用自适应算法在线抑制机器人作业时的振动，提升稳定性与效率。
Online Learning for Vibration Suppression in Physical Robot Interaction using Power Tools
- 采用改进的阻尼BMFLC算法，实现振动的在线学习与前馈力控制。
- 在仿真中抑制率优于原方法，收敛更快且抗噪能力更强。
- 适合需要高精度力控的工业打磨、装配等物理交互场景。
振动抑制是协作机器人在建筑等复杂环境部署的关键能力。本文研究由电钻等外部设备引发的振动主动抑制问题，采用带限多傅里叶线性组合器（BMFLC）算法实现振动的在线学习，并通过前馈力控制进行抵消。提出一种阻尼型BMFLC方法，引入基于逻辑函数的自适应步长机制，显著提升收敛速度并增强抗噪性能。在包含时变多频振动的大量仿真与真实物理交互实验中验证该方法有效性。仿真结果表明，相比原始BMFLC及其基于递推最小二乘和卡尔曼滤波的扩展方法，本方法抑制率更高，且计算效率更优。真实打磨实验进一步证明其实际应用价值。补充视频见https://youtu.be/ms6m-6JyVAI。
原文摘要 · Abstract (English)
Vibration suppression is an important capability for collaborative robots deployed in challenging environments such as construction sites. We study the active suppression of vibration caused by external sources such as power tools. We adopt the band-limited multiple Fourier linear combiner (BMFLC) algorithm to learn the vibration online and counter it by feedforward force control. We propose the damped BMFLC method, extending BMFLC with a novel adaptive step-size approach that improves the convergence time and noise resistance. Our logistic function-based damping mechanism reduces the effect of noise and enables larger learning rates. We evaluate our method on extensive simulation experiments with realistic time-varying multi-frequency vibration and real-world physical interaction experiments. The simulation experiments show that our method improves the suppression rate in comparison to the original BMFLC and its recursive least squares and Kalman filter-based extensions. Furthermore, our method is far more efficient than the latter two. We further validate the effectiveness of our method in real-world polishing experiments. A supplementary video is available at https://youtu.be/ms6m-6JyVAI.
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