物理-MAC层集成:基于MLR-OLS和随机森林的移动超高频RFID机器人读取状态的跨层传感方法
Ruoyu Pan1, Bo Qin1, Jiaqi Liu1
1College of Communication and Information Engineering, Xi'an University of Posts and Telecommunications, Xi'an 710121, China.
Sensors (Basel, Switzerland)
|January 28, 2026
概括
这项研究引入了UHF RFID机器人的新交叉层传感方法,提高了库存效率并减少了自动仓库中错过的读数. 适应性策略通过准缺少的项目来提高商品阅读率.
科学领域:
- 机器人和自动化机器人与自动化
- 无线通信和传感传感器
- 供应链管理 供应链管理
背景情况:
- 仓库中的移动超高频RFID机器人通常遵循固定的路径,缺乏环境感知.
- 这导致了错过RFID标签读数和低效的库存管理等问题.
研究的目的:
- 为移动超高频RFID机器人开发跨层传感方法,以提高读取精度和效率.
- 为了使机器人能够感知货架布局和商品分配,以优化运营.
主要方法:
- 建议使用多线性回归-直角最小方形 (MLR-OLS) 进行货架定位的物理层位置传感模型.
- 开发了基于K-means的标签密度分类和基于随机森林的缺失标签估计算法,使用物理和MAC层特征.
- 引入了基于感知阅读状态的适应性阅读策略,以针对丢失的货物.
主要成果:
- 在中低机器人速度下实现了厘米级的货架定位精度.
- 在感知货物分布和估计丢失货物的数量方面表现出高可靠性.
- 适应性阅读策略显著改善了整体商品阅读率.
结论:
- 拟议的跨层传感方法提高了移动UHF RFID机器人系统的感知和效率.
- 这项工作为RFID机器人在自动仓储中的先进应用提供了基础.
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