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arXiv 2312.04572cs.RO
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利用 LSTM 实现高海况下无人机自主着舰中的非线性舰船甲板运动预测

Harnessing LSTM for Nonlinear Ship Deck Motion Prediction in UAV Autonomous Landing amidst High Sea States

Zhejiang Provincial Engineering Research Centre for Special Aircrafts（浙江省特种飞行器工程研究中心）；Ningbo Institute of Materials Technology & Engineering, CAS（中国科学院宁波材料技术与工程研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Faculty of Electrical Engineering and Computer Science, Ningbo University（宁波大学电气工程与计算机科学学院）

Feifan Yu, Wenyuan Cong, Xinmin Chen, Yue Lin, Jiqiang Wang

备注：
11 pages, 7 figures, accept by ICANDVC2023
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AI总结：

针对高海况下无人机自主着舰需识别甲板“休整期”的问题，本文采用 LSTM 基于复合正弦波模拟数据预测三维舰船甲板运动，建立了高精度预测模型。

摘要：

在高海况下实现无人机自主着舰，要求无人机只能在舰船甲板运动最小的“休整期”着舰。在这种情况下，根据舰船运动模式确定其“休整期”成为解决该挑战的基本前提。本研究采用长短期记忆（LSTM）神经网络，从纵向浪、横向浪和垂向浪三个维度预测舰船运动。在缺乏高海况下真实舰船数据的情况下，本文采用复合正弦波模型模拟舰船甲板运动。通过该方法，建立了一个高精度模型，并在多种随机正弦波组合模型中展现出良好效果。

英文摘要：

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.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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