JIGGLE: 変形可能な手術環境における境界パラメータ推定のための能動的センシングフレームワーク
JIGGLE: An Active Sensing Framework for Boundary Parameters Estimation in Deformable Surgical Environments
軟体シミュレータと拡張カルマンフィルタを統合して組織の付着点を確率的に推定し、情報利得を最大化しつつ安全コストを最小化する能動制御で未知の境界パラメータを対話的にセンシングする枠組みを提案。
著者: Nikhil Uday Shinde, Xiao Liang, Fei Liu, Yutong Zhang, Florian Richter, Sylvia Herbert, Michael C. Yip
分類: cs.RO
原文アブストラクト
Surgical automation can improve the accessibility and consistency of life saving procedures. Most surgeries require separating layers of tissue to access the surgical site, and suturing to reattach incisions. These tasks involve deformable manipulation to safely identify and alter tissue attachment (boundary) topology. Due to poor visual acuity and frequent occlusions, surgeons tend to carefully manipulate the tissue in ways that enable inference of the tissue's attachment points without causing unsafe tearing. In a similar fashion, we propose JIGGLE, a framework for estimation and interactive sensing of unknown boundary parameters in deformable surgical environments. This framework has two key components: (1) a probabilistic estimation to identify the current attachment points, achieved by integrating a differentiable soft-body simulator with an extended Kalman filter (EKF), and (2) an optimization-based active control pipeline that generates actions to maximize information gain of the tissue attachments, while simultaneously minimizing safety costs. The robustness of our estimation approach is demonstrated through experiments with real animal tissue, where we infer sutured attachment points using stereo endoscope observations. We also demonstrate the capabilities of our method in handling complex topological changes such as cutting and suturing.
