  Inaccurate tool localization is one of the main reasons for failures in
automating surgical tasks. Imprecise robot kinematics and noisy observations
caused by the poor visual acuity of an endoscopic camera make tool tracking
challenging. Previous works in surgical automation adopt environment-specific
setups or hard-coded strategies instead of explicitly considering motion and
observation uncertainty of tool tracking in their policies. In this work, we
present SURESTEP, an uncertainty-aware trajectory optimization framework for
robust surgical automation. We model the uncertainty of tool tracking with the
components motivated by the sources of noise in typical surgical scenes. Using
a Gaussian assumption to propagate our uncertainty models through a given tool
trajectory, SURESTEP provides a general framework that minimizes the upper
bound on the entropy of the final estimated tool distribution. We compare
SURESTEP with a baseline method on a real-world suture needle regrasping task
under challenging environmental conditions, such as poor lighting and a moving
endoscopic camera. The results over 60 regrasps on the da Vinci Research Kit
(dVRK) demonstrate that our optimized trajectories significantly outperform the
un-optimized baseline.
