基于IMU的定向估计算法的时间和频域分析与机器人手臂定向作为参考
Ruslan Sultan1, Steffen Greiser1
1Institute of Management and Technology, Hochschule Osnabrück, Kaiserstr. 10c, 49809 Lingen, Germany.
Sensors (Basel, Switzerland)
|August 28, 2025
概括
麦格威克 (MF) 和补充性 (CF) 过器在时间和频率领域提供基于IMU的导向估计的卓越性能. 间接卡尔曼 (IKF) 过器表现最差,但时间延迟最低.
科学领域:
- 机器人技术
- 传感器融合
- 控制系统
背景情况:
- 惯性测量单位 (IMU) 对于机器人的方向估计至关重要.
- 使用各种算法,如间接卡尔曼 (IKF),马奇威克 (MF) 和补充性 (CF) 过器.
- 不同领域的性能评估对于算法选择至关重要.
研究的目的:
- 分析和比较基于IMU的导向估计算法的性能.
- 使用机器人手臂在时间和频率领域评估算法.
- 调整过器参数以获得最佳性能.
主要方法:
- 在6关节的UR5e机器人手臂上使用了6-DoF IMU,机器人定位作为地面真相.
- 用于测试的姿势序列和通用二元噪声 (GBN) 信号.
- 计算的根平均平方误差 (RMSE),最大绝对误差 (MaxAE),复合频率响应,一致性和平均等效时间延迟 (AETD).
主要成果:
- 麦格维克 (MF) 和补充性 (CF) 过器在时间领域表现最好.
- 在频率领域,MF和CF的表现也相对较好.
- 间接卡尔曼 (IKF) 波器始终表现最差,但实现了最低的AETD.
结论:
- 建议使用MF和CF进行基于IMU的可靠导向估计.
- 当最小时间延迟是主要问题时,IKF可能是合适的.
- 算法选择取决于特定的应用程序对准确性和动态性的要求.
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