PREdicting MObility using STATistics (PreMoStat)

The proposed work addresses the problem of determining whether a given small unmanned ground vehicle (SUGV) can traverse a given terrain, when the state of both the SUGV and the terrain are not known exactly. In Phase I, we developed Monte Carlo-based methods to predict the mobility of the PackBot on steps, and we experimentally validated those methods with experiments with a real robot on real terrain with varying coefficient of friction and step height. In Phase II we will more fully develop the statistical prediction methodology, including extensions, optimization, and continued validation. In Phase II we will also develop a software application integrating the statistical mobility prediction and visualization methods.