事前モデル不要の連続体マニピュレータ衝突検出学習
Learning to Detect Collisions for Continuum Manipulators without a Prior Model
連続体マニピュレータの衝突検出を、形状センサ情報のみから機械学習で行う手法を提案し、未知の柔軟・硬質障害物環境で有効性を示した。
著者: Shahriar Sefati, Shahin Sefati, Iulian Iordachita, Russell H. Taylor, Mehran Armand
分類: cs.RO, cs.LG
原文アブストラクト
Due to their flexibility, dexterity, and compact size, Continuum Manipulators (CMs) can enhance minimally invasive interventions. In these procedures, the CM may be operated in proximity of sensitive organs; therefore, requiring accurate and appropriate feedback when colliding with their surroundings. Conventional CM collision detection algorithms rely on a combination of exact CM constrained kinematics model, geometrical assumptions such as constant curvature behavior, a priori knowledge of the environmental constraint geometry, and/or additional sensors to scan the environment or sense contacts. In this paper, we propose a data-driven machine learning approach using only the available sensory information, without requiring any prior geometrical assumptions, model of the CM or the surrounding environment. The proposed algorithm is implemented and evaluated on a non-constant curvature CM, equipped with Fiber Bragg Grating (FBG) optical sensors for shape sensing purposes. Results demonstrate successful detection of collisions in constrained environments with soft and hard obstacles with unknown stiffness and location.
