  Fast, autonomous flight in unstructured, cluttered environments such as
forests is challenging because it requires the robot to compute new plans in
realtime on a computationally-constrained platform. In this paper, we enable
this capability with a search-based planning framework that adapts sampling
density in realtime to find dynamically-feasible plans while remaining
computationally tractable. A paramount challenge in search-based planning is
that dense obstacles both necessitate large graphs (to guarantee completeness)
and reduce the efficiency of graph search (as heuristics become less accurate).
To address this, we develop a planning framework with two parts: one that
maximizes planner completeness for a given graph size, and a second that
dynamically maximizes graph size subject to computational constraints. This
framework is enabled by motion planning graphs that are defined by a single
parameter, dispersion, which quantifies the maximum trajectory cost to reach an
arbitrary state from the graph. We show through real and simulated experiments
how the dispersion can be adapted to different environments in realtime,
allowing operation in environments with varying density. The simulated
experiment demonstrates improved performance over a baseline search-based
planning algorithm. We also demonstrate flight speeds of up to 2.5m/s in
real-world cluttered pine forests.
