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TITLE: SOFT-RIGID HYBRID ROBOTS FOR LASER-ASSISTED ROBOTIC SURGERIES
ABSTRACT: Lasers have become an essential tool in many surgical applications due to their ability to selectively ablate tissue based on light absorption which varies with laser wavelength. This selectivity can minimize damage to healthy tissue, shorten recovery time, and reduce the risk of postoperative complications. To further enhance laser surgery techniques, robotic technology can be utilized to improve the precision of laser targeting by enabling automatic closed-loop control. One of the robotic technologies that can be leveraged in these medical applications is soft robotics. The inherent compliance, flexibility, and robustness of soft robots make them an ideal technology for delicate surgical environments. This dissertation prospectus focuses on developing a laser-steering robot built upon soft robotic technology. To achieve this, two key areas are addressed: the introduction of the soft-rigid hybrid (SHY) robot concept to tackle current challenges in soft robotics in general and the design and development of a SHY robot tailored for laser surgeries.
Some of the key challenges in soft robotics include (1) fabrication inconsistency, (2) scalability, and (3) precise motion controllability. Tackling these challenges, a layer-by-layer fabrication technique that enables the integration of soft, rigid, flexible, and conductive materials is introduced. This strategy is used to build SHY robots that seamlessly integrate a soft-foldable actuator, a proprioceptive sensor, and a mechanical controller into a compact form factor. With this integration, the SHY robot combines the flexibility and compliance of soft robots and the stability and precise motion control of rigid robots. Additionally, onboard proprioceptive sensors enable real-time shape sensing, which can also be used for feedback control.
The developed fabrication technique and robot concept have been applied to create a SHY robot designed for laser surgery applications. The key objectives in its development are to enhance the accuracy of the robot's movements and increase its autonomy. Optical proprioceptive sensors are integrated to provide real-time feedback on the robot's configuration, and machine learning techniques are leveraged for the sensor calibration. Improving the precision and accuracy of the robot and laser beam movement, a closed-loop control is planned to be implemented. Finally, the feasibility of the robot will be evaluated through in-vitro experiments, where it will autonomously guide the laser beam to specific target locations within the in-vitro model.
COMMITTEE: ADVISOR/CHAIR Professor Sheila Russo, ME/MSE; Professor Tommaso Ranzani, ME/MSE/BME; Professor Andrew Sabelhaus, ME/SE
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