ロボット手術における組織異常の能動探索のための軌道最適化センシング
Trajectory-Optimized Sensing for Active Search of Tissue Abnormalities in Robotic Surgery
ガウス過程と能動学習を用いて、ロボットが触診経路を最適化しながら腫瘍などの硬い組織を自動で位置推定・形状把握する手法を開発し、複数のロボットで有効性を示した。
著者: Hadi Salman, Elif Ayvali, Rangaprasad Arun Srivatsan, Yifei Ma, Nicolas Zevallos, Rashid Yasin, Long Wang, Nabil Siman, Howie Choset
分類: cs.RO
原文アブストラクト
In this work, we develop an approach for guiding robots to automatically localize and find the shapes of tumors and other stiff inclusions present in the anatomy. Our approach uses Gaussian processes to model the stiffness distribution and active learning to direct the palpation path of the robot. The palpation paths are chosen such that they maximize an acquisition function provided by an active learning algorithm. Our approach provides the flexibility to avoid obstacles in the robot's path, incorporate uncertainties in robot position and sensor measurements, include prior information about location of stiff inclusions while respecting the robot-kinematics. To the best of our knowledge this is the first work in literature that considers all the above conditions while localizing tumors. The proposed framework is evaluated via simulation and experimentation on three different robot platforms: 6-DoF industrial arm, da Vinci Research Kit (dVRK), and the Insertable Robotic Effector Platform (IREP). Results show that our approach can accurately estimate the locations and boundaries of the stiff inclusions while reducing exploration time.
