外观
论文arXiv 2610.0804122 天前
基于干涉式 FBG 阵列传感与混合深度学习的 AGV 连续高精度定位方法
原文链接：A Continuous High Precision Localization Method for AGVs Based on Interferometric FBG Array Sensing and Hybrid Deep Learning（arXiv 2610.08041 · 发布日期：2026-10-06）
A Continuous High Precision Localization Method for AGVs Based on Interferometric FBG Array Sensing and Hybrid Deep Learning
针对AGV连续高精度定位，结合干涉式FBG阵列传感与混合深度学习，在复杂工业条件实验中验证有效性。
核心要点
- 面向电磁敏感工业环境，解决传统离散定位连续性不足和现有连续定位精度受限问题。
- 设计浅埋式干涉式FBG阵列传感轨道，稳定获取高质量信号并进行任务导向预处理。
- 构建滑动时间窗多通道联合表示，用混合深度学习利用通道空间相关性和时间连续性估计位置。
- 实验显示在复杂工业条件下平均绝对误差低于2厘米，表明方法在实际AGV定位场景中有效且稳健。
验证范围与局限：验证范围为复杂工业条件下的AGV定位实验，摘要未说明更多平台数量或任务类型。
英文摘要 (Abstract)
High precision continuous localization is a fundamental challenge for automated guided vehicles (AGVs) operating in electromagnetically sensitive industrial environments. To mitigate the continuity limitations of conventional discrete localization methods and the accuracy limitations of existing continuous localization methods, this paper proposes a continuous AGV localization method that integrates interferometric fiber Bragg grating (FBG) array sensing with a hybrid deep learning framework. We first design a shallowly buried interferometric FBG array sensing track to enable the stable acquisition of high-quality signals. The received sensing signals are subsequently parameterized and preprocessed in a task-oriented manner, in which a multichannel joint representation is constructed using a sliding time window. A hybrid deep learning framework is developed to estimate AGV position from multichannel FBG response signals by jointly exploiting inter-channel spatial correlation and temporal continuity in the measurement sequence. Experimental results show that the proposed method achieves a mean absolute error (MAE) of less than 2 cm under complex industrial conditions, proving its effectiveness and robustness in practical AGV localization scenarios.
作者：Laiming Wu, Xin Gui, Hui Cao, Yunchuan Zhang, Haiyang Wang, Jiaqi Wang, Zhengying Li
来源
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