Abstract
The existence of tactile afferents sensitive to slip-related mechanical transients in the human hand augments the robustness of grasping through secondary force modulation protocols. Despite this knowledge and the fact that tactile-based slip detection has been researched for decades, robust slip detection is still not an out-of-the-box capability for any commercially available tactile sensor. This research seeks to bridge this gap with a comprehensive study addressing several aspects of slip detection. Key developments include a systematic data collection process yielding millions of sensory data points, the generalized conversion of multivariate-to-univariate sensor output, an insightful spectral analysis of the univariate sensor outputs, and the application of Long Short-Term Memory (LSTM) neural networks on the univariate signals to produce robust slip detectors from three commercially available sensors capable of tactile sensing. The sensing elements underlying these sensors vary in quantity, spatial arrangement, and mechanics, leveraging principles in electro-mechanical resistance, optics, and hydro-acoustics. Critically, slip detection performance of the tactile technologies is quantified through a measurement methodology that unveils the effects of data window size, sampling rate, material type, slip speed, and sensor manufacturing variability. Results indicate that the investigated commercial tactile sensors are inherently capable of high-quality slip detection.
Abstract (translated)
对人手中滑动相关机械瞬变敏感的触觉传入的存在增强了通过辅助力调制方案抓住的鲁棒性。尽管有这方面的知识，并且基于触觉的滑动检测已经研究了数十年，但对于任何市场上可买到的触觉传感器来说，稳健滑动检测仍然不是一种开箱即用的功能。本研究旨在通过综合研究解决滑动检测的几个方面来弥补这一差距。关键的发展包括一个系统的数据收集过程，产生数百万个感官数据点，多变量到单变量传感器输出的广义转换，单变量传感器输出的深入的频谱分析以及长时间短期记忆（LSTM）神经的应用网络上的单变量信号，从三个市场上可买到的传感器产生鲁棒的滑动探测器能够触觉感应。这些传感器下面的传感元件在数量，空间布置和机械性能方面各不相同，它们充分利用了机电阻力，光学和水声技术的原理。重要的是，触觉技术的滑动检测性能通过测量方法来量化，该方法揭示了数据窗口大小，采样率，材料类型，滑动速度和传感器制造变化的影响。结果表明，所研究的商用触觉传感器本质上能够进行高质量的滑动检测。
URL
https://arxiv.org/abs/1806.10451
