  We present a hierarchical skeleton-guided motion planning algorithm to guide
mobile robots. A good skeleton maps the connectivity of the subspace of c-space
containing significant degrees of freedom and is able to guide the planner to
find the desired solutions fast. However, sometimes the skeleton does not
closely represent the free c-space, which often misleads current
skeleton-guided planners. The hierarchical skeleton-guided planning strategy
gradually relaxes its reliance on the workspace skeleton as C space is sampled,
thereby incrementally returning a sub-optimal path, a feature that is not
guaranteed in the standard skeleton-guided algorithm. Experimental comparisons
to the standard skeleton-guided planners and other lazy planning strategies
show significant improvement in roadmap construction run time while maintaining
path quality for multi-query problems in cluttered environments.
