SURESTEP: 不確実性を考慮した軌道最適化によるロバストな手術自動化のための視覚的ツール追跡の強化
SURESTEP: An Uncertainty-Aware Trajectory Optimization Framework to Enhance Visual Tool Tracking for Robust Surgical Automation
手術自動化におけるツール追跡の不確実性をモデル化し、エントロピー上限を最小化する軌道最適化フレームワークを提案。da Vinci Research Kitでの縫合針再把持タスクで有効性を実証。
著者: Nikhil U. Shinde, Zih-Yun Chiu, Florian Richter, Jason Lim, Yuheng Zhi, Sylvia Herbert, Michael C. Yip
分類: cs.RO
原文アブストラクト
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.
