TY - GEN
T1 - Voronoi Features for Tactile Sensing
T2 - Direct Inference of Pressure, Shear, and Contact Locations
AU - Cramphorn, Luke
AU - Lloyd, John
AU - Lepora, Nathan
PY - 2018/9/13
Y1 - 2018/9/13
N2 - 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.
AB - 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.
UR - http://toc.proceedings.com/40564webtoc.pdf
U2 - 10.1109/ICRA.2018.8460644
DO - 10.1109/ICRA.2018.8460644
M3 - Conference Contribution (Conference Proceeding)
SN - 9781538630822
T3 - International Conference on Robotics and Automation (ICRA)
BT - 2018 IEEE International Conference on Robotics and Automation (ICRA 2018)
PB - Institute of Electrical and Electronics Engineers (IEEE)
ER -