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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.

Autonomous Underwater Vehicles (AUVs) are required to carry out a mission with minimum supervision. Often, the AUV's hardware integrity is compromised amidst operation; thus, jeopardising the mission's success. Thruster failures, for example, may affect AUVs locomotion. Following a thruster failure, the plan may require changes to compensate, if possible, for the loss of mobility. In this paper, we present an algorithm that identifies thruster failures in run-time. Moreover, the algorithm corrects the vehicle's dynamical model to incorporate the defective thruster. The algorithm uses a Mixture of Gaussians representation for the vehicle's state. Variational Bayes Approximation has been utilised to yield the filtering equations. As indicated by experimental evaluation, the algorithm detects thruster-failure events correctly; and, in turn, learns an accurate dynamical model of the vehicle at its current state. Experiments were carried out on a real platform in a wave tank at Heriot-Watt University.

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.

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.