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.

翻译：连续域中多台非完整约束机器人的路径规划是一项具有众多应用场景的困难机器人学挑战。尽管该领域近期取得了显著进展，但高效且高质量的计算解决方案仍然缺乏，特别是在机器人需持续接收新任务的终身场景中。本研究通过将离散域多机器人规划先进方法中的关键思想拓展至多台阿克曼（类车）机器人在终身场景中的运动规划，成功实现了高性能的中心化与去中心化规划器。我们提出的规划器能够计算使机器人精确到达 $SE(2)$ 目标位姿的轨迹。通过仿真与真实世界实验，我们充分验证并确认了所提方法的有效性。