Massachusetts Institute of Technology

Catheter Robotics Inc.

Other

Michael Frederick Eilenberg

Hugh Miller Herr

Todd J Cohen

Hartmut Geyer

Ken Endo

Pavitra Krishnaswamy

Jared Markowitz

Jing Wang

A model-based neuromechanical controller for a robotic limb having at least one joint includes a finite state machine configured to receive feedback data relating to the state of the robotic limb and to determine the state of the robotic limb, a muscle model processor configured to receive state information from the finite state machine and, using muscle geometry and reflex architecture information and a neuromuscular model, to determine at least one desired joint torque or stiffness command to be sent to the robotic limb, and a joint command processor configured to command the biomimetic torques and stiffnesses determined by the muscle model processor at the robotic limb joint. The feedback data is preferably provided by at least one sensor mounted at each joint of the robotic limb. In a preferred embodiment, the robotic limb is a leg and the finite state machine is synchronized to the leg gait cycle.

Method for using a model-based controller for a robotic leg

A neuromuscular model-based controller for a robotic limb having at least one joint includes a neuromuscular model having a muscle model, muscle geometry and reflex feedback loop to determine at least one torque or impedance command to be sent to the robotic limb. One or more parameters that determine relation between feedback data and activation of the muscle model are adjusted consequent to sensory data from at least one of an intrinsic sensor and an extrinsic sensor. A controller in communication with the neuromuscular model is configured to receive the at least one torque or impedance command and controls at least one of position, torque and impedance of the robotic limb joint.

Neuromuscular model-based sensing and control paradigm for a robotic leg

Method for controlling a robotic limb joint

Embodiment methods of treating a human or animal patient using a remotely controlled catheter insertion system include inserting a catheter handle of a catheter into a handle controller of the remotely controlled catheter insertion system, operating the catheter remotely with the remotely controlled catheter insertion system to introduce the catheter into a body of the patient, and performing a diagnostic or therapeutic procedure on the patient using the catheter. Diagnostic or therapeutic procedures may include a mapping procedure, an ablation procedure, an angioplasty procedure, a drug delivery procedure; an electrophysiology procedure, a radiological procedure, and a medical device implantation or positioning procedure.

Remotely controlled catheter insertion system

A remotely controlled insertion system for a medical device is described. The system comprises a robotic device and a remote control mechanism. The robotic device has a handle controller to receive and hold the control handle or proximal end of a medical device. The medical device is capable of moving in up to six ranges of motion. In one embodiment a first motor is connected through a drive screw to a handle controller to move the medical device forward and backward. A second motor is connected to drive wheels effective to rotate the medical device clockwise and counter-clockwise. A third motor drives a series of gears that are connected to one or more control members on the medical device, this being effective to deflect a tip of the medical device so that movement of the third motor causes such deflection. A control unit is connected to all three motors.

A model-based neuromechanical controller for a robotic limb having at least one joint includes a finite state machine configured to receive feedback data relating to the state of the robotic limb and to determine the state of the robotic limb, a muscle model processor configured to receive state information from the finite state machine and, using muscle tendon lever arm and muscle tendon length equations and reflex control equations in a neuromuscular model, to determine at least one desired joint torque or stiffness command to be sent to the robotic limb, and a joint command processor configured to command the biomimetic torques and stiffnesses determined by the muscle model processor at the robotic limb joint. The feedback data is preferably provided by at least one sensor mounted at each joint of the robotic limb. In a preferred embodiment, the robotic limb is a leg and the finite state machine is synchronized to the leg gait cycle.

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Port Washington, NY, United States of America

Michael Frederick Eilenberg