  Path planning is an active area of research essential for many applications
in robotics. Popular techniques include graph-based searches and sampling-based
planners. These approaches are powerful but have limitations. This paper
continues work to combine their strengths and mitigate their limitations using
a unified planning paradigm. It does this by viewing the path planning problem
as the two subproblems of search and approximation and using advanced
graph-search techniques on a sampling-based approximation. This perspective
leads to Advanced BIT*. ABIT* combines truncated anytime graph-based searches,
such as ATD*, with anytime almost-surely asymptotically optimal sampling-based
planners, such as RRT*. This allows it to quickly find initial solutions and
then converge towards the optimum in an anytime manner. ABIT* outperforms
existing single-query, sampling-based planners on the tested problems in
$\mathbb{R}^{4}$ and $\mathbb{R}^{8}$, and was demonstrated on real-world
problems with NASA/JPL-Caltech.
