  Despite recent progress improving the efficiency and quality of motion
planning, planning collision-free and dynamically-feasible trajectories in
partially-mapped environments remains challenging, since constantly replanning
as unseen obstacles are revealed during navigation both incurs significant
computational expense and can introduce problematic oscillatory behavior. To
improve the quality of motion planning in partial maps, this paper develops a
framework that augments sampling-based motion planning to leverage a high-level
discrete layer and prior solutions to guide motion-tree expansion during
replanning, affording both (i) faster planning and (ii) improved solution
coherence. Our framework shows significant improvements in runtime and solution
distance when compared with other sampling-based motion planners.
