The navigation of magnetic medical robots typically relies on localizing an actuated, intracorporeal, ferromagnetic body and back-computing a necessary field and gradient that would result in a desired wrench on the device. Uncertainty in this localization degrades the precision of force transmission. Reducing applied force uncertainty may enhance tasks such as in vivo navigation of miniature robots, actuation of magnetically guided catheters, tissue palpation, as well as simply ensuring a bound on forces applied on sensitive tissue. In this paper, we analyze the effects of localization noise on force uncertainty by using sensitivity ellipsoids of the magnetic force Jacobian and introduce an algorithm for uncertainty reduction. We validate the algorithm in both a simulation study and in a physical experiment. In simulation, we observe reductions in estimated force uncertainty by factors of up to 2.8 and 3.1 when using one and two actuating magnets, respectively. On a physical platform, we demonstrate a force uncertainty reduction by a factor of up to 2.5 as measured using an external sensor. Being the first consideration of force uncertainty resulting from noisy localization, this paper provides a strategy for investigators to minimize uncertainty in magnetic force transmission.

In this paper, we demonstrate velocity-level closed-loop control of a tethered magnetic capsule endoscope that is actuated via serial manipulator with a permanent magnet at its end-effector. Closed-loop control (2 degrees-of-freedom in position, and 2 in orientation) is made possible with the use of a real-time magnetic localization algorithm that utilizes the actuating magnetic field and thus does not require additional hardware. Velocity control is implemented to create smooth motion that is clinically necessary for colorectal cancer diagnostics. Our control algorithm generates a spline that passes through a set of input points that roughly defines the shape of the desired trajectory. The velocity controller acts in the tangential direction to the path, while a secondary position controller enforces a nonholonomic constraint on capsule motion. A soft nonholonomic constraint is naturally imposed by the lumen while we enforce a strict constraint for both more accurate estimation of tether disturbance and hypothesized intuitiveness for a clinician's teleoperation. An integrating disturbance force estimation control term is introduced to predict the disturbance of the tether. This paper presents the theoretical formulations and experimental validation of our methodology. Results show the system's ability to achieve a repeatable velocity step response with low steady-state error as well as ability of the tethered capsule to maneuver around a bend.

Minimally invasive robotic surgery techniques are becoming popular thanks to their enhanced patient benefits, including shorter recovery time, better cosmetic results and reduced discomforts. Less invasive procedures would be achieved with the use of Medical Capsule Robots (MCRs). These devices are characterized by low power requirements and small dimensions as well as uncompromising safety. MCRs operate wirelessly in abdominal Minimally Invasive Surgery (MIS) and Natural Orifice Transluminal Endoscopic Surgery (NOTES) or in the Gastrointestinal (GI) tract. The design process of MCRs, however, is expensive and time consuming. A platform for rapid prototyping MCRs is needed so that MCR researchers can reduce development costs and spend more time in studying innovative MCR applications. In this work, we introduce an open source modular platform geared toward rapid prototyping MCRs. To speed up the prototyping process, the MCR is programmed using TinyOS instead of bare-bone C. We present the hardware architecture of the platform, and the motivation for using TinyOS. To show the viability of TinyOS, we present results from an experiment involving sensing, actuation and wireless communication. This work lays the foundation for our future goal of building an integrated design environment for the design, analysis and simulation of MCRs.

Minimally invasive surgical techniques are becoming popular due to their enhanced patient benefits. Less invasive procedures can be achieved with the use of wireless Medical Capsule Robots (MCRs). MCRs are low powered and small in size and can be used for physiological parameter monitoring, therapy delivery, and biopsy sampling. Designing MCRs from the ground up is a costly and time consuming process. In this work, we present a flexible modular architecture to facilitate the design of MCRs and propose using TinyOS as the operating system. To assess the architecture and validate the feasibility of TinyOS, we implement a closed-loop control of a sensor-actuator system and compare the results with a traditional MCR built based on an 8051 microcontroller (MCU) programmed in plain C. Similar performances from the two approaches lead us to conclude that TinyOS is a valid option to implement a modular architecture for designing MCRs.