  Path planning for multiple non-holonomic robots in continuous domains
constitutes a difficult robotics challenge with many applications. Despite
significant recent progress on the topic, computationally efficient and
high-quality solutions are lacking, especially in lifelong settings where
robots must continuously take on new tasks. In this work, we make it possible
to extend key ideas enabling state-of-the-art (SOTA) methods for multi-robot
planning in discrete domains to the motion planning of multiple Ackerman
(car-like) robots in lifelong settings, yielding high-performance centralized
and decentralized planners. Our planners compute trajectories that allow the
robots to reach precise $SE(2)$ goal poses. The effectiveness of our methods is
thoroughly evaluated and confirmed using both simulation and real-world
experiments.
