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The shape of a concentric tube robot depends not only on the relative rotations and translations of its constituent tubes, but also on the history of relative tube displacements. Existing mechanics-based models neglect all history-dependent phenomena with the result that when calibrated on experimental data collected over a robot’s workspace, the maximum tip position error can exceed 8 mm for a 200-mm-long robot. In this paper, we develop a model that computes the bounding kinematic solutions in which Coulomb friction is acting either to maximize or minimize the relative twisting between each pair of contacting tubes. The path histories associated with these limiting cases correspond to first performing all tube translations and then performing relative tube rotations of sufficient angle so that the maximum Coulomb friction force is obtained along the interface of each contacting tube pair. The robot tip configurations produced by these path histories are shown experimentally to bound position error with respect to the estimated frictionless model compared to path histories comprised of translation or mixed translation and rotation. Intertube friction forces and torques are computed as proportional to the intertube contact forces. To compute these contact forces, the standard zero-clearance assumption that constrains the concentrically combined tubes to possess the same centerline is relaxed. The effects of clearance and friction are explored through numerical and physical experiments and it is shown that friction can explain much of the prediction error observed in existing models. This model is not intended for real-time control, but rather for path planning—to provide error bounds and to inform how the ordering of tube rotations and translations can be used to reduce the effect of friction.

International audience; While all minimally invasive procedures involve navigating from a small incision in the skin to the site of the intervention, it has not been previously demonstrated how this can be done 10 autonomously. To show that autonomous navigation is possible, we investigated it in the hardest place to do it-inside the beating heart. We created a robotic catheter that can navigate through the blood-filled heart using wall-following algorithms inspired by positively thigmotactic animals. The catheter employs haptic vision, a hybrid sense using imaging for both touch-based surface identification and force sensing, to accomplish wall following inside the blood-filled heart. 15 Through in vivo animal experiments, we demonstrate that the performance of an autonomously-controlled robotic catheter rivals that of an experienced clinician. Autonomous navigation is a fundamental capability on which more sophisticated levels of autonomy can be built, e.g., to perform a procedure. Similar to the role of automation in fighter aircraft, such capabilities can free the clinician to focus on the most critical aspects of the procedure while providing precise and 20 repeatable tool motions independent of operator experience and fatigue.

Magnetic resonance navigation (MRN) offers the potential for real-time steering of drug particles and cells to targets throughout the body. In this technique, the magnetic gradients of an MRI scanner perform image-based steering of magnetically-labelled therapeutics through the vasculature and into tumours. A major challenge of current techniques for MRN is that they alternate between pulse sequences for particle imaging and propulsion. Since no propulsion occurs while imaging the particles, this results in a significant reduction in imaging frequency and propulsive force. We report a new approach in which an imaging sequence is designed to simultaneously image and propel particles. This sequence provides a tradeoff between maximum propulsive force and imaging frequency. In our reported example, the sequence can image at 27 Hz while still generating 95% of the force produced by a purely propulsive pulse sequence. We implemented our pulse sequence on a standard clinical scanner using millimetre-scale particles and demonstrated high-speed (74 mm/s) navigation of a multi-branched vascular network phantom. Our study suggests that the magnetic gradient magnitudes previously demonstrated to be sufficient for pure propulsion of micron-scale therapeutics in magnetic resonance targeting (MRT) could also be sufficient for real-time steering of these particles.

OBJECTIVE Rigid endoscopes enable minimally invasive access to the ventricular system; however, the operative field is limited to the instrument tip, necessitating rotation of the entire instrument and causing consequent tissue compression while reaching around corners. Although flexible endoscopes offer tip steerability to address this limitation, they are more difficult to control and provide fewer and smaller working channels. A middle ground between these instruments—a rigid endoscope that possesses multiple instrument ports (for example, one at the tip and one on the side)—is proposed in this article, and a prototype device is evaluated in the context of a third ventricular colloid cyst resection combined with septostomy. METHODS A prototype neuroendoscope was designed and fabricated to include 2 optical ports, one located at the instrument tip and one located laterally. Each optical port includes its own complementary metal-oxide semiconductor (CMOS) chip camera, light-emitting diode (LED) illumination, and working channels. The tip port incorporates a clear silicone optical window that provides 2 additional features. First, for enhanced safety during tool insertion, instruments can be initially seen inside the window before they extend from the scope tip. Second, the compliant tip can be pressed against tissue to enable visualization even in a blood-filled field. These capabilities were tested in fresh porcine brains. The image quality of the multiport endoscope was evaluated using test targets positioned at clinically relevant distances from each imaging port, comparing it with those of clinical rigid and flexible neuroendoscopes. Human cadaver testing was used to demonstrate third ventricular colloid cyst phantom resection through the tip port and a septostomy performed through the lateral port. To extend its utility in the treatment of periventricular tumors using MR-guided laser therapy, the device was designed to be MR compatible. Its functionality and compatibility inside a 3-T clinical scanner were also tested in a brain from a freshly euthanized female pig. RESULTS Testing in porcine brains confirmed the multiport endoscope's ability to visualize tissue in a blood-filled field and to operate inside a 3-T MRI scanner. Cadaver testing confirmed the device's utility in operating through both of its ports and performing combined third ventricular colloid cyst resection and septostomy with an endoscope rotation of less than 5°. CONCLUSIONS The proposed design provides freedom in selecting both the number and orientation of imaging and instrument ports, which can be customized for each ventricular pathological entity. The lightweight, easily manipulated device can provide added steerability while reducing the potential for the serious brain distortion that happens with rigid endoscope navigation. This capability would be particularly valuable in treating hydrocephalus, both primary and secondary (due to tumors, cysts, and so forth). Magnetic resonance compatibility can aid in endoscope-assisted ventricular aqueductal plasty and stenting, the management of multiloculated complex hydrocephalus, and postinflammatory hydrocephalus in which scarring obscures the ventricular anatomy.

Concentric tube robots comprise telescopic precurved elastic tubes. The robot's tip and shape are controlled via relative tube motions, i.e. tube rotations and translations. Non-linear interactions between the tubes, e.g. friction and torsion, as well as uncertainty in the physical properties of the tubes themselves, e.g. the Young's modulus, curvature, or stiffness, hinder accurate kinematic modelling. In this paper, we present a machine-learning-based methodology for kinematic modelling of concentric tube robots and in situ model adaptation. Our approach is based on Locally Weighted Projection Regression (LWPR). The model comprises an ensemble of linear models, each of which locally approximates the original complex kinematic relation. LWPR can accommodate for model deviations by adjusting the respective local models at run-time, resulting in an adaptive kinematics framework. We evaluated our approach on data gathered from a three-tube robot, and report high accuracy across the robot's configuration space.

This paper presents a novel, data-driven algorithm for the computation of the Remaining Useful Life (RUL) of an asset. The algorithm utilizes the asset's state history to learn a prognostic model from data. The prognostic model comprises an ensemble of Auto-Regressive (AR) models, together with a state-of-the-art classifier. The AR part of the algorithm is used to predict the system's state evolution. The classifier discriminates between healthy and faulty operation, given the asset's current state. The predicted state, as computed by the AR model, is fed to the classifier. The first time when the predicted state is classified as faulty is returned as the RUL of the system. The resulting prognostic algorithm was tested on the CMAPSS dataset as provided from NASA Ames Research Center. Cases of unknown future input trajectory as well as cases with multiple faults have been investigated.