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Leveraging Tactile Sensors for Low Latency Embedded Smart Hands for Prosthetic and Robotic Applications by Wang, Xiaying; Geiger, Fabian; Niculescu, Vlad; Magno, Michele; Benini, Luca is a scholarly article available to read on EtoBox.

What is Leveraging Tactile Sensors for Low Latency Embedded Smart Hands for Prosthetic and Robotic Applications about?

Tactile sensing is a crucial perception mode for robots and human amputees in need of controlling a prosthetic device. Today robotic and prosthetic systems are still missing the important feature of accurate tactile sensing. This lack is mainly due to the fact that the existing tactile technologies have limited spatial and temporal resolution and are either expensive or not scalable. In this paper, we present the design and the implementation of a hardware-software embedded system called SmartHand. It is specifically designed to enable the acquisition and the real-time processing of high-resolution tactile information from a hand-shaped multi-sensor array for prosthetic and robotic applications. During data collection, our system can deliver a high throughput of 100 frames per second, which is 13.7x higher than previous related work. We collected a new tactile dataset while interacting with daily-life objects during five different sessions. We propose a compact yet accurate convolutional neural network that requires one order of magnitude less memory and 15.6x fewer computations compared to related work without degrading classification accuracy. The top-1 and top-3 cross-validation

Author

Wang, Xiaying; Geiger, Fabian; Niculescu, Vlad; Magno, Michele; Benini, Luca

Published

2022

Language

EN