  In this work, we present a novel sampling-based path planning method, called
SPRINT. The method finds solutions for high dimensional path planning problems
quickly and robustly. Its efficiency comes from minimizing the number of
collision check samples. This reduction in sampling relies on heuristics that
predict the likelihood that samples will be useful in the search process.
Specifically, heuristics (1) prioritize more promising search regions; (2) cull
samples from local minima regions; and (3) steer the search away from
previously observed collision states. Empirical evaluations show that our
method finds shorter or comparable-length solution paths in significantly less
time than commonly used methods. We demonstrate that these performance gains
can be largely attributed to our approach to achieve sample efficiency.
