An insect-inspired bionic sensor for tactile localization and material classification with state-dependent modulation

Patanè L, Hellbach S, Krause AF, Arena P, Dürr V (2012)
Frontiers in Neurorobotics 6: 1-18.

Zeitschriftenaufsatz | Veröffentlicht | Englisch
 
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Abstract / Bemerkung
Insects carry a pair of antennae on their head: multimodal sensory organs that serve a wide range of sensory-guided behaviors. During locomotion, antennae are involved in near-range orientation, for example in detecting, localizing, probing, and negotiating obstacles. Here we present a bionic, active tactile sensing system inspired by insect antennae. It comprises an actuated elastic rod equipped with a terminal acceleration sensor. The measurement principle is based on the analysis of damped harmonic oscillations registered upon contact with an object.The dominant frequency of the oscillation is extracted to determine the distance of the contact point along the probe and basal angular encoders allow tactile localization in a polar coordinate system. Finally, the damping behavior of the registered signalis exploited to determine the most likely material. The tactile sensor is tested in four approaches with increasing neural plausibility: first, we show that peak extraction from the Fourier spectrum is sufficient for tactile localization with position errors below 1%. Also,the damping property of the extracted frequency isused for material classification. Second, we show that the Fourier spectrum can be analysed by an Artificial Neural Network (ANN) which can be trained to decode contact distance and to classify contact materials.Thirdly, we show how efficiency can be improved by band-pass filtering the Fourier spectrum by application of non-negative matrix factorization. This reduces the input dimension by 95% while reducing classification performance by 8% only. Finally, we replace the FFT by an array of spiking neurons with gradually differing resonance properties, such that their spike rate is a function of the input frequency. We show that this network can be applied to detect tactile contact events of a wheeled robot, and how detrimental effects of robot velocity on antennal dynamics can be suppressed by state-dependent modulation of the input signals.
Stichworte
forward model; spiking network; material classification; insect antenna; tactile sense; tactile localization; bionic sensor
Erscheinungsjahr
2012
Zeitschriftentitel
Frontiers in Neurorobotics
Band
6
Seite(n)
1-18
ISSN
1662-5218
eISSN
1662-5218
Page URI
https://pub.uni-bielefeld.de/record/2519479

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Patanè L, Hellbach S, Krause AF, Arena P, Dürr V. An insect-inspired bionic sensor for tactile localization and material classification with state-dependent modulation. Frontiers in Neurorobotics. 2012;6:1-18.
Patanè, L., Hellbach, S., Krause, A. F., Arena, P., & Dürr, V. (2012). An insect-inspired bionic sensor for tactile localization and material classification with state-dependent modulation. Frontiers in Neurorobotics, 6, 1-18. doi:10.3389/fnbot.2012.00008
Patanè, L., Hellbach, S., Krause, A. F., Arena, P., and Dürr, V. (2012). An insect-inspired bionic sensor for tactile localization and material classification with state-dependent modulation. Frontiers in Neurorobotics 6, 1-18.
Patanè, L., et al., 2012. An insect-inspired bionic sensor for tactile localization and material classification with state-dependent modulation. Frontiers in Neurorobotics, 6, p 1-18.
L. Patanè, et al., “An insect-inspired bionic sensor for tactile localization and material classification with state-dependent modulation”, Frontiers in Neurorobotics, vol. 6, 2012, pp. 1-18.
Patanè, L., Hellbach, S., Krause, A.F., Arena, P., Dürr, V.: An insect-inspired bionic sensor for tactile localization and material classification with state-dependent modulation. Frontiers in Neurorobotics. 6, 1-18 (2012).
Patanè, Luca, Hellbach, Sven, Krause, André Frank, Arena, Paolo, and Dürr, Volker. “An insect-inspired bionic sensor for tactile localization and material classification with state-dependent modulation”. Frontiers in Neurorobotics 6 (2012): 1-18.

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