Technological advancements in video equipment and biocompatible materials has enabled improvements in complex surgery through small incisions. The mastery of these laparoscopic surgical techniques is now a requirement for surgeons, however, the necessary skills are not intuitive and require hundreds of practice hours. The current state of surgical education includes animate models, inanimate physical models, and computer-based simulations, the latter of which are limited by cost, accessibility, and a lack of engagement. We propose a novel low-cost training interface that mimics the laparoscopic surgical environment using customized instruments whose movement and control are used as inputs for video games. The system is significantly less expensive than commercial systems and allows users freedom to select and play any game, enabling a take-home system with potential for higher levels of engagement, as well as familiarity and expertise with ambidextrous laparoscopic hand motion. A preliminary study compared performance on FLS (Fundamentals of Laparoscopic Surgery) testing before and after training. For a precision cutting task, groups that trained on a standard simulator or on the new system with either a non-inverted or inverted hand-instrument mapping, showed statistically significant improvements, warranting further investigation of training with this new system.

Continuum robots have significant potential for impact in surgical applications, due totheir compliance and ability to safely traverse complex and constrained environments. One class of continuum robots, known as concentric tube robots (CTRs), show particular promise for minimally invasive surgery due to their miniature size (0.5-3mm) and inherent hollow channel for the passage of surgical tools. Despite recent advancements in CTRs, several challenges related to design, manufacturing, and teleoperation must still be addressed to achieve successful clinical translation. Due to variations in anatomy among patients and variations in task requirements among procedures, it is often necessary to customize the design of these robots on a patient- or population-specific basis. However, the complex kinematics and large design space make the design problem challenging. Here we propose a generalized computational framework that efficiently optimizes the CTR design and motion plan for safe navigation through a patient’s anatomy. The framework is the first fully gradient-based method for CTR design optimization and motion planning, enabling an efficient and scalable solution for simultaneously optimizing continuous variables, even across multiple anatomies. However, uncertainties in the manufacturing process can lead to challenges in the transition from simulated designs to physical robots. To minimize this gap, we propose an end-to-end design and manufacturing workflow on top of the previously developed optimization framework for CTRs that considers the often-overlooked impact of manufacturing uncertainty, focusing on two primary sources — tube curvature and diameter. This comprehensive approach incorporates a two-step design optimization and an uncertainty-based selection of manufacturing tolerances. By integrating these uncertainties into the design process, we can effectively bridge the gap between simulation and real-world performance, which we demonstrate through a case study on micro-laryngeal surgery. In addition to the design and fabrication of the patient-side robot, another critical aspect of minimally invasive surgical robotic systems is the surgeon-side input device for teleoperation. Delivering effective haptic feedback to the surgeon remains an open research question. Here we introduce a haptic continuum robot (HCR) for multi-modal cutaneous feedback, capable of conveying skin stretch, slip, normal indentation, and vibration to the fingertips. This device is the first to explore the use of both the tip and body of the continuum robot for haptic feedback, and it has the potential to convey task-critical information during teleoperation tasks. In summary, this dissertation aims to address the challenges in end-to-end concentric tube robot system design, enhancing both patient- and surgeon-side systems to ultimately improve clinical outcomes.