A medical system comprises a robotic manipulator arm and an imaging probe coupled to the robotic manipulator arm such that the imaging probe is movable in connection with the robotic manipulator arm. The system also comprises a control system in communication with the robotic manipulator arm and the imaging probe. The control system performs operations comprising extracting system information. The system information includes kinematic information from a robotic arm of a medical system, setup information, or a combination of the kinematic information and setup information associated with a medical procedure to be performed. The control system also generates, by a control system processor, a first registration between a first set of model points of a model of a patient anatomy of interest and a second set of intra-operatively collected captured points of a portion of the patient anatomy of interest, wherein the registration is based on the extracted system information.

This disclosure relates generally to navigation of a tele-robot in dynamic environment using in-situ intelligence. Tele-robotics is the area of robotics concerned with the control of robots (tele-robots) in a remote environment from a distance. In reality the remote environment where the tele robot navigates may be dynamic in nature with unpredictable movements, making the navigation extremely challenging. The disclosure proposes an in-situ intelligent navigation of a tele-robot in a dynamic environment. The disclosed in-situ intelligence enables the tele-robot to understand the dynamic environment by identification and estimation of future location of objects based on a generating/training a motion model. Further the disclosed techniques also enable communication between a master and the tele-robot (whenever necessary) based on an application layer communication semantic.

A system for controlling a first robotic arm relative to a second robotic arm is disclosed. The system includes a two robotic arms each including a surgical tool and a tool driver. A central control circuit is configured to communicate with the robotic arms to determine a position of the robotic arms and modify a control algorithm for one of the robotic arms based on the relative position of the other robotic arm.

A method for adaptive control of surgical network control and interaction is disclosed. The surgical network includes a surgical feedback system. The surgical feedback system includes a surgical instrument, a data source, and a surgical hub configured to communicably couple to the data source and the surgical instrument. The surgical hub includes a control circuit. The method includes receiving, by the control circuit, information related to devices communicatively coupled to the surgical network; and adaptively controlling, by the control circuit, the surgical network based on the received information.

A method implemented by a surgical instrument is disclosed. The surgical instrument includes first and second jaws and a flexible circuit including multiple sensors to optimize performance of a radio frequency (RF) device. The flexible circuit includes at least one therapeutic electrode couplable to a source of RF energy, at least two sensing electrodes, and at least one insulative layer. The insulative layer is positioned between the at least one therapeutic electrode and the at least two sensing electrodes. The method includes contacting tissue positioned between the first and second jaws of the surgical instrument with the at least one therapeutic electrode and at the least two sensing electrodes; sensing signals from the at least two sensing electrodes; and controlling RF energy delivered to the at least one therapeutic electrode based on the sensed signals.

A method for adaptive control of surgical network control and interaction is disclosed. The surgical network includes a surgical feedback system. The surgical feedback system includes a surgical instrument, a data source, and a surgical hub configured to communicably couple to the data source and the surgical instrument. The surgical hub includes a control circuit. The method includes receiving, by the control circuit, information related to devices communicatively coupled to the surgical network; and adaptively controlling, by the control circuit, the surgical network based on the received information.

Described herein is a framework for efficient task-agnostic, user-independent adaptive teleoperation of mobile robots and remotely operated vehicles (ROV), including ground vehicles (including legged systems), aircraft, watercraft and spacecraft. The efficiency of a human operator is improved by minimizing the entropy of the control inputs, thereby minimizing operator energy and achieving higher performance in the form of smoother trajectories by concurrently estimating the user intent online and adaptively updating the action set available to the human operator.

The present invention provides a fractional order sliding mode synchronous control method for a teleoperation system based on an event trigger mechanism. The method comprises: establishing a dynamics model for the teleoperation system by considering external disturbance and parameter uncertainty, selecting a master robot and a slave robot, interactively establishing the teleoperation system through a communication network, determining system parameters of the dynamics model, designing a fractional order nonsingular rapid terminal sliding mode surface equation by utilizing a position tracking error and a fractional order calculus, setting a trigger event condition of information interaction between the master robot and the slave robot, designing a self-adaptive fractional order nonsingular rapid terminal sliding mode controller based on the sliding mode, designing a Lyapunov function to carry out stability analysis, proving the boundedness of a closed-loop state signal of the system.

A system (1) for emulating remote control of a physical robot (5) via a wireless network (11) is disclosed. The system comprises a control module (2) for determining a trajectory for the physical robot and generating trajectory control data that comprises velocity data and positional data based on the determined trajectory. The system further comprises a first control loop (3), comprising a first feed forward controller (4). The first feed forward controller is configured to receive the trajectory control data, send, via the wireless network (11), and a first velocity command to a first control interface of the physical robot (5). The first velocity command is based on the trajectory data. The first feed forward controller (4) is further configured to receive, via the wireless network, a first set of sensor data from physical robot. The system (1) further comprises a simulated robot implementing a digital twin (9) of the physical robot, and a second control loop (6) comprising a second feed forward controller (7). The second feed forward controller is configured to receive, via the wireless network, a second set of sensor data from the physical robot, determine a second velocity command based on the received second set of sensor data, and send the second velocity command to a second control interface of the digital twin. A corresponding method (100) is also disclosed.

According to one embodiment, a system includes a processor and a transmitter. The processor is configured to generate a first control signal including a first instruction to operate at least part of a terminal with in a first validity and generate a second control signal including a second instruction to operate at least part of the terminal within a second validity period after an operation of the at least part of the terminal in accordance with the first instruction. The transmitter is configured to transmit the first control signal to the terminal at first timing and transmit the second control signal to the terminal at second timing after the first timing. An end of the first validity period is after the second validity timing.