  Robust autonomy stacks require tight integration of perception, motion
planning, and control layers, but these layers often inadequately incorporate
inherent perception and prediction uncertainties, either ignoring them
altogether or making questionable assumptions of Gaussianity. Robots with
nonlinear dynamics and complex sensing modalities operating in an uncertain
environment demand more careful consideration of how uncertainties propagate
across stack layers. We propose a framework to integrate perception, motion
planning, and control by explicitly incorporating perception and prediction
uncertainties into planning so that risks of constraint violation can be
mitigated. Specifically, we use a nonlinear model predictive control based
steering law coupled with a decorrelation scheme based Unscented Kalman Filter
for state and environment estimation to propagate the robot state and
environment uncertainties. Subsequently, we use distributionally robust risk
constraints to limit the risk in the presence of these uncertainties. Finally,
we present a layered autonomy stack consisting of a nonlinear steering-based
distributionally robust motion planning module and a reference trajectory
tracking module. Our numerical experiments with nonlinear robot models and an
urban driving simulator show the effectiveness of our proposed approaches.
