Shape recognition through tactile contour tracing a simulation study

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Abstract

We present Contour-net, a bio-inspired model for tactile contour- tracing driven by an Hopf oscillator. By controlling the rhythmic movements of a simulated insect-like feeler, the model executes both wide searching and local sampling movements. Contour-tracing is achieved by means of contact-induced phase-forwarding of the oscillator. To classify the shape of an object, collected contact events can be directly fed into machine learning algorithms with minimal pre-processing (scaling). Three types of classifiers were evaluated, the best one being a Support Vector Machine. The likelihood of correct classification steadily increases with the number of collected contacts, enabling an incremental classification during sampling. Given a sufficiently large training data set, tactile shape recognition can be achieved in a position-, orientation- and sizeinvariant manner. The suitability for robotic applications is discussed.

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Krause, A. F., Harischandra, N., & DÜrr, V. (2015). Shape recognition through tactile contour tracing a simulation study. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9420, pp. 54–77). Springer Verlag. https://doi.org/10.1007/978-3-319-27543-7_3

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