  Path planning in an uncertain environment is a key enabler of true vehicle
autonomy. Over the past two decades, numerous approaches have been developed to
account for errors in the vehicle path while navigating complex and often
uncertain environments. An important capability of such planning is the
prediction of vehicle dispersion covariances about a candidate path. This work
develops a new closed-loop linear covariance (CL-LinCov) framework applicable
to a wide range of autonomous system architectures. Important features of the
developed framework include the (1) separation of high-level guidance from
low-level control, (2) support for output-feedback controllers with internal
states, dynamics, and output, and (3) multi-use continuous sensors for
navigation state propagation, guidance, and feedback control. The closed-loop
nature of the framework preserves the important coupling between the system
dynamics, exogenous disturbances, and the guidance, navigation, and control
algorithms. The developed framework is applied to a simplified model of an
unmanned aerial vehicle and validated by comparison via Monte Carlo analysis.
The utility of the CL-LinCov information is illustrated by its application to
path planning in a static, uncertain obstacle field via a modified version of
the Rapidly Exploring Random Tree algorithm.
