Quantum Signal, LLC · Signal Processing Solutions

Those involved in unmanned ground vehicles have substantial interest in understanding and predicting the mobility of military vehicles in natural terrain. For example, future US Army operations (under FCS) will employ small (i.e. sub-500 kg) autonomous or semi-autonomous UGVs in both cross-country and urban environments, and 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. QS and the MIT Robotics Mobility Laboratory are developing a robust, efficient method for UGV mobility prediction on behalf of US Army ERDC. This method 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 will yield accurate predictions of mobility in general 3D terrain and not rely on idealized obstacle "primitives". In essence, the work will allow a manned or unmanned UGV to answer the question: what paths can I take through this terrain that will not get me stuck?

For more information, please contact Quantum Signal at robotics@quantumsignal.com, MIT Robotics Mobility Group at kdi@mit.edu, or US Army ERDC randy.jones@us.army.mil.