  Search-based techniques have shown great success in motion planning problems
such as robotic navigation by discretizing the state space and precomputing
motion primitives. However in domains with complex dynamic constraints,
constructing motion primitives in a discretized state space is non-trivial.
This requires operating in continuous space which can be challenging for
search-based planners as they can get stuck in local minima regions. Previous
work on planning in continuous spaces introduced soft duplicate detection which
requires search to compute the duplicity of a state with respect to previously
seen states to avoid exploring states that are likely to be duplicates,
especially in local minima regions. They propose a simple metric utilizing the
euclidean distance between states, and proximity to obstacles to compute the
duplicity. In this paper, we improve upon this metric by introducing a
kinodynamically informed metric, subtree overlap, between two states as the
similarity between their successors that can be reached within a fixed time
horizon using kinodynamic motion primitives. This captures the intuition that,
due to robot dynamics, duplicate states can be far in euclidean distance and
result in very similar successor states, while non-duplicate states can be
close and result in widely different successors.
