Two-phase online calibration for infrared-based inter-robot positioning modules

Abstract

Multi-robot systems can solve complex tasks that require the coordination of the team-member positions with respect to each other. While the development of ad-hoc relative positioning platforms embedding cheap off-the-shelf components is a practical choice, it leads not only to differences between the platforms themselves, but also to a high sensitivity to external factors. In this paper, we present a novel lightweight online calibration method composed of two phases, capable of running on miniature robots with limited computational capabilities. Furthermore, by exploiting a Gaussian process regression in its second phase, the proposed calibration approach is able to capture deviations from an assumed underlying physical model. We compare the performance of our approach with the theoretical Cramér-Rao lower bound and test its efficiency on real robots equipped with range and bearing modules.