能動的システム同定のためのサンプリングベース軌道計画
Sampling-based Motion Planning for Active Multirotor System Identification
マルチロータ機が未知パラメータを素早く同定できるよう、EKFで信念ダイナミクスを近似しつつサンプリングベース計画で情報量最大の軌道を探索する手法を提案し、実機で収束時間と不確かさを4分の1に短縮した。
著者: Rik Bähnemann, Michael Burri, Enric Galceran, Roland Siegwart, Juan Nieto
分類: cs.RO
原文アブストラクト
This paper reports on an algorithm for planning trajectories that allow a multirotor micro aerial vehicle (MAV) to quickly identify a set of unknown parameters. In many problems like self calibration or model parameter identification some states are only observable under a specific motion. These motions are often hard to find, especially for inexperienced users. Therefore, we consider system model identification in an active setting, where the vehicle autonomously decides what actions to take in order to quickly identify the model. Our algorithm approximates the belief dynamics of the system around a candidate trajectory using an extended Kalman filter (EKF). It uses sampling-based motion planning to explore the space of possible beliefs and find a maximally informative trajectory within a user-defined budget. We validate our method in simulation and on a real system showing the feasibility and repeatability of the proposed approach. Our planner creates trajectories which reduce model parameter convergence time and uncertainty by a factor of four.
