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对传感器诊断的集中和联合学习技术的比较分析,应用于多机器人系统的合作本地化

Zaynab El Mawas¹, Cindy Cappelle¹, Mohamad Daher²

¹CRIStAL, Centre de Recherche en Informatique Signal et Automatique de Lille, University of Lille, CNRS, UMR 9189, F-59000 Lille, France.

Sensors (Basel, Switzerland)

|September 9, 2023

概括

本研究引入了多车系统的耐故障合作本地化方法,通过结合基于模型和数据驱动的故障检测. 它比较了集中式和联合式学习方法,以提高系统对传感器故障的可靠性.

科学领域:

机器人技术和自主系统
控制理论和工程控制理论和工程
人工智能和机器学习

背景情况:

多车系统需要强大的合作本地化,以确保安全运行.
车辆中的传感器故障可以显著降低本地化性能和系统可靠性.
现有的本地化方法可能无法充分解决各种传感器故障场景.

研究的目的:

为多车辆系统开发一个耐故障的合作本地化解决方案.
整合基于模型和数据驱动的技术,以进行有效的传感器故障诊断.
为了比较集中和联合学习的性能,用于在合作本地化中检测故障.

主要方法:

结合基于模型的诊断与数据驱动的差异化模型来识别故障.
使用带有注入传感器故障的标记数据集来训练诊断模型.
实施和比较集中式学习 (汇总故障指标) 和联合式学习 (本地模式培训).
在学习过程中使用逻辑回归.

主要成果:

拟议的合方法有效地识别和标记错误的传感器读数.
集中式和联合式学习策略都显示出故障检测能力.
在这种情况下,比较分析提供了对集中式和联合式学习之间的绩效权衡的见解.
系统的有效性在各种故障场景中得到验证.

结论:

基于模型和数据驱动的技术的整合为容错的合作本地化提供了一个有前途的解决方案.
联合学习为分布式多车辆系统中故障检测的集中学习提供了一个可行的,保护隐私的替代方案.
该研究强调了强大的故障检测机制的重要性,以确保合作自主系统的安全性和可靠性.

关键词:

合作的本地化合作.数据融合数据融合容错宽容容错宽容容错宽容联合学习的联合学习信息理论信息理论机器学习是机器学习.

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