一个多方法框架,用于在传感器校准和不确定性量化中建立角加速参考值
Maximilian Gießler1,2, Bernd Waltersberger3, Thomas Götz4
1Department of Mechanical and Process Engineering, Offenburg University of Applied Sciences, Offenburg, Germany. maximilian.giessler@hs-offenburg.de.
Communications engineering
|April 8, 2025
概括
这项研究引入了测量机器人运动干扰的新框架. 它验证了惯性测量集群 (IMC) 作为校准动力传感器的可靠标准,提高了机器人的精度.
科学领域:
- 机器人和控制系统 机器人和控制系统
- 传感器技术和测量科学 传感器技术和测量科学
背景情况:
- 工业,医疗保健和家庭中的机器人面临着意外干扰的挑战.
- 实时反依赖于噪音传感器数据,反向动态计算放大噪音.
- 对于直接传感方法的量化测量不确定性仍然没有得到充分解决.
研究的目的:
- 提出一个多方法框架,以建立一个角加速参考.
- 为了证明这个参考作为校准动力传感器的标准的实用性.
- 为量化新型直接测量传感器,惯性测量集群 (IMC) 的不确定性.
主要方法:
- 开发一个多方法框架的角度加速参考.
- 使用蒙特卡洛模拟来量化惯性测量集群 (IMC) 的不确定性.
- 使用最小平方优化进行比较分析与其他测量方法相比.
主要成果:
- IMC 显示低标准偏差 (平均值) 0.3rad/s2,95%CI:[0.28,0.31]rad/s2) 在角加速时达到21rad/s2.
- 在IMC实现了可靠的数据表记录的角度加速度测量.
- 与其他方法相比,IMC显示角度加速,速度和角度的偏差较小.
结论:
- 拟议的框架为动力传感器校准提供了可靠的参考标准.
- 惯性测量集群 (IMC) 提供卓越的精度和运动传感的偏差降低.
- 这项工作解决了在直接机器人运动测量中对不确定性定量化的关键需求.
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