一种通用工具交互力估计方法,用于机器人工具操纵
Diyun Wen1, Jiangtao Xiao1, Yu Xie1
1Pen-Tung Sah Institute of Micro-Nano Science and Technology, Xiamen University, Xiamen 361005, China.
Sensors (Basel, Switzerland)
|November 13, 2025
概括
本研究提出了一种通用方法,用于估计机器人工具端相互作用力,即使使用不同的工具和姿势. 它使用在线识别的动态模型和尖端神经网络来准确地反力.
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:
- 精确的六度自由度 (6-DoF) 交互力/扭矩在工具端对于机器人操纵至关重要.
- 通过机器人手腕传感器进行间接测量,在实时解工具惯力方面面临挑战,尤其是在不同的工具和抓取姿势的情况下.
研究的目的:
- 开发一种通用方法来估计适用于各种抓器和工具的工具端相互作用力.
- 为了解决不同工具,抓住姿势,传感器零漂移和手腕传感器爬行效应引起的不确定性.
主要方法:
- 基于牛顿-欧勒方法的在线识别工具动态模型是为集成的抓工具系统开发的.
- 零漂移传感器被纳入,并在粗力估计的动态模型中在线识别.
- 使用尖端神经网络 (SNN) 来补偿随时间变化的手腕传感器爬行效应.
主要成果:
- 在机器人手臂上的实验验证表明,对于估计的工具端相互作用力,根平均平方误差低于0.5N (力) 和0.03Nm (扭矩).
- 取得的精度与现场测量相当.
- 该方法已成功应用于带阻抗控制的机器人刀操纵,使精确和快速的相互作用力反成为可能.
结论:
- 提出的通用估计方法有效地处理各种工具和掌握姿势.
- 在线动力学模型和SNN的整合提供了准确而强大的工具终端力估计.
- 这种方法增强了机器人操纵能力,特别是在需要精确的力量反的合规操作中.
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