Voronoi Features for Tactile Sensing: Direct Inference of Pressure, Shear, and Contact Locations

Luke Cramphorn, John Lloyd, Nathan Lepora

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

11 Citations (Scopus)
255 Downloads (Pure)

Abstract

There are a wide range of features that tactile contact provides, each with different aspects of information that can be used for object grasping, manipulation, and perception. In this paper inference of some key tactile features, tip displacement, contact location, shear direction and magnitude, is demonstrated by introducing a novel method of transducing a third dimension to the sensor data via Voronoi tessellation. The inferred features are displayed throughout the work in a new visualisation mode derived from the Voronoi tessellation; these visualisations create easier interpretation of data from an optical tactile sensor that measures local shear from displacement of internal pins (the TacTip). The output values of tip displacement and shear magnitude are calibrated to appropriate mechanical units and validate the direction of shear inferred from the sensor. We show that these methods can infer the direction of shear to 2.3 degrees without the need for training a classifier or regressor. The approach demonstrated here will increase the versatility and generality of the sensors and thus allow sensor to be used in more unstructured and unknown environments, as well as improve the use of these tactile sensors in more complex systems such as robot hands.
Original languageEnglish
Title of host publication2018 IEEE International Conference on Robotics and Automation (ICRA 2018)
Subtitle of host publicationProceedings of a meeting held 21-25 May 2018, Brisbane, Australia.
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
ISBN (Electronic)9781538630815
ISBN (Print)9781538630822
DOIs
Publication statusPublished - 13 Sep 2018

Publication series

NameInternational Conference on Robotics and Automation (ICRA)
PublisherIEEE
ISSN (Print)1050-4729
ISSN (Electronic)2577-087X

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