The Cognitive Robotic System (CRS) has been developed to use the Soar cognitive architecture for the control of
unmanned vehicles and has been tested on two heterogeneous ground robots: a six-legged robot (hexapod) and a
wheeled robot. The CRS has been used to demonstrate the applicability of Soar for unmanned vehicles by using a Soar
agent to control a robot to navigate to a target location in the presence of a cul-de-sac obstacle. Current work on the CRS
has focused on the development of computer vision, additional sensors, and map generating systems that are capable of
generating high level information from the environment that will be useful for reasoning in Soar. The scalability of Soar
allows us to add more sensors and behaviors quite easily.
Unmanned Aerial Vehicle (UAV) system integration with naval vessels is currently realized in limited form. The
operational envelopes of these vehicles are constricted due to the complexities involved with at-sea flight testing.
Furthermore, the unsteady nature of ship airwakes and the use of automated UAV control software necessitates that
these tests be extremely conservative in nature. Modeling and simulation are natural alternatives to flight testing;
however, a fully-coupled computational fluid dynamics (CFD) solution requires many thousands of CPU hours. We
therefore seek to decrease simulation time by accelerating the underlying computations using state-of-the-art,
commodity hardware. In this paper we present the progress of our proposed solution, harnessing the computational
power of high-end commodity graphics processing units (GPUs) to create an accelerated Euler equations solver on
unstructured hexahedral grids.