We present a minimalist model for estimating point-to-point wireless connectivity in robotic networks where every node in the network is mobile, i.e., the space of the problem is truly ℝ 2  × ℝ 2 . This model is built on the idea that the geometry of an environment leads to a region-based decomposition where communication between pairs of regions can be approximated accurately by simple stochastic models. We develop an estimation framework that allows for these simple stochastic models to be predicted and updated based on sparse measurements as demonstrated experimentally.
['Jonathan Fink', 'Jeffrey N. Twigg', 'Paul L. Yu', 'Brian M. Sadler']
A parsimonious model for wireless connectivity in robotic networks