通过最小误差透标准和自适应式LSTM网络对移动机器人进行稳健的轨迹预测
Da Xie1, Zengxun Li2, Chun Zhang3
1Xi'an Key Laboratory of Active Photoelectric Imaging Detection Technology, Xi'an Technological University, Xi'an 710021, China.
Entropy (Basel, Switzerland)
|February 27, 2026
概括
本研究介绍了MEE-LSTM,这是一种强大的机器人导航模型,使用最小误差 (MEE) 来处理噪音传感器数据. 它在现实世界条件下与冲动噪声显著优于标准模型.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 机器学习 机器学习
- 计算机视觉 计算机视觉
背景情况:
- 机器人导航依赖于准确的轨迹预测.
- 标准的深度学习模型经常使用平均平方误差 (MSE),这很容易受到现实世界的噪音的影响,比如传感器故障和屏蔽.
- 这种脆弱性限制了机器人在实际环境中的可靠性.
研究的目的:
- 开发一个强大的轨迹预测框架,适应非高斯冲动噪声.
- 在降低传感条件下提高机器人导航系统的可靠性.
主要方法:
- 拟议的MEE-LSTM,将长期短期内存网络与最小误差 (MEE) 标准集成在一起.
- 利用Renyi的二次最小化来对异常值进行内在梯度剪切.
- 引入基于Silverman的自适应化 (SAA) 来管理内核带宽,以实现稳定的信息理论学习.
主要成果:
- 在干净的数据集上,MEE-LSTM表现出具有竞争力的准确性,在噪音环境中表现出卓越的弹性.
- 在20%的冲动噪声下,MEE-LSTM实现了≈0.51m的平均位移误差 (ADE),而MSE基线显著降低 (ADE>2.1m).
- 与基于MSE的方法相比,这代表了75.7%的稳定性改善.
结论:
- 在具有挑战性的机器人感知场景中,MEE-LSTM提供了一种基于统计学的方法,用于可靠的轨迹预测.
- 拟议的方法显著提高了机器人导航安全性和对传感器噪声的稳定性.
- 这项工作为在不可预测的环境中更可靠的自主系统铺平了道路.
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