自律飛行体のためのリアルタイム異常検知手法
Automatic Real-time Anomaly Detection for Autonomous Aerial Vehicles
再帰的最小二乗法を用いて飛行体の入出力関係をオンラインでモデル化し、機体モデルに依存せず異常をリアルタイム検知する手法を提案。22回の飛行試験で精度86.36%を達成し、アクチュエータ故障データセットも公開した。
著者: Azarakhsh Keipour, Mohammadreza Mousaei, Sebastian Scherer
分類: eess.SY, cs.RO, cs.SY
原文アブストラクト
The recent increase in the use of aerial vehicles raises concerns about the safety and reliability of autonomous operations. There is a growing need for methods to monitor the status of these aircraft and report any faults and anomalies to the safety pilot or to the autopilot to deal with the emergency situation. In this paper, we present a real-time approach using the Recursive Least Squares method to detect anomalies in the behavior of an aircraft. The method models the relationship between correlated input-output pairs online and uses the model to detect anomalies. The result is an easy-to-deploy anomaly detection method that does not assume a specific aircraft model and can detect many types of faults and anomalies in a wide range of autonomous aircraft. The experiments on this method show a precision of 88.23%, recall of 88.23%, and 86.36% accuracy for over 22 flight tests. The other contribution is providing a new fault detection open dataset for autonomous aircraft, which contains complete data and the ground truth for 22 fixed-wing flights with eight different types of mid-flight actuator failures to help future fault detection research for aircraft. The source code and the dataset can be accessed from https://theairlab.org/fault-detection/.
