Electrically-tunable positioning of topological defects in liquid crystals.

Topological defects are a consequence of broken symmetry in ordered systems and are important for understanding a wide variety of phenomena in physics. In liquid crystals (LCs), defects exist as points of discontinuous order in the vector field that describes the average orientation of the molecules in space and are crucial for explaining the fundamental behaviour and properties of these mesophases. Recently, LC defects have also been explored from the perspective of technological applications including self-assembly of nanomaterials, optical-vortex generation and in tunable plasmonic metamaterials. Here, we demonstrate the fabrication and stabilisation of electrically-tunable defects in an LC device using two-photon polymerisation and explore the dynamic behaviour of defects when confined by polymer structures laser-written in topologically discontinuous states. We anticipate that our defect fabrication technique will enable the realisation of tunable, 3D, reconfigurable LC templates towards nanoparticle self-assembly, tunable metamaterials and next-generation spatial light modulators for light-shaping.

Conditions for reliable grip force and jaw angle estimation of da Vinci surgical tools.

PURPOSE: This work presents an estimation technique as well as corresponding conditions which are necessary to produce an accurate estimate of grip force and jaw angle on a da Vinci surgical tool using back-end sensors alone. METHODS: This work utilizes an artificial neural network as the regression estimator on a dataset acquired from custom hardware on the proximal and distal ends. Through a series of experiments, we test the effect of estimation accuracy due to change in operating frequency, using the opposite jaw, and using different tools. A case study is then presented comparing our estimation technique with direct measurements of material response curves on two synthetic tissue surrogates. RESULTS: We establish the following criteria as necessary to produce an accurate estimate: operate within training frequency bounds, use the same side jaw, and use the same tool. Under these criteria, an average root mean square error of 1.04 mN m in grip force and 0.17 degrees in jaw angle is achieved. Additionally, applying these criteria in the case study resulted in direct measurements which fell within the 95% confidence bands of our estimation technique. CONCLUSION: Our estimation technique, along with important training criteria, is presented herein to further improve the literature pertaining to grip force estimation. We propose the training criteria to begin establishing bounds on the applicability of estimation techniques used for grip force estimation for eventual translation into clinical practice.

Blended shared control utilizing online identification : Regulating grasping forces of a surrogate surgical grasper.

PURPOSE: Surgical robots are increasingly common, yet routine tasks such as tissue grasping remain potentially harmful with high occurrences of tissue crush injury due to the lack of force feedback from the grasper. This work aims to investigate whether a blended shared control framework which utilizes real-time identification of the object being grasped as part of the feedback may help address the prevalence of tissue crush injury in robotic surgeries. METHODS: This work tests the proposed shared control framework and tissue identification algorithm on a custom surrogate surgical robotic grasping setup. This scheme utilizes identification of the object being grasped as part of the feedback to regulate to a desired force. The blended shared control is arbitrated between human and an implicit force controller based on a computed confidence in the identification of the grasped object. The online identification is performed using least squares based on a nonlinear tissue model. Testing was performed on five silicone tissue surrogates. Twenty grasps were conducted, with half of the grasps performed under manual control and half of the grasps performed with the proposed blended shared control, to test the efficacy of the control scheme. RESULTS: The identification method resulted in an average of 95% accuracy across all time samples of all tissue grasps using a full leave-grasp-out cross-validation. There was an average convergence time of [Formula: see text] ms across all training grasps for all tissue surrogates. Additionally, there was a reduction in peak forces induced during grasping for all tissue surrogates when applying blended shared control online. CONCLUSION: The blended shared control using online identification more successfully regulated grasping forces to the desired target force when compared with manual control. The preliminary work on this surrogate setup for surgical grasping merits further investigation on real surgical tools and with real human tissues.