Design and Control of Robotic Systems and Devices for Medical Applications

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The proposed research program is aimed at developing advanced robotic and intelligent systems for the next generation of minimally invasive medical interventions that will minimize invasiveness and trauma while enhancing safety, efficiency, reliability and cost-effectiveness. There is therefore a need to examine the current state of robotics-assisted medical interventions, for example by using recent developments in continuum robotics, machine learning, and “smart” actuation and sensing technologies, coupled with advances in haptics and teleoperation. The proposed research program will address this need through the development of various technologies and techniques in the context of the following applications: Framework for the Design and Real-Time Control of Concentric-Tube Robots (CTRs): A CTR is a type of continuum robotic device consisting of a set of thin concentric tubes, shaped in different curved profiles. By rotating and translating the tubes with respect to each other, their curvatures can be used to obtain different curved profiles for the overall robot and to position and orient the robot's tip while avoiding sensitive regions in and around the surgical workspace. The CTR's size constraints and shape make force sensing challenging. The flexibility and changing shape introduce significant complexity in the design of a control architecture as well as in workspace determination and path planning. Furthermore, flexibility in the CTR makes the problem of haptics-enabled teleoperation particularly challenging. The proposed framework will provide a methodology to address issues such as anatomical and other constraints imposed by particular interventions, the kinematic dexterity and workspace requirements, the imaging and other sensing modalities to be used, control issues (such as position and force control, robustness to modeling and measurement uncertainty), and haptics and teleoperation. Autonomous Systems for Surgical Training and Skills Assessment for (RA)MIS: This program of research will build on recent preliminary work conducted in controlled environments that has demonstrated the possibility of introducing autonomy in suturing tasks during Robotics-Assisted Minimally Invasive Surgery (RAMIS). The proposed research will explore how a robotic platform and a variety of sensing modalities can be used to analyze the performance of various robotics-assisted or conventional minimally invasive surgery tasks by experts. Machine learning approaches can then use the resulting data to train a surgical robotic system to serve as an “Intelligent Virtual Expert (IVE)” so that it can be used in place of an actual expert surgeon for offline (RA)MIS training. Application of approaches such as Deep Neural Networks, Recurrent Neural Networks, Reinforcement Learning and their variations will be investigated.