We developed a novel whisker-follicle sensor that measures three mechanical signals at the whisker base. The first two signals are closely related to the two bending moments, and the third is an approximation to the axial force. Previous simulation studies have shown that these three signals are sufficient to determine the three-dimensional (3D) location at which the whisker makes contact with an object. Here we demonstrate hardware implementation of 3D contact point determination and then use continuous sweeps of the whisker to show proof-of principle 3D contour extraction. We begin by using simulations to confirm the uniqueness of the mapping between the mechanical signals at the whisker base and the 3D contact point location for the specific dimensions of the hardware whisker. Multi-output random forest regression is then used to predict the contact point locations of objects based on observed mechanical signals. When calibrated to the simulated data, signals from the hardware whisker can correctly predict contact point locations to within 1.5 cm about 74% of the time. However, if normalized output voltages from the hardware whiskers are used to train the algorithm (without calibrating to simulation), predictions improve to within 1.5 cm for about 96% of contact points and to within 0.6 cm for about 78% of contact points. This improvement suggests that as long as three appropriate predictor signals are chosen, calibrating to simulations may not be required. The sensor was next used to perform contour extraction on a cylinder and a cone. We show that basic contour extraction can be obtained with just two sweeps of the sensor. With further sweeps, it is expected that full 3D shape reconstruction could be achieved.
|Original language||English (US)|
|Title of host publication||Proceedings of Robotics|
|Subtitle of host publication||Science and Systems XIV|
|Editors||Hadas Kress-Gazit, Siddhartha Srinivasa, Tom Howard, Nikolay Atanasov|
|Number of pages||10|
|State||Published - 2018|