4.6 Article

Neuromorphic approach to tactile edge orientation estimation using spatiotemporal similarity

期刊

NEUROCOMPUTING
卷 407, 期 -, 页码 246-258

出版社

ELSEVIER
DOI: 10.1016/j.neucom.2020.04.131

关键词

Tactile sensing; Neuromorphic; Biomimetic; Spatiotemporal; Edge orientation estimation; Mechanoreceptors

资金

  1. Singapore Institute for Neurotechnology (SINAPSE), NUS, Singapore
  2. Office of Naval Research Global

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Moving a finger around the boundary edge is one of the important strategies followed by humans for ascertaining the shape and size of an object by touch. Tactile response from thousands of mechanorecep-tors in the human hand offers a high spatiotemporal resolution to perceive the edge orientation quickly (< 50 ms) and accurately (acuity of < 3 degrees). Inspired by the computational efficiency of biological tactile system, we present a neuromorphic approach to artificial tactile sensing that mimics the spike-based spa-tiotemporal tactile response of Fast Adapting type I (FA-I) mechanoreceptors. We propose a novel, model -based spatiotemporal correlation matching method to estimate the orientation of the boundary edge while a piezoresistive tactile sensor array attached to robotic arm palpates over the object. Results high-light the ability of the proposed method to efficiently leverage spatial and temporal information, by obtaining very precise orientation estimates (+/- 1.67 degrees error for edges oriented from 10 degrees to 90 degrees, with a step of 5 degrees) in spite of a low-resolution sensor (169 mm(2), 4 x 4 resolution). A comparison with both spatial and spatiotemporal based classifiers indicates that the proposed method achieves 20% lower mean absolute error (MAE) than its closest counterparts, all of which required supervised training. Furthermore, we show that even with a ten-fold loss of spike time precision (1 ms-10 ms), MAE is maintained at 2 degrees. This work highlights that even with a modest sensor size and resolution, spatiotemporal similarity met-rics can be used to obtain very precise estimates of orientation. Such an approach has potential applica-tions towards improving the tactile sensing capability in robotic/prosthetic hands where knowledge of spatial edge orientation information is paramount for object manipulation, and sensor contact area is often sparse and small. (c) 2020 Elsevier B.V. All rights reserved.

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