  Randomized sampling based algorithms are widely used in robot motion planning
due to the problem's intractability, and are experimentally effective on a wide
range of problem instances. Most variants do not sample uniformly at random,
and instead bias their sampling using various heuristics for determining which
samples will provide more information, or are more likely to participate in the
final solution. In this work, we define the \emph{motion planning guiding
space}, which encapsulates many seemingly distinct prior works under the same
framework. In addition, we suggest an information theoretic method to evaluate
guided planning which places the focus on the quality of the resulting biased
sampling. Finally, we analyze several motion planning algorithms in order to
demonstrate the applicability of our definition and its evaluation.
