Robotic cardiac catheterization using ultrasound (US) imaging catheters provides real time imaging from within the heart while reducing the difficulty in manually steering a four degree-of-freedom (4-DOF) catheter. Accurate robotic catheter navigation in the heart is challenging due to a variety of disturbances including cyclical physiological motions, such as respiration. In this work we compensate for respiratory motion by using an Extended Kalman Filter (EKF) to predict target motion and by applying the predictions to steer the US imaging catheter. The system performance was measured in bench top experiments with phantom vasculature. The robotic system with predictive filtering tracked cyclically moving targets with 1.59 mm and 0.72° mean error. Accurately tracking moving structures can improve intra-procedural treatments and visualization.

Cardiac catheterization with ultrasound (US) imaging catheters provides real time US imaging from within the heart, but manually navigating a four degree of freedom (DOF) imaging catheter is difficult and requires extensive training. Existing work has demonstrated robotic catheter steering in constrained bench top environments. Closed-loop control in an unconstrained setting, such as patient vasculature, remains a significant challenge due to friction, backlash, and physiological disturbances. In this paper we present a new method for closed-loop control of the catheter tip that can accurately and robustly steer 4-DOF cardiac catheters and other flexible manipulators despite these effects. The performance of the system is demonstrated in a vasculature phantom and an in vivo porcine animal model. During bench top studies the robotic system converged to the desired US imager pose with sub-millimeter and sub-degree-level accuracy. During animal trials the system achieved 2.0 mm and 0.65° accuracy. Accurate and robust robotic navigation of flexible manipulators will enable enhanced visualization and treatment during procedures.

This paper presents the design and control of a teleoperated robotic system for dexterous micromanipulation tasks at the meso-scale, specifically open microsurgery. Robotic open microsurgery is an unexplored yet potentially a high impact area of surgical robotics. Microsurgical operations, such as microanastomosis of blood vessels and reattachment of nerve fibers, require high levels of manual dexterity and accuracy that surpass human capabilities. A 3-DoF robotic wrist is designed and built based on a spherical five-bar mechanism. The wrist is attached to a 3-axis commercial off-the-shelf linear stage, achieving a fully dexterous system. Design requirements are determined using motion data collected during a simulated microanastomosis operation. The wrist design is optimized to maximize workspace and manipulability. The system is teleoperated using a haptic device, and has the required bandwidth to replicate microsurgical motions. The system was successfully used in a micromanipulation task to stack 1 mm-diameter metal spheres. The micromanipulation system presented here may improve surgical outcomes during open microsurgery by offering better accuracy and dexterity to surgeons.

In this paper we have rapidly prototyped customized, highly-sensitive, mm-scale multi-axis force sensors for medical applications. Using a composite laminate batch fabrication process with biocompatible constituent materials, we have fabricated a fully-integrated, 10×10 mm three-axis force sensor with up to 5 V/N sensitivity and RMS noise on the order of ~1.6 mN, operational over a range of -500 to 500 mN in the x- and y-axes, and -2.5 to 2.5 N in the z-axis. Custom foil-based strain sensors were fabricated in parallel with the mechanical structure, obviating the need for post-manufacturing alignment and assembly. The sensor and its custom-fabricated signal conditioning circuitry fit within a 1×1×2 cm volume to realize a fully-integrated force transduction platform with potential haptics and control applications in minimally-invasive surgical tools. The form factor, biocompatibility, and cost of the sensor and signal conditioning makes this method ideal for rapid-prototyping low-cost, mm-scale distal force sensors. Sensor performance is validated in a simulated tissue palpation task using a robotic master-slave platform.