机器人关节内在摩擦和扭矩波动的识别,具有集成的扭矩传感器,适用于轮轴承的表征
Sri Harsha Turlapati1, Van Pho Nguyen1,2, Juhi Gurnani1
1School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798, Singapore.
Sensors (Basel, Switzerland)
|December 17, 2024
概括
本研究提出了一种使用辐射基函数来预测机器人的内在动态的新方法. 这提高了精细的机器人操纵任务测量外部力的准确性.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 控制系统 控制系统
- 机器学习 机器学习
背景情况:
- 机器人中集成的联合扭矩传感器可以测量相互作用力和机器人的内在动力学.
- 区分这些力量对于微妙的操纵任务至关重要,因为相互作用力很小.
研究的目的:
- 开发和验证一种用于识别和预测机器人的内在动态的方法.
- 提高使用集成传感器测量外部相互作用力的准确性.
主要方法:
- 用线性回归与非线性辐射基函数来建模内在动力学.
- 该模型在轮轴承转任务上进行了验证,以测量摩擦.
主要成果:
- 与没有内在动力学补偿的方法相比,拟的模型显著降低了测量轴承扭矩的误差.
- 在使用联合7扭矩传感器时,轴承扭矩测量的RMS误差为11%.
结论:
- 开发的方法有效地弥补了机器人的内在动态.
- 这使得机器人可以更准确地测量力和扭矩,从而提高精细的操纵能力.
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