風を感じる：マルチロータ機における気流・抗力・接触の同時推定
Touch the Wind: Simultaneous Airflow, Drag and Interaction Sensing on a Multirotor
生物模倣の気流センサとUKFを組み合わせ、ドローンの風・抗力・接触力を同時推定する手法を提案し、モデルベースと深層学習ベースを比較検証した。
著者: Andrea Tagliabue, Aleix Paris, Suhan Kim, Regan Kubicek, Sarah Bergbreiter, Jonathan P. How
分類: cs.RO, cs.LG, cs.SY, eess.SY
原文アブストラクト
Disturbance estimation for Micro Aerial Vehicles (MAVs) is crucial for robustness and safety. In this paper, we use novel, bio-inspired airflow sensors to measure the airflow acting on a MAV, and we fuse this information in an Unscented Kalman Filter (UKF) to simultaneously estimate the three-dimensional wind vector, the drag force, and other interaction forces (e.g. due to collisions, interaction with a human) acting on the robot. To this end, we present and compare a fully model-based and a deep learning-based strategy. The model-based approach considers the MAV and airflow sensor dynamics and its interaction with the wind, while the deep learning-based strategy uses a Long Short-Term Memory (LSTM) neural network to obtain an estimate of the relative airflow, which is then fused in the proposed filter. We validate our methods in hardware experiments, showing that we can accurately estimate relative airflow of up to 4 m/s, and we can differentiate drag and interaction force.
