未知物体に対するマーカーレスビジュアルサーボによるヒューマノイドロボットの把持
Markerless visual servoing on unknown objects for humanoid robot platforms
ステレオ視とベイズフィルタで未知物体の把持可能領域とエンドエフェクタ姿勢を推定し、画像ベースのビジュアルサーボで把持動作を実現した。
著者: Claudio Fantacci, Giulia Vezzani, Ugo Pattacini, Vadim Tikhanoff, Lorenzo Natale
分類: cs.RO, cs.SY, eess.SY, stat.CO
原文アブストラクト
To precisely reach for an object with a humanoid robot, it is of central importance to have good knowledge of both end-effector, object pose and shape. In this work we propose a framework for markerless visual servoing on unknown objects, which is divided in four main parts: I) a least-squares minimization problem is formulated to find the volume of the object graspable by the robot's hand using its stereo vision; II) a recursive Bayesian filtering technique, based on Sequential Monte Carlo (SMC) filtering, estimates the 6D pose (position and orientation) of the robot's end-effector without the use of markers; III) a nonlinear constrained optimization problem is formulated to compute the desired graspable pose about the object; IV) an image-based visual servo control commands the robot's end-effector toward the desired pose. We demonstrate effectiveness and robustness of our approach with extensive experiments on the iCub humanoid robot platform, achieving real-time computation, smooth trajectories and sub-pixel precisions.
