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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.

This paper presents a filtering algorithm for non-linear systems in the case of sensor degradation. The algorithm adapts the relative importance of the sensor measurements, compared to the model predictions, in real time; yielding a filter that is robust to noisy observations and sensor blackouts. The filter is constructed using a Variational Bayes Approximation of the conditional probability distribution of the system's state; i.e., the probability distribution of the state, given the measurements from the sensors. The algorithm is evaluated both in simulation and experimentally on a robotic platform. In the experiments, the sensor measurements from an Autonomous Underwater Vehicle (AUV) are altered artificially. The sensor output is either corrupted with outliers or manually stuck to a constant value; simulating in this fashion a sensor defect. In both cases, the filter reconstructs the robot's state accurately, thus enabling the vehicle to resume with mission execution.

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.

Navigation is instrumental in the successful deployment of Autonomous Underwater Vehicles (AUVs). Sensor hardware is installed on AUVs to support navigational accuracy. Sensors, however, may fail during deployment, thereby jeopardizing the mission. This work proposes a solution, based on an adaptive dynamic model, to accurately predict the navigation of the AUV. A hydrodynamic model, derived from simple laws of physics, is integrated with a powerful non-parametric regression method. The incremental regression method, namely the Locally Weighted Projection Regression (LWPR), is used to compensate for un-modeled dynamics, as well as for possible changes in the operating conditions of the vehicle. The augmented hydrodynamic model is used within an Extended Kalman Filter, to provide optimal estimations of the AUV’s position and orientation. Experimental results demonstrate an overall improvement in the prediction of the vehicle’s acceleration and velocity.

Successful ophthalmic surgeries using intraocular untethered microrobots or tethered robotic microtools require methods to robustly track the microdevices in the posterior of the human eye. The dimensions and specularities of the microdevices are major obstacles for accurate tracking. In addition, the optical structure of the human eye makes it challenging to keep the objects of interest constantly in focus, resulting in blurred images. In this paper, the advantages of using different colorspaces for intraocular tracking are examined. After selection of the appropriate colorspace, thresholds that ensure maximum separation of the device from the background are calculated. Based on trained color histograms, level sets are used to track in real time, and the use of statistical shape information is incorporated in the existing tracking framework. The efficacy of the algorithm is demonstrated by tracking a microrobot in a model eye, using a custom made ophthalmoscope and off-the-shelf ophthalmoscopy lenses. With the appropriate colorspace and threshold selection, tracking errors are minimized and are further diminished using shape information.