  An accurate motion model is a fundamental component of most autonomous
navigation systems. While much work has been done on improving model
formulation, no standard protocol exists for gathering empirical data required
to train models. In this work, we address this issue by proposing Data-driven
Robot Input Vector Exploration (DRIVE), a protocol that enables characterizing
uncrewed ground vehicles (UGVs) input limits and gathering empirical model
training data. We also propose a novel learned slip approach outperforming
similar acceleration learning approaches. Our contributions are validated
through an extensive experimental evaluation, cumulating over 7 km and 1.8 h of
driving data over three distinct UGVs and four terrain types. We show that our
protocol offers increased predictive performance over common human-driven
data-gathering protocols. Furthermore, our protocol converges with 46 s of
training data, almost four times less than the shortest human dataset gathering
protocol. We show that the operational limit for our model is reached in
extreme slip conditions encountered on surfaced ice. DRIVE is an efficient way
of characterizing UGV motion in its operational conditions. Our code and
dataset are both available online at this link:
https://github.com/norlab-ulaval/DRIVE.
