  Whole body tactile perception via tactile skins offers large benefits for
robots in unstructured environments. To fully realize this benefit, tactile
systems must support real-time data acquisition over a massive number of
tactile sensor elements. We present a novel approach for scalable tactile data
acquisition using compressed sensing. We first demonstrate that the tactile
data is amenable to compressed sensing techniques. We then develop a solution
for fast data sampling, compression, and reconstruction that is suited for
tactile system hardware and has potential for reducing the wiring complexity.
Finally, we evaluate the performance of our technique on simulated tactile
sensor networks. Our evaluations show that compressed sensing, with a
compression ratio of 3 to 1, can achieve higher signal acquisition accuracy
than full data acquisition of noisy sensor data.
