  Modern sampling-based motion planning algorithms typically take between
hundreds of milliseconds to dozens of seconds to find collision-free motions
for high degree-of-freedom problems. This paper presents performance
improvements of more than 500x over the state-of-the-art, bringing planning
times into the range of microseconds and solution rates into the range of
kilohertz, without specialized hardware. Our key insight is how to exploit
fine-grained parallelism within sampling-based planners, providing
generality-preserving algorithmic improvements to any such planner and
significantly accelerating critical subroutines, such as forward kinematics and
collision checking. We demonstrate our approach over a diverse set of
challenging, realistic problems for complex robots ranging from 7 to 14
degrees-of-freedom. Moreover, we show that our approach does not require
high-power hardware by also evaluating on a low-power single-board computer.
The planning speeds demonstrated are fast enough to reside in the range of
control frequencies and open up new avenues of motion planning research.
