  Motion planning algorithms often leverage topological information about the
environment to improve planner performance. However, these methods often focus
only on the environment's connectivity while ignoring other properties such as
obstacle clearance, terrain conditions, and resource accessibility. We present
a method that augments a skeleton representing the workspace topology with such
information to guide a sampling-based motion planner to rapidly discover
regions most relevant to the problem at hand. Our approach decouples guidance
and planning, making it possible for basic planning algorithms to find desired
paths earlier in the planning process. We demonstrate the efficacy of our
approach in both robotics problems and applications in drug design. Our method
is able to produce desirable paths quickly with no change to the underlying
planner.
