Abstract

Robotic surgical systems are becoming widely used in various surgical specialties ranging from urologic and gynecological surgery to cardiothoracic surgery.  The state-of-the-art robotic surgical systems represent tremendous improvements over manual minimally invasive surgery, with 6 degrees-of-freedom manipulators that provide improved dexterity, and immersive interfaces that provide improved hand-eye coordination.  These systems are also becoming platforms for information augmentation.  However, the state-of-the-art robotic surgical systems still have substantial shortcomings:   Robotic surgical manipulations are slower than open surgical manipulations. Robotic surgical systems lack high fidelity haptic feedback. And, issues with situational awareness still remain.

In this talk, I will introduce the current state of our research towards the development of intelligent robotic surgical assistants.  The goal of this research is to develop robotic surgical systems that can act more like surgical assistants and less like master-slave controlled tools.  In the proposed paradigm, the robotic surgical system will have subtask automation capabilities for performing basic low-level manipulation tasks. The subtask automation will allow the surgeon to have a high-level interaction with the system rather than controlling it through low-level direct teleoperation.  Such a system would potentially reduce tedium from simple, repetitive tasks; assist the surgeon in complex manipulation tasks; and reduce the cognitive load on the surgeon.

Automated execution of surgical tasks requires development of new robotic planning, perception, and control algorithms for robustly performing robotic manipulation under substantial uncertainty.  The presentation will introduce our recent work on these three aspects of the problem.  I will first focus on our research on robotic perception algorithms.  Specifically, I will present algorithms for estimation of deformable object boundary constraints and material parameters, and for localization and tracking of surgical thread.  I will then introduce our work on planning algorithms, which focus on needle path planning for surgical suturing, and optimal needle grasp and entry port planning.  Finally, I will present control algorithms for needle driving and knot tying.

Bio

1. Cenk Cavusoglu is currently a Professor at the Department of Electrical Engineering and Computer Science of Case Western Reserve University (CWRU), with secondary appointments in Biomedical Engineering, and Mechanical and Aerospace Engineering.  He received the B.S. degree in Electrical and Electronic Engineering from the Middle East Technical University, Ankara, Turkey, in 1995, and the M.S. and Ph.D. degrees in Electrical Engineering and Computer Sciences from the University of California, Berkeley, in 1997 and 2000, respectively.  He was a Visiting Researcher at the INRIA Rhones-Alpes Research Center, Grenoble, France (1998); a Postdoctoral Researcher and Lecturer at the University of California, Berkeley (2000-2002); and, a Visiting Associate Professor at Bilkent University, Ankara, Turkey (2009-2010).

Dr. Cavusoglu’s research spans the general areas of robotics and human-machine interfaces with special emphasis on medical robotics, and haptics.  Specifically, for the past twenty years, he has been conducting research on all of the different aspects of medical robotic systems from control, mechanism, and system design, to human-machine interfaces, haptics, and algorithms.

More information on Dr. Cavusoglu’s research can be found at his homepage at

http://engr.case.edu/cavusoglu_cenk/