高海況下のUAV自律着陸に向けたLSTMによる船体甲板動揺の非線形予測
Harnessing LSTM for Nonlinear Ship Deck Motion Prediction in UAV Autonomous Landing amidst High Sea States
高海況でのUAV着艦のため、LSTMで船体の縦・横・上下動を予測し「静止期間」を推定する手法を提案。実データの代わりに複合正弦波モデルで検証し高精度を確認した。
著者: Feifan Yu, Wenyuan Cong, Xinmin Chen, Yue Lin, Jiqiang Wang
分類: cs.RO
原文アブストラクト
Autonomous landing of UAVs in high sea states requires the UAV to land exclusively during the ship deck's "rest period," coinciding with minimal movement. Given this scenario, determining the ship's "rest period" based on its movement patterns becomes a fundamental prerequisite for addressing this challenge. This study employs the Long Short-Term Memory (LSTM) neural network to predict the ship's motion across three dimensions: longi-tudinal, transverse, and vertical waves. In the absence of actual ship data under high sea states, this paper employs a composite sine wave model to simulate ship deck motion. Through this approach, a highly accurate model is established, exhibiting promising outcomes within various stochastic sine wave combination models.
