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IPT-DCD:使用深度学习方法在动态通信延迟下进行远程操作的互插预测器.
Hwanhee Kang1, Eugene Kim2, Myeonghwan Hwang2
1Robot Engineering, Korea National University of Science and Technology, 217 Gajeong-ro, Yuseong-gu, Daejeon 34113, Republic of Korea.
Sensors (Basel, Switzerland)
|July 12, 2025
概括
这项研究引入了在动态通信延迟 (IPT-DCD) 下进行远程操作的插值预测器,以增强控制稳定性. IPT-DCD有效地重建和预测命令,提高不稳定的远程操作环境中的稳定性.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 控制系统 控制系统
- 通信工程 通信工程
背景情况:
- 远程操作系统面临的挑战是控制稳定性和安全性.
- 动态通信延迟显著降低了系统性能.
研究的目的:
- 为远程操作系统提出一个新的预测器,IPT-DCD.
- 解决和减轻动态通信延迟的影响.
主要方法:
- 在动态通信延迟下 (IPT-DCD) 开发了用于远程操作的插值预测器.
- 使用编码器-解码器LSTM架构进行命令预测.
- 应用于信号预处理的反向转移和插值 (BSI).
主要成果:
- IPT-DCD通过插值重建异步接收的控制命令.
- 该模型使用许多到许多时间序列结构生成实时方向盘命令输出.
- 与基线模型相比,IPT-DCD对较大的通信延迟异常值表现出优越的稳定性.
结论:
- IPT-DCD有效地提高了远程操作中的控制稳定性和安全性.
- 拟议的方法在动态和不稳定的通信环境中非常有效.
- 对于现实世界的远程操作应用,IPT-DCD提供了显著的改进.

