期刊
NEUROCOMPUTING
卷 244, 期 -, 页码 102-111出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.neucom.2017.03.025
关键词
Soft neuromorphic approach; Spike train distance; Biomimetic fingertip; Tactile sensing; Surface roughness recognition
资金
- Joint Ph.D. Degree Programme NTU-TU Darmstadt
- Fraunhofer Singapore
- National Research Foundation (NRF)
- A*STAR AOP project [1223600005]
- A*STAR Industrial Robotics Programme [1225100007]
Surface roughness is an important object property and can significantly affect the friction characteristics, wear resistance, and fatigue life of components. Although some work has been done on demonstrating the capability of specific tactile sensors for surface roughness discrimination, the soft neuromorphic approach by taking inspirations from neuroscience for tactile surface roughness discrimination is exceptionally rare. This paper aims to fill this gap, and presents a soft neuromorphic method for tactile surface roughness discrimination with a biomimetic fingertip. The analog tactile signals generated from polyvinylidene difluoride (PVDF) films are fed as input to the Izhikevich neurons in order to obtain spike trains. Two distinct decoding schemes based on k-nearest neighbors (kNN) in both spike feature space and spike train space are used for surface roughness discrimination. We thoroughly examined the different spike train distance based kNN (STD-kNN) algorithms for decoding spike trains. Eight standard rough surfaces with roughness values (Ra) of 50 mu m, 25 mu m, 12.5 mu m, 6.3 mu m 3.2 mu m, 1.6 mu m, 0.8 mu m, and 0.4 mu m are explored. The highest classification accuracy of (77.6 +/- 13.7) % can be achieved with kNN (k= 11) classifier and the Victor-Purpura distance (q = 0.024 ms(-1)). (c) 2017 Elsevier B.V. All rights reserved.
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