  Autonomous robotic surgery has seen significant progression over the last
decade with the aims of reducing surgeon fatigue, improving procedural
consistency, and perhaps one day take over surgery itself. However, automation
has not been applied to the critical surgical task of controlling tissue and
blood vessel bleeding--known as hemostasis. The task of hemostasis covers a
spectrum of bleeding sources and a range of blood velocity, trajectory, and
volume. In an extreme case, an un-controlled blood vessel fills the surgical
field with flowing blood. In this work, we present the first, automated
solution for hemostasis through development of a novel probabilistic blood flow
detection algorithm and a trajectory generation technique that guides
autonomous suction tools towards pooling blood. The blood flow detection
algorithm is tested in both simulated scenes and in a real-life trauma scenario
involving a hemorrhage that occurred during thyroidectomy. The complete
solution is tested in a physical lab setting with the da Vinci Research Kit
(dVRK) and a simulated surgical cavity for blood to flow through. The results
show that our automated solution has accurate detection, a fast reaction time,
and effective removal of the flowing blood. Therefore, the proposed methods are
powerful tools to clearing the surgical field which can be followed by either a
surgeon or future robotic automation developments to close the vessel rupture.
