不確かな動力学を持つ劣駆動ロボットの計画と制御のためのオンライン学習
On-Line Learning for Planning and Control of Underactuated Robots with Uncertain Dynamics
モデル不確かさによる外乱をオンラインで学習し、最適化ベースの計画と部分フィードバック線形化制御を反復的に改善する手法を提案。Pendubotの振り上げ実験で有効性を示した。
著者: Giulio Turrisi, Marco Capotondi, Claudio Gaz, Valerio Modugno, Giuseppe Oriolo, Alessandro De Luca
分類: cs.RO
原文アブストラクト
We present an iterative approach for planning and controlling motions of underactuated robots with uncertain dynamics. At its core, there is a learning process which estimates the perturbations induced by the model uncertainty on the active and passive degrees of freedom. The generic iteration of the algorithm makes use of the learned data in both the planning phase, which is based on optimization, and the control phase, where partial feedback linearization of the active dofs is performed on the model updated on-line. The performance of the proposed approach is shown by comparative simulations and experiments on a Pendubot executing various types of swing-up maneuvers. Very few iterations are typically needed to generate dynamically feasible trajectories and the tracking control that guarantees their accurate execution, even in the presence of large model uncertainties.
