The past decade has seen the rapid development and deployment of unmanned systems throughout the world in both civilian and military applications. Significant development has been led by the Department of Defense (DoD), which has sought to develop and field military systems, such as unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs), with elevated levels of autonomy to accomplish their mission with reduced funding and manpower. As their role increases, such systems must be able to adapt and learn, and make nondeterministic decisions. Current unmanned systems exhibit minimal autonomous behaviors. As their autonomy increases and their behaviors become more intelligent (adapting and learning from previous experiences), the state space for their behaviors becomes non deterministic or intractably complex.

Consequently, fielding such systems requires extensive testing and evaluation, as well as verification and validation to determine a system’s performance and the acceptable level of risk to make it releasable – a challenging task. To address this, I apply a novel systems perspective to develop a model-based framework to predict future system performance based on the complexity of the operating environment using newly introduced complexity measures and learned costs. Herein I consider an autonomous military ground robot navigating in complex off-road environments. Using my model and data from Defense Advanced Research Projects Agency (DARPA)-led experiments, I demonstrate the accuracy with which my model can predict system performance and then validate my model against other experimental results.