  The path planning problems arising in manipulation planning and in task and
motion planning settings are typically repetitive: the same manipulator moves
in a space that only changes slightly. Despite this potential for reuse of
information, few planners fully exploit the available information. To better
enable this reuse, we decompose the collision checking into reusable, and
non-reusable parts. We then treat the sequences of path planning problems in
manipulation planning as a multiquery path planning problem. This allows the
usage of planners that actively minimize planning effort over multiple queries,
and by doing so, actively reuse previous knowledge. We implement this approach
in EIRM* and effort ordered LazyPRM*, and benchmark it on multiple simulated
robotic examples. Further, we show that the approach of decomposing collision
checks additionally enables the reuse of the gained knowledge over multiple
different instances of the same problem, i.e., in a multiquery manipulation
planning scenario. The planners using the decomposed collision checking
outperform the other planners in initial solution time by up to a factor of two
while providing a similar solution quality.
