12 research outputs found
Voronoi Features for Tactile Sensing: Direct Inference of Pressure, Shear, and Contact Locations
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 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.Comment: Presented at ICRA 201
From pixels to percepts: Highly robust edge perception and contour following using deep learning and an optical biomimetic tactile sensor
Deep learning has the potential to have the impact on robot touch that it has
had on robot vision. Optical tactile sensors act as a bridge between the
subjects by allowing techniques from vision to be applied to touch. In this
paper, we apply deep learning to an optical biomimetic tactile sensor, the
TacTip, which images an array of papillae (pins) inside its sensing surface
analogous to structures within human skin. Our main result is that the
application of a deep CNN can give reliable edge perception and thus a robust
policy for planning contact points to move around object contours. Robustness
is demonstrated over several irregular and compliant objects with both tapping
and continuous sliding, using a model trained only by tapping onto a disk.
These results relied on using techniques to encourage generalization to tasks
beyond which the model was trained. We expect this is a generic problem in
practical applications of tactile sensing that deep learning will solve. A
video demonstrating the approach can be found at
https://www.youtube.com/watch?v=QHrGsG9AHtsComment: Accepted in RAL and ICRA 2019. N. Lepora and J. Lloyd contributed
equally to this wor
Tactile Sensors for Friction Estimation and Incipient Slip Detection - Toward Dexterous Robotic Manipulation:A Review
Humans can handle and manipulate objects with ease; however, human dexterity has yet to be matched by artificial systems. Receptors in our fingers and hands provide essential tactile information to the motor control system during dexterous manipulation such that the grip force is scaled to the tangential forces according to the coefficient of friction. Likewise, tactile sensing will become essential for robotic and prosthetic gripping performance as applications move toward unstructured environments. However, most existing research ignores the need to sense the frictional properties of the sensor-object interface, which (along with contact forces and torques) is essential for finding the minimum grip force required to securely grasp an object. Here, we review this problem by surveying the field of tactile sensing from the perspective that sensors should: 1) detect gross slip (to adjust the grip force); 2) detect incipient slip (dependent on the frictional properties of the sensor-object interface and the geometries and mechanics of the sensor and the object) as an indication of grip security; or 3) measure friction on contact with an object and/or following a gross or incipient slip event while manipulating an object. Recommendations are made to help focus future sensor design efforts toward a generalizable and practical solution to sense, and hence control grip security. Specifically, we propose that the sensor mechanics should encourage incipient slip, by allowing parts of the sensor to slip while other parts remain stuck, and that instrumentation should measure displacement and deformation to complement conventional force, pressure, and vibration tactile sensing
Addition of a biomimetic fingerprint on an artificial fingertip enhances tactile spatial acuity
EPSRC grant on Tactile Superresolution Sensing (EP/M02993X/1) The following data was obtained from 3 variants of the TacTip (with fingerprint, with fingerprint and cores, without fingerprint), an optical tactile sensor integrated on a 6-dof robotic arm The sensor performed localization on 9 stimuli with varying spatial frequency over a 30 mm range. The data was used to demonstrate the effect of fingerprints on tactile spatial perception