Magnetic resonance imaging (MRI) devices provide magnetic field gradients, which can be used to remotely actuate magnetic robots that may one day carry out medical tasks, such as diagnosis, drug delivery, or therapeutic-laser-based procedures, in addition to the high-resolution tissue images for diagnosis. However, in comparison with magnetic systems that are custom designed solely for magnetic actuation, the magnetic environment of an MRI device is constrained by the requirements for imaging, which reduces the number of active degrees of freedom available for magnetic actuation. Moreover, the current MRI-powered untethered robots are limited to translational magnetic pulling in three dimensions only. In this article, we propose a design for an untethered magnetic robot that can rotate in a three-dimensional liquid volume in magnetic environments, like those in MRI devices. We demonstrate rotational actuation of our robot inside a commercially available MRI gradient coil. Two kinds of near-neutrally buoyant robot designs are proposed, where each design has particular advantages. Design methodologies, analysis of rotational performance, closed-loop orientation control of up to 2.51 rad of orientation change with maximum net displacement of 18.4 mm, and angular velocity control (0.2-0.63 rad/s) of these robot designs are presented.

A reliable, real time, multi-sensor fusion functionality is crucial for localization of actively controlled capsule endoscopy robots, which are an emerging, minimally invasive diagnostic and therapeutic technology for the gastrointestinal (GI) tract. In this study, we propose a novel multi-sensor fusion approach based on a particle filter that incorporates an online estimation of sensor reliability and a non-linear kinematic model learned by a recurrent neural network. Our method sequentially estimates the true robot pose from noisy pose observations delivered by multiple sensors. We experimentally test the method using 5 degree-of-freedom (5-DoF) absolute pose measurement by a magnetic localization system and a 6-DoF relative pose measurement by visual odometry. In addition, the proposed method is capable of detecting and handling sensor failures by ignoring corrupted data, providing the robustness expected of a medical device. Detailed analyses and evaluations are presented using ex vivo experiments on a porcine stomach model, proving that our system achieves high translational and rotational accuracies for different types of endoscopic capsule robot trajectories.

In the last decade, researchers and medical device companies have made major advances towards transforming passive capsule endoscopes into active medical robots. One of the major challenges is to endow capsule robots with accurate perception of the environment inside the human body, which will provide necessary information and enable improved medical procedures. We extend the success of deep learning approaches from various research fields to the problem of sensor fusion for endoscopic capsule robots in the case of asynchronous and asymmetric sensor data without any need of calibration between sensors. The results performed on real pig stomach datasets show that our method achieves high precision for both translational and rotational movements and contains various advantages over traditional sensor fusion techniques.

Concentric tube robots are needle-sized manipulators which have been investigated for use in minimally invasive surgeries. It was noted early in the development of these devices that elastic energy storage can lead to rapid snapping motion for designs with moderate to high tube curvatures. Substantial progress has recently been made in the concentric tube robot community in designing snap-free robots, planning stable paths, and characterizing conditions that result in snapping for specific classes of concentric tube robots. However, a general measure for how stable a given robot configuration is has yet to be proposed. In this paper, we use bifurcation and elastic stability theory to provide such a measure, as well as to produce a test for determining whether a given design is snap-free (i.e. whether snapping can occur anywhere in the unloaded robot's workspace). These results are useful in designing, planning motions for, and controlling concentric tube robots with high curvatures.

In designing concentric tube robots, it is often desirable to use curvatures that are as high as possible. However, with high curvatures comes the potential for elastic instabilities. This can be addressed by motion planning or by designing the robot to preclude the possibility of instability. In this paper, we pursue the latter. Our primary contribution is to show that local bifurcation theory can be used to predict the existence of elastic instability anywhere in the workspace of robots with arbitrarily many tubes, with straight transmissions. We show how to use these results to design robots that are guaranteed to remain elastically stable at all times.

Concentric tube robots are thin, tentacle-like devices that can move along curved paths and can potentially enable new, less invasive surgical procedures. Safe and effective operation of this type of robot requires that the robot's shaft avoid sensitive anatomical structures (e.g., critical vessels and organs) while the surgeon teleoperates the robot's tip. However, the robot's unintuitive kinematics makes it difficult for a human user to manually ensure obstacle avoidance along the entire tentacle-like shape of the robot's shaft. We present a motion planning approach for concentric tube robot teleoperation that enables the robot to interactively maneuver its tip to points selected by a user while automatically avoiding obstacles along its shaft. We achieve automatic collision avoidance by precomputing a roadmap of collision-free robot configurations based on a description of the anatomical obstacles, which are attainable via volumetric medical imaging. We also mitigate the effects of kinematic modeling error in reaching the goal positions by adjusting motions based on robot tip position sensing. We evaluate our motion planner on a teleoperated concentric tube robot and demonstrate its obstacle avoidance and accuracy in environments with tubular obstacles.

The needle-sized surgical tools used in arthroscopy, otolaryngology, and other surgical fields could become even more valuable to surgeons if endowed with the ability to navigate around sharp corners to manipulate or visualize tissue. We present a needle-sized wrist design that grants this ability. It can be easily interfaced with manual tools or concentric tube robots and is straightforward and inexpensive to manufacture. The wrist consists of a nitinol tube with several asymmetric cutouts, actuated by a tendon. Perhaps counter-intuitively, within this seemingly simple design concept, design optimization is challenging due to the number of parameters available and nonlinearities in material properties. In this paper, we examine a subset of possible geometries and derive kinematic and static models. Experimental results with a 1.16 mm diameter prototype validate the models. Lastly, we provide a discussion summarizing the lessons learned in our early experience designing and fabricating wrists of this type.

Concentric tube robots can enable new clinical interventions if they are able to pass through soft tissue, deploy along desired paths through open cavities, or travel along winding lumens. These behaviors require the robot to deploy in such a way that the curved shape of its shaft remains unchanged as the tip progresses forward (i.e., "follow-the-leader" deployment). Follow-the-leader deployment is challenging for concentric tube robots due to elastic (and particularly torsional) coupling between the tubes that form the robot. However, as we show in this paper, follow-the-leader deployment is possible, provided that tube precurvatures and deployment sequences are appropriately selected. We begin by defining follow-the-leader deployment and providing conditions that must be satisfied for a concentric tube robot to achieve it. We then examine several useful special cases of follow-the-leader deployment, showing that both circular and helical precurvatures can be employed, and provide an experimental illustration of the helical case. We also explore approximate follow-the-leader behavior and provide a metric for the similarity of a general deployment to a follow-the-leader deployment. Finally, we consider access to the hippocampus in the brain to treat epilepsy, as a motivating clinical example for follow-the-leader deployment.

Concentric tube robots exhibit complex workspaces due to the way their component tubes bend and twist as they interact with one another. This paper explores ways to compute and characterize their workspaces. We use Monte Carlo random samples of the robot's joint space and a discrete volumetric workspace representation, which can describe both reachability and redundancy. Experiments on two physical prototypes are provided to illustrate the proposed approach.

Concentric tube continuum robots provide an infinite-dimensional design space, consisting of individual tube space curves and other tube parameters. Even when design choices are made to restrict the design space to a small number of discrete parameters, ad hoc selection of parameter values to achieve coverage of a desired volume, in the presence of geometric workspace constraints, is essentially impossible - even for experienced researchers. General design algorithms proposed to date have focused on reaching a discrete set of specific points, and have made non-physical approximations in the robot model (most significantly assuming infinite torsional rigidity), to speed up model computation. In this paper, we extend prior algorithms to use more accurate models and incorporate volume-based objectives. These extensions are illustrated in a case study on the design of a concentric tube robot for endonasal pituitary surgery. We show that volume-based design optimization increases the reachable percentage of the surgical workspace by an average of approximately 50%, in comparison to various sets of manually selected design parameters. We conclude that volume-based objectives should be included in future multi-objective design optimization procedures for concentric tube continuum robots.

Continuum robots have opened a broad array of applications to robotics in general, and the concentric tube continuum robots promise many benefits in medicine. Many people intuitively assume that these robots can deploy along a curved trajectory, in such a way that the curved shape of the robot's shaft remains unchanged as the tip progresses forward (i.e. “follow-the-leader” deployment). This capability would be useful in advancing along winding lumens (e.g. blood vessels, lung bronchi, etc.), as well as when the device is embedded in soft tissue and used as a steerable needle. However, in this paper we show that deploying in a follow-the-leader manner is not possible except in very special cases of tube precurvatures, combined with specific deployment sequences. We also show that follow-the-leader deployment is not possible even for many of the “simple” cases where one might intuitively expect it to be. Fortunately, useful special cases of perfect follow-the-leader behavior do exist, and we provide examples and describe the conditions that must be satisfied for this to be possible. We also study approximate follow-the-leader behavior, proposing a metric to quantify the similarity of a general deployment to a follow-the-leader deployment.

The high incidence of intracerebral hemorrhages, together with a 40% mortality rate, provide strong motivation for enhancements in the treatment methods available to physicians. To minimize the disruption to healthy brain tissue associated with gaining access to the surgical site that is imposed by traditional open or endoscopic surgical intervention, we propose a new minimally-invasive, image-guided, robotic approach that provides articulation within the lesion at the tip of a needle. In this paper we present a biocompatible and sterilizable robot, together with an image-guidance approach designed to deliver the tip of the needle accurately to the blood clot and to move it within the clot, to aspirate it. An experimental evaluation demonstrates removal of 92% of the target clot tissue in a proof-of-concept phantom study.

Steerable needles can potentially increase the accuracy of needle-based diagnosis and therapy delivery, provided they can be adequately controlled based on medical image information. We propose a novel sliding mode control law that can be used to deliver the tip of a flexible asymmetric-tipped needle to a desired point, or to track a desired trajectory within tissue. The proposed control strategy requires no a priori knowledge of model parameters, has bounded input speeds, and requires little computational resources. We show that if the standard nonholonomic model for tip-steered needles holds, then the control law will converge to desired targets in a reachable workspace, within a tolerance that can be defined by the control parameters. Experimental results validate the control law for target points and trajectory following in phantom tissue and ex vivo liver. Experiments with targets that move during insertion illustrate robustness to disturbances caused by tissue deformation.

We describe transnasal skull base surgery, including the current clinical procedure and the ways in which a robotic system has the potential to enhance the current standard of care. The available workspace is characterized by segmenting medical images and reconstructing the available 3D geometry. We then describe thin, ¿tentacle-like¿ robotic tools with shafts constructed from concentric tube robots, and an actuation unit designed to robotically control them in a teleoperated setting. Lastly, we discuss the results of a proof-of-concept study in a cadaveric specimen, illustrating the ability of the robot to access clinically relevant skull base targets.