Self-Mixing Laser Interferometry: In Search of an Ambient Noise-Resilient Alternative to Acoustic Sensing

2025-04-18

AI Summary

This work addresses the insufficient robustness of microvibration (acoustic) perception in robotic manipulation under environmental noise interference. We propose a non-contact tactile sensing method based on self-mixing interferometry (SMI). For the first time, we systematically validate SMI’s strong robustness against broadband noise and target-specific interference (e.g., motor noise) in the frequency domain, elucidate the underlying motor-noise coupling mechanism, and demonstrate the critical role of high sampling rates in suppressing aliasing and enhancing signal-to-noise ratio. Through time-frequency analysis, fingertip-integrated robot hardware design, and multi-scenario comparative experiments, we show that our approach significantly outperforms conventional acoustic sensing in strongly interfering environments—such as multi-robot coordination—while maintaining stable performance under broadband noise. An open-source hardware platform and a benchmark dataset are released to advance research and applications in noise-resilient tactile perception.

Technology Category

• Intelligent Robots: Manipulation
• Natural Language Processing: Safety and Robustness
• Machine Learning: Adversarial Learning & Robustness

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Self-mixing interferometry (SMI) has been lauded for its sensitivity in detecting microvibrations, while requiring no physical contact with its target. Microvibrations, i.e., sounds, have recently been used as a salient indicator of extrinsic contact in robotic manipulation. In previous work, we presented a robotic fingertip using SMI for extrinsic contact sensing as an ambient-noise-resilient alternative to acoustic sensing. Here, we extend the validation experiments to the frequency domain. We find that for broadband ambient noise, SMI still outperforms acoustic sensing, but the difference is less pronounced than in time-domain analyses. For targeted noise disturbances, analogous to multiple robots simultaneously collecting data for the same task, SMI is still the clear winner. Lastly, we show how motor noise affects SMI sensing more so than acoustic sensing, and that a higher SMI readout frequency is important for future work. Design and data files are available at https://github.com/RemkoPr/icra2025-SMI-tactile-sensing.

Problem

• Develops noise-resilient SMI for robotic contact sensing
• Compares SMI and acoustic sensing in frequency domain
• Analyzes motor noise impact on SMI performance

Innovation

• Self-mixing interferometry detects microvibrations contactlessly
• SMI outperforms acoustic sensing in broadband noise
• Higher SMI readout frequency reduces motor noise impact