Papers Published
Abstract:
We introduce a hierarchical variant of the
probabilistic roadmap method for motion
planning. By recursively refining an
initially sparse sampling in neighborhoods
of the C-obstacle boundary, our algorithm
generates a smaller roadmap that is more
likely to find narrow passages than uniform
sampling. We analyze the failure probability
and computation time, relating them to path
length, path clearance, roadmap size,
recursion depth, and a local property of the
free space. The approach is general, and can
be tailored to any variety of robots. In
particular, we describe algorithmic details
for a planar articulated arm.