  We present a hierarchical tree-based motion planning strategy, HAS-RRT,
guided by the workspace skeleton to solve motion planning problems in robotics
and computational biology. Relying on the information about the connectivity of
the workspace and the ranking of available paths in the workspace, the strategy
prioritizes paths indicated by the workspace guidance to find a valid motion
plan for the moving object efficiently. In instances of suboptimal guidance,
the strategy adapts its reliance on the guidance by hierarchically reverting to
local exploration of the planning space. We offer an extensive comparative
analysis against other tree-based planning strategies and demonstrate that
HAS-RRT reliably and efficiently finds low-cost paths. In contrast to methods
prone to inconsistent performance across different environments or reliance on
specific parameters, HAS-RRT is robust to workspace variability.
