MechE PhD Dissertation Defense: Daniel Van Lewen

TITLE: SOFT ROBOTIC SYSTEMS FOR INTERVENTIONAL ENDOSCOPY

ABSTRACT: Interventional neurosurgery and bronchoscopy represent two significant surgical theaters where delicate tissues must be effectively contacted and target regions can require navigation of narrow, tortuous paths. Current minimally invasive techniques in these procedures are limited in the safety, access, distal dexterity, and feedback they provide. Therefore, there remains a clinical need for soft robots that can safely interact with tissues, deploy dexterous tools for surgical tasks, and navigate into deep regions to more effectively perform procedures. This thesis work focuses on the design, fabrication, and control of two soft robotic systems to address these challenges. We first propose an origami-inspired soft robotic retractor which leverages its compliant form to vary the level of tissue retraction. Thus, it can distribute contact forces when generating a surgical workspace in neurosurgery. Fabrication via a 2D layering technique facilitates the integration of multiple functionalities not only through controlled pneumatic actuation of its shape but also through the embedding of force sensing units. Actuation and sensing capabilities are validated with respect to clinical requirements and analytical models. Clinical viability of workspace generation and force feedback is demonstrated in an in-vitro environment. We further investigate how safer contact interactions and software integration can be leveraged to meaningfully operate within the deep surgical environment of the lungs. We present a millimeter-scale soft robot to address the difficulties in diagnosing early stage lung cancer within interventional bronchoscopy. Within this robot, we embed three indepen-dent degrees of freedom which enable steering toward the targeted lung branch, stabilization within the lung for increased force transmission, and the deployment of a needle at the dis-tal tip for performing biopsy. Robot performance is characterized and validated with the in-vitro biopsy of simulated tissue. Further, a semi-autonomous navigation platform is de-veloped by implementing algorithms in computer vision, preoperative robotic path planning, and external actuation. In-vitro navigation experiments show the ability to reduce surgeon workload and precisely reach the lung periphery. These soft robotic surgical platforms are demonstrated to enhance surgical capabilities within these crucial MIS procedures paving the way for more effective surgeries and improved patient outcomes.

COMMITTEE: ADVISOR Professor Sheila Russo, ME/MSE; CHAIR Professor Tommaso Ranzani, ME/MSE/BME; Professor Alyssa Pierson, ME/SE; Professor Ehab Billatos, School of Medicine