基于干涉型光纤布拉格光栅阵列传感与混合深度学习的AGV连续高精度定位方法
A Continuous High Precision Localization Method for AGVs Based on Interferometric FBG Array Sensing and Hybrid Deep Learning
浏览论文内容
中文总结 AI 辅助
本文提出一种结合干涉型FBG阵列传感与混合深度学习的AGV连续定位方法，在复杂工业条件下实现小于2厘米的平均绝对误差，解决了传统离散定位的连续性及现有连续定位的精度问题。
中文摘要 AI 辅助
在电磁敏感工业环境中运行自动导引车（AGV）时，高精度连续定位是一项基本挑战。为缓解传统离散定位方法的连续性局限以及现有连续定位方法的精度局限，本文提出了一种集成干涉型光纤布拉格光栅（FBG）阵列传感与混合深度学习框架的连续AGV定位方法。我们首先设计了一种浅埋式干涉型FBG阵列传感轨道，以实现高质量信号的稳定采集。随后，接收到的传感信号以任务导向的方式进行参数化和预处理，其中利用滑动时间窗构建多通道联合表示。开发了一种混合深度学习框架，通过联合利用测量序列中的通道间空间相关性和时间连续性，从多通道FBG响应信号估计AGV位置。实验结果表明，所提方法在复杂工业条件下实现了小于2厘米的平均绝对误差（MAE），证明了其在实际AGV定位场景中的有效性和鲁棒性。
英文摘要
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.
发表机构
- Hubei Longzhong Laboratory（湖北隆中实验室）
- Wuhan University of Technology（武汉理工大学）
- National Engineering Research Center of Fiber Optic Sensing Technology and Networks（光纤传感技术网络国家工程研究中心）
- Jiangsu Zhiren Jinghang New Materials Research Institute Company Ltd.（江苏智人京航新材料研究院有限公司）
- State Key Laboratory of Advanced Technology for Materials Synthesis and Processing（材料合成与加工先进技术国家重点实验室）
机构由 AI 辅助整理，请以论文原文为准。
