electromyography sensor, a robot and a computer, wherein the RGB-D camera collects video information of robot teaching scenes and sends to the computer; the surface electromyography sensor acquires surface electromyography signals and inertial acceleration signals of the robot teacher, and sends to the computer; the computer recognizes a articulated arm and a human joint, detects a contact position between the articulated arm and the human joint, and further calculates strength and direction of forces rendered from a human contact position after the human joint contacts the articulated arm, and sends a signal controlling the contacted articulated arm to move along with such a strength and direction of forces and robot teaching is done.

Abstract: The present invention relates to a robot teaching system based on image segmentation and surface electromyography and robot teaching method thereof, comprising a RGB-D camera, a surface electromyography sensor, a robot and a computer, wherein the RGB-D camera collects video information of robot teaching scenes and sends to the computer; the surface electromyography sensor acquires surface electromyography signals and inertial acceleration signals of the robot teacher, and sends to the computer; the computer recognizes a articulated arm and a human joint, detects a contact position between the articulated arm and the human joint, and further calculates strength and direction of forces rendered from a human contact position after the human joint contacts the articulated arm, and sends a signal controlling the contacted articulated arm to move along with such a strength and direction of forces and robot teaching is done.

Abstract: In one embodiment, the method includes receiving, by a processor, subscriber usage data for a plurality of subscribers and performance metrics data for a plurality of cells of a wireless network. The method further includes identifying, by the processor, a set of subscribers that have one or more peak usage time intervals that are periodic. The method further includes identifying, by the processor, one or more peak usage time intervals of the plurality of cells in the wireless network. The method further includes classifying, by the processor, a first subset of the set of subscribers based on a correlation of the one or more peak usage time intervals of respective ones of the set of subscribers to the peak usage time intervals of the cell.

Abstract: Methods and systems for forwarding packets over Label Switched Paths (LSPs) in a Virtual Private Network (VPN) are implemented within a Layer-2 architecture. A system includes a number of multi-purpose nodes connected by a number of multi-protocol label switching (MPLS) LSP links. Each multi-purpose node contains at least one bridging module (BM) that runs an extension of a bridging protocol (BP) contained in the IEEE 802.1d standard. The BP is used to establish MPLS LSPs between the BMs. The BP then generates a spanning tree using a spanning tree program to establish an optimal number of active LSPs. The remaining LSPs are then set to “inactive” The BM de-allocates the resources assigned to inactive LSPs and makes the resources available to other active LSPs.

Abstract: Methods and systems for forwarding packets over Label Switched Paths (LSPs) in a Virtual Private Network (VPN) are implemented within a Layer-2 architecture. A system includes a number of multi-purpose nodes connected by a number of multi-protocol label switching (MPLS) LSP links. Each multi-purpose node contains at least one bridging module (BM) that runs an extension of a bridging protocol (BP) contained in the IEEE 802.1d standard. The BP is used to establish MPSL LSPs between the BMs. The BP then generates a spanning tree using a spanning tree program to establish an optimal number of active LSPs. The remaining LSPs are then set to “inactive.” The BM de-allocates the resources assigned to inactive LSPs and makes the resources available to other active LSPs.