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

DRIVE: Data-driven Robot Input Vector Exploration