A Sensorized Multicurved Robot Finger With Data-Driven Touch Sensing via Overlapping Light Signals
A Sensorized Multicurved Robot Finger With Data-Driven Touch Sensing via Overlapping Light Signals
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DOI：
10.1109/tmech.2020.2975578
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发表时间：
2020-10-01
影响因子：
6.4
通讯作者：
Ciocarlie, Matei
中科院分区：
文献类型：
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作者：
Piacenza, Pedro;Behrman, Keith;Ciocarlie, Matei
尽管在触摸和力转换方面取得了重大进展，但触觉传感在机器人操作中仍然远远没有普及。用于构建触摸传感器的现有方法已被证明难以集成到机器人手指中，这是由于存在多种挑战，包括难以覆盖多曲面、高线数或包装限制，从而阻止它们在灵巧的手中使用。在这篇文章中，我们提出了一个多曲面机器人手指精确的触摸定位和正常的力量检测复杂的，3-D表面。我们的方法的关键是重叠信号的新颖使用，从光发射器和接收器嵌入在一个透明的波导层，覆盖手指的功能区域。通过测量每个发射器和接收器之间的光传输，我们表明，我们可以获得一个非常丰富的信号集，响应于手指的变形，由于触摸的变化。然后，我们证明了纯数据驱动的深度学习方法能够从这些数据中提取有用的信息，例如接触位置和施加的法向力，而不需要分析模型。最终的结果是一个完全集成的传感器化机器人手指，具有低线数和使用易于访问的制造方法，旨在轻松集成到灵巧的机械手。
Despite significant advances in touch and force transduction, tactile sensing is still far from ubiquitous in robotic manipulation. Existing methods for building touch sensors have proven difficult to integrate into robot fingers due to multiple challenges, including difficulty in covering multicurved surfaces, high wire count, or packaging constrains preventing their use in dexterous hands. In this article, we present a multicurved robotic finger with accurate touch localization and normal force detection over complex, 3-D surfaces. The key to our approach is the novel use of overlapping signals from light emitters and receivers embedded in a transparent waveguide layer that covers the functional areas of the finger. By measuring light transport between every emitter and receiver, we show that we can obtain a very rich signal set that changes in response to deformation of the finger due to touch. We then show that purely data-driven deep learning methods are able to extract useful information from such data, such as contact location and applied normal force, without the need for analytical models. The final result is a fully integrated, sensorized robot finger, with a low wire count and using easily accessible manufacturing methods, designed for easy integration into dexterous manipulators.