Event-based signaling for large-scale artificial robotic skin - realization and performance evaluation

Abstract

In this paper we describe how we realized event-based signaling for large scale artificial robotic skin. We developed a new algorithm for the event generation on multi-modal skin cells. The skin cells have two modes, the conventional data sampling mode and the event mode. A comprehensive performance evaluation and comparison of these two modes is presented. We perform different experiments on our robot TOMM which has two UR5 robot arms, each covered with 260 multi-modal skin cells. Each skin cell samples 9 signals of 4 different modalities. Finally we derive models for extrapolating CPU usage and network traffic for larger numbers of skin cells and higher sample rates. The results show that the event-based system has superior performance and its performance edge increases with larger numbers of skin cells and higher sample rates. Experimental validation on our real robot system shows that in reactive control the event-based system reduces in comparison to the conventional system the packet rate by 48.2% and the CPU usage by 17.79%. We extrapolate the worst case for 5000 cells and show that the event-based system can at least reduce the packet rate by 21.2% and the CPU usage by 17.46%.