  This paper considers the integration of gap-based local navigation methods
with artificial potential field (APF) methods to derive a local planning module
for hierarchical navigation systems that has provable collision-free
properties. Given that APF theory applies to idealized robot models, the
provable properties are lost when applied to more realistic models. We describe
a set of algorithm modifications that correct for these errors and enhance
robustness to non-ideal models. Central to the construction of the local
planner is the use of sensory-derived local free-space models that detect gaps
and use them for the synthesis of the APF. Modifications are given for a
nonholonomic robot model. Integration of the local planner, called potential
gap, into a hierarchical navigation system provides the local goals and
trajectories needed for collision-free navigation through unknown environments.
Monte Carlo experiments in benchmark worlds confirm the asserted safety and
robustness properties by testing under various robot models.
