Mobility prediction for unmanned ground vehicles in uncertain environments

The ability of autonomous unmanned ground vehicles (UGVs) to rapidly and effectively predict terrain negotiability is a critical requirement for their use on challenging terrain. Most methods for assessing traversability, however, assume precise knowledge of vehicle and terrain properties. In practical applications, uncertainties are associated with the estimation of the vehicle/terrain parameters, and these uncertainties must be considered while determining vehicular mobility. Here a computationally inexpensive method for efficient mobility prediction based on the stochastic response surface (SRSM) approach is presented that considers imprecise knowledge of terrain and vehicle parameters while analyzing various metrics associated with UGV mobility. A conventional Monte Carlo method and the proposed response surface methodology have been applied to two simulated cases of mobility analysis, and it has been shown that the SRSM method is an efficient tool as compared to conventional Monte Carlo methods for the analysis of vehicular mobility in uncertain environments.