LCSR Seminar: 2 presentations
12:00pm Presentation: Shahriar Sefati
Title: FBG-Based Position Estimation of Highly Deformable Continuum Manipulators: Model-Dependent vs. Data-Driven Approaches
Abstract: Fiber Bragg Grating (FBG) is a promising strategy for sensing in flexible medical instruments and continuum manipulators. Conventional shape sensing techniques using FBG involve finding the curvature at discrete FBG active areas and integrating curvature over the length of the continuum dexterous manipulator (CDM) for tip position estimation. However, due to limited number of sensing locations and many geometrical assumptions, these methods are prone to large error propagation especially when the CDM undergoes large deflections or interacts with obstacles. In this talk, I will give an overview of complications in using the conventional tip position estimation methods that are dependent on sensor model, and propose a new data-driven method that overcomes these challenges. The method’s performance is evaluated on a CDM developed for orthopedic applications, and the results are compared to conventional model-dependent methods during large deflection bending and interactions with obstacles.
Bio: Shahriar Sefati is a Ph.D. candidate in the Department of Mechanical Engineering at Johns Hopkins University, and affiliated with Biomechanical and Image-guided Surgical Systems (BIGSS) as part of Laboratory for Computational Sensing and Robotics (LCSR). He received his B.S. degree in Mechanical Engineering from Sharif University of Technology in 2014 and M.S.E. degree in Computer Science from Johns Hopkins University in 2017. He has also been a robotics and controls engineer intern at Verb Surgical, Inc. in summer 2018. Shahriar’s research focuses on continuum manipulators and flexible robotics for less-invasive surgery.
12:30 Presenation: Iulian Iordachita
Title: Safe Robot-assisted Retinal Surgery
Abstract: Modern patient health care involves maintenance and restoration of health by medication or surgical intervention. This research talk focuses solely on surgical procedures, like retinal surgery, where surgeons perform high-risk but necessary treatments whilst facing significant technical and human limitations in an extremely constrained environment. Inaccuracy in tool positioning and movement are among the important factors limiting performance in retinal surgery. The challenges are further exacerbated by the fact that in the majority of contact events, the forces encountered are below the tactile perception of the surgeon. Inability to detect surgically relevant forces leads to a lack of control over potentially injurious factors that result in complications. This situation is less than optimal and can significantly benefit from the recent advances in robot assistance, sensor feedback and human machine interface design. Robotic assistance may be ideally suited to address common problems encountered in most (micro)manipulation tasks, including hand tremor, poor tool manipulation resolution, and accessibility, and to open up surgical strategies that are beyond human capability. Various force sensors have been developed for microsurgery and minimally invasive surgery. Optical fibers strain sensors, specifically fiber Bragg gratings (FBG), are very sensitive, capable of detecting sub-micro strain chances, are very small in size, lightweight, biocompatible, sterilizable, multiplexable, and immune to electrostatic and electromagnetic noise. In retinal surgery, FBG-based force-sensing tools can provide the necessary information that will guide the surgeon through any maneuver, effectively reduce forces with improved precision and potentially improve the safety and efficacy of the surgical procedure. Optical fiber-based sensorized instruments in correlation with robot-assisted (micro)surgery could address the current limitations in surgery by integrating novel technology that transcend human sensor-motor capabilities into robotic systems that provide both, real-time significant information and physical support to the surgeon, with the ultimate goal of improving clinical care and enabling novel therapies.
