RATTLE：軌道上検証を伴うロバストなオンラインパラメトリックモデル改善のための動作計画アルゴリズム
The RATTLE Motion Planning Algorithm for Robust Online Parametric Model Improvement with On-Orbit Validation
パラメトリック不確かさをオンラインで学習しつつロバスト性を保証する動作計画アルゴリズムRATTLEを拡張し、国際宇宙ステーションのAstrobeeロボットによる微小重力実験で有効性を示した論文。
著者: Keenan Albee, Monica Ekal, Brian Coltin, Rodrigo Ventura, Richard Linares, David W. Miller
分類: cs.RO
原文アブストラクト
Certain forms of uncertainty that robotic systems encounter can be explicitly learned within the context of a known model, like parametric model uncertainties such as mass and moments of inertia. Quantifying such parametric uncertainty is important for more accurate prediction of the system behavior, leading to safe and precise task execution. In tandem, providing a form of robustness guarantee against prevailing uncertainty levels like environmental disturbances and current model knowledge is also desirable. To that end, the authors' previously proposed RATTLE algorithm, a framework for online information-aware motion planning, is outlined and extended to enhance its applicability to real robotic systems. RATTLE provides a clear tradeoff between information-seeking motion and traditional goal-achieving motion and features online-updateable models. Additionally, online-updateable low level control robustness guarantees and a new method for automatic adjustment of information content down to a specified estimation precision is proposed. Results of extensive experimentation in microgravity using the Astrobee robots aboard the International Space Station and practical implementation details are presented, demonstrating RATTLE's capabilities for real-time, robust, online-updateable, and model information-seeking motion planning capabilities under parametric uncertainty.
