Efficient Stochastic Mobility Prediction for Mobile Robotic Systems

The Army has devoted significant resources to understanding and predicting the mobility of military vehicles in natural terrain. Future Army operations (under FCS) will employ small (i.e. sub-500 kg) autonomous or semi-autonomous UGVs in both cross-country and urban environments. A fundamental requirement of these UGVs is to quickly and robustly predict their ability to successfully negotiate terrain regions and surmount obstacles. This mobility prediction capability is critical to successful deployment of UGVs that can operate effectively in challenging terrain with minimal or no human supervision. The purpose of this research program is to develop a robust, efficient method for UGV mobility prediction that exploits recent advances in statistical simulation to yield a fundamentally new approach to mobility prediction for small UGVs. By coupling rigorous statistical techniques with physics-based UGV and terrain models, the methods (once complete) will yield accurate predictions of mobility in general 3D terrain and not rely on idealized obstacle “primitives”. The result of the proposed Phase 2 research will be a proof-of-concept demonstration of the mobility prediction method operating on an Army-relevant UGV test bed in both simulated and real-world environments. The proposed work will be performed as a collaboration between Quantum Signal, LLC (QS) and the Massachusetts Institute of Technology’s (MIT) Robotics Mobility Group.