UC San Diego

Surgical robots are becoming increasingly common in operating rooms, which provides the opportunity to deploy automation algorithms for surgery. Surgical task automation aims to improve patient throughput, reduce quality-of-care variance among surgeries, and potentially deliver complete automated surgery in the future. While progress in developing autonomous surgical tasks has leaped forward, reactive maneuvers to traumatic events, such as hemostasis, represent a critical area that has attracted little attention. Hemostasis describes a state of the surgical field that is achieved when there is no site of active bleeding and the tissues are unobstructed by blood. Unlike previously automated tasks that occur in a more predictable cadence within a procedure, bleeding can be unpredictable, which necessitates hemostatic maneuvers at any time during surgery.

In this dissertation, all the necessary perception, motion planning, and control strategies are presented to autonomously control a robotic suction tool to clear the surgical field from blood. First, a surgical tool tracking technique is proposed that localizes the robotic agent, which will clear the surgical field, in the endoscopic camera frame. The surgical tool tracking is combined with a deformable tissue tracker to completely track a surgical scene before a vessel rupture occurs. The combination of the two trackers is coined SuPer, the Surgical Perception framework. Next, the blood from a vessel rupture scenario is perceived by detecting and reconstructing the flowing blood from the endoscopic camera data. Finally, a controller and a motion planner for the robotic suction tool to clear the surgical field of blood are presented.