The original sampling-based algorithms (e.g., PRM, RRT), with a variety of
follow-ups, typically use collision checking to validate local paths up to a
given resolution. Free local paths are assembled into a graph capturing the
connectivity of free configuration space to find collision-free paths. In
general, distance/proximity query implies a computational surplus compared to a
Boolean collision check. However, distance information can be used to infer
about the free volumes of C-space, which may serve as generators of arbitrarily
many local paths that are collision-free by design. A method to quickly explore
C-spaces of robotic manipulators and thus facilitate motion planning is
described. The method is based on a geometrical structure called generalized
bur. It is a star-like tree, rooted at a given point in free C-space, and with
an arbitrary number of guaranteed collision-free edges computed using distance
information from the workspace and simple forward kinematics. Generalized bur
captures large portions of free C-space, enabling accelerated exploration. When
plugged in a suitable RRT-like planning algorithm, generalized burs enable
significant performance improvements with respect to competing algorithms. Of
particular interest are human-centric environments where humans/obstacles move
and thus render the problem of motion planning remarkably challenging.