マルチロータ機におけるプロペラ損傷の検出・分類・推定
Propeller damage detection, classification and estimation in multirotor vehicles
マルチロータUAVのプロペラ損傷を、慣性計測と制御入力のみから検出・種類分類・深刻度推定するデータ駆動型フレームワークを実機飛行データで構築した。
著者: Claudio Pose, Juan Giribet, Gabriel Torre
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
This manuscript details an architecture and training methodology for a data-driven framework aimed at detecting, identifying, and quantifying damage in the propeller blades of multirotor Unmanned Aerial Vehicles. By substituting one propeller with a damaged counterpart-encompassing three distinct damage types of varying severity-real flight data was collected. This data was then used to train a composite model, comprising both classifiers and neural networks, capable of accurately identifying the type of failure, estimating damage severity, and pinpointing the affected rotor. The data employed for this analysis was exclusively sourced from inertial measurements and control command inputs, ensuring adaptability across diverse multirotor vehicle platforms.