  Despite the performance advantages of modern sampling-based motion planners,
solving high dimensional planning problems in near real-time remains a
challenge. Applications include hyper-redundant manipulators, snake-like and
humanoid robots. Based on the intuition that many of these problem instances do
not require the robots to exercise every degree of freedom independently, we
introduce an enhancement to popular sampling-based planning algorithms aimed at
circumventing the exponential dependence on dimensionality. We propose
beginning the search in a lower dimensional subspace of the configuration space
in the hopes that a simple solution will be found quickly. After a certain
number of samples are generated, if no solution is found, we increase the
dimension of the search subspace by one and continue sampling in the higher
dimensional subspace. In the worst case, the search subspace expands to include
the full configuration space - making the completeness properties identical to
the underlying sampling-based planer. Our experiments comparing the enhanced
and traditional version of RRT, RRT-Connect, and BidirectionalT-RRT on both a
planar hyper-redundant manipulator and the Baxter humanoid robot indicate that
a solution is typically found much faster using this approach and the run time
appears to be less sensitive to the dimension of the full configuration space.
We explore important implementation issues in the sampling process and discuss
its limitations.
