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.

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 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.

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.