RSS-based relative localization and tethering for moving robots in unknown environments

The LANdroids project requires robots to autonomously localize, track, and follow (a task also known as tethering) other robots or humans in an unknown environment with limited sensing abilities. In this paper, we present a localization and tethering approach that relies solely on wireless signal strength and robot odometry without requiring any known reference points in the domain. We introduce a data-driven, probabilistic model that maps received signal strength (RSS) values to real-world distance distributions and embed this model in a grid-based localization algorithm that successfully performs the LANdroids tethering task. We furthermore show, that it is possible to improve localization through the addition of a compass sensor and inter-robot information sharing.