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Updated: Jul 12, 2025

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

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在智能工厂中为自动引导车辆采用基于机器学习的接入点选择策略.

Fumiko Ohori^1,2, Hirozumi Yamaguchi², Satoko Itaya¹

¹National Institute of Information and Communications Technology, Yokosuka 239-0847, Japan.

Sensors (Basel, Switzerland)

|October 28, 2023

概括

本研究引入了用于自动引导车辆 (AGV) 的新机器学习方法,以改进无线接入点 (AP) 切换. 该技术将通信持续时间提高1.34倍,减少制造环境中的停机时间.

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科学领域:

工业自动化工业自动化
无线通信系统无线通信系统
机器学习应用机器学习应用

背景情况:

自动引导车辆 (AGV) 在制造业中越来越多地被部署,以提高移动性和灵活性.
无线通信对于AGV运行至关重要,需要在多个接入点 (AP) 之间进行无过渡.
基于接收信号强度指标 (RSSI) 的现有AP选择方法在动态制造环境中由于信号不稳定性而不可靠.

研究的目的:

为在制造环境中运行的AGV开发先进的AP选择技术.
在AP切换期间最大限度地减少通信停机时间,以实现可靠的AGV监控和控制.
在不稳定的无线环境中克服传统的基于 RSSI 的方法的局限性.

主要方法:

利用AGV运动模式 (位置,轨迹,方向) 来预测最佳的AP连接.
利用机器学习从AP中学习特定位置,轨迹和方向的RSSI.
通过来自独特数据集的真实世界工厂数据验证方法.

主要成果:

与传统的基于信号强度的切换相比,提出的方法将每条路线的潜在通信持续时间延长1.34倍.
在通信稳定性方面,与标准Wi-Fi驱动程序相比显著改进.

关键词:

自动引导车辆自动引导车辆灵活的工厂灵活的工厂链接质量估计链接质量估计机器学习是机器学习.生产物流生产物流测量无线电频道的测量收到的信号强度指示器

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Last Updated: Jul 12, 2025

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减少了对无线传播路径进行广泛的手动调查的需要.

结论:

基于机器学习的新型AP选择技术有效地提高了制造业的AGV无线通信.
该方法为AP切换提供了比当前行业实践更强大,更有效的解决方案.
无线环境的自动评估和调整简化了AGV适应现有的AP基础设施.