RBE Ph.D. Dissertation Proposal
An Asynchronous Simulation Framework for Multi-User Interactive Collaboration: Application to Robot-Assisted Surgery
Abstract: Robot-assisted surgery is a high-risk application involving human operated machines. While the use of robots during surgical procedures has been shown to significantly improve surgery-related outcomes, the surgery's overall performance quality is directly influenced by the skills of the surgeon and the Operating Room (OR) team. However, surgical performance quality is currently measured using a wide variety of metrics that encompass a range of outcomes (e.g., patient pain and recovery, surgeon’s comfort, duration of procedure), and additional research is needed to better understand the optimal criteria for benchmarking surgical performance. Presently, surgeons are trained to perform robot-assisted surgeries using interactive simulators. However, in the absence of well-defined performance standards, these simulators are limited and lack the capacity to account for the various complexities of a real-world surgical procedure. Because interactive simulators are typically designed for specific robots that perform a small number of tasks and involve only a single user, they are inflexible in terms of their portability to different robots and the inclusion of multiple operators (e.g., nurses, medical assistants). Additionally, while most simulators provide high-quality visuals, simplification techniques are often employed to avoid stability issues for physics computation, contact dynamics and multi-manual interaction. This study addresses the limitations of existing simulators by outlining various specifications required to develop techniques that mimic real-world interactions and collaboration. Features such as multi-user hand-eye coordination, interaction and asynchronous force feedback are implemented as part of the design requirements. Moreover, this study focuses on the inclusion of distributed control, shared task allocation and assistive feedback -- through machine learning, secondary and tertiary operators -- alongside the primary human operator.
Professor Gregory Fischer (Advisor)
Professor Berk Calli
Professor Loris Fichera
Professor Peter Kazanzides, John Hopkins University