Our research focuses on developing and testing shared control strategies for robotic-assisted surgery (RAS) to improve surgical precision, reduce collateral damage, and enhance patient outcomes. Recognizing the challenges of fully autonomous systems in complex surgical environments, we designed an interface that combines human expertise with robotic precision, allowing surgeons to supervise and intervene as needed. Our goal is to increase surgical efficiency while maintaining the highest level of safety.

The system integrates stereo imaging, RGB-D cameras, a haptic device, and a teleoperation controller to assist surgeons during path planning and execution. Surgeons interact with a 3D display of the surgical area, visualized through a Quest 3 headset, to select key points for path planning. The system generates an initial path, which is refined through algorithms for smoothness, shortest-path computation, and obstacle avoidance. A custom 3D-printed holder mounts the electrosurgery tool on the robot, and real-time feedback is provided through visual overlays and haptic cues. Foot pedals allow seamless switching between manual and autonomous modes, enabling surgeons to maintain control during critical moments.

The project’s testing was divided into two components: shared path planning and shared control. I led the tests for the shared path-planning system, which focused on measuring the system's ability to assist surgeons in defining precise and efficient paths. These tests evaluated assisted and unassisted path planning under various conditions, including combinations of visual and haptic feedback. Metrics such as point spacing consistency, task completion time, and perceived workload (using the Surg-TLX scale) were recorded. The results showed that the assisted path-planning system reduced task completion time, improved path accuracy, and lowered the perceived workload compared to unassisted methods. The inclusion of haptic feedback further enhanced control by helping the user identify and adjust paths around obstacles.

My partner conducted tests for the shared control system, which evaluated the integration of manual and autonomous modes for surgical task execution. These tests involved cutting two distinct tumor shapes under different control strategies: pure autonomous control, teleoperation only, semi-autonomous control without switching costs, and semi-autonomous control with switching costs. Metrics such as task completion time, teleoperation time percentage, incision accuracy on the tissue surface, depth accuracy, and perceived workload (using the Surg-TLX scale) were analyzed. The results demonstrated that semi-autonomous control strategies offered a balance between human decision-making and robotic precision, reducing task completion time and improving accuracy while minimizing the surgeon’s workload.

Through these complementary tests, we validated the effectiveness of shared path planning and shared control strategies in robotic-assisted surgery. By combining human expertise with robotic precision, our system enhances surgical efficiency and safety. Future work will involve refining the interface, optimizing control algorithms, and integrating the system into a surgical robotic testbed for further validation in realistic scenarios. This research underscores the importance of human-robot collaboration in advancing robotic-assisted surgery.

Published Papers: A Confidence-Based Shared Control Strategy for Robotic Electrosurgery - https://ieeexplore.ieee.org/document/10964360

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