Local guidance has recently proven to be a powerful driver of empirical performance in real-time, suboptimal multi-agent pathfinding (MAPF), improving the scalable configuration-based solver LaCAM. By injecting informative spatiotemporal cues around each agent, local guidance mitigates congestion, reduces waiting, and remains scalable enough even with tight time budgets, yielding state-of-the-art performance for one-shot MAPF. This study asks whether the same benefits can be lifted to the lifelong setting (LMAPF), where tasks arrive continuously and improvements in per-step plans can increase task completion throughput over long horizons. We propose LLLG, a Lifelong version of LaCAM enhanced with Local Guidance, which employs a receding-horizon windowed planning framework and warm-starts guidance from the previous solution at each timestep. Our method scales effectively, maintains high throughput even in compact, dense environments, and surpasses existing planners, thereby pushing the frontier of real-time, lifelong MAPF.

翻译：局部导引近期已被证实是实时次优多智能体路径规划（MAPF）中提升实证性能的强大驱动力，它改进了基于可扩展构型的求解器LaCAM。通过为每个智能体注入信息丰富的时空线索，局部导引能够缓解拥堵、减少等待时间，即使在严格时间预算下仍保持高度可扩展性，从而在一次性MAPF中达到了最优性能。本研究探讨能否将相同优势推广至终身制场景（LMAPF）——其中任务持续到达，且单步规划方案的改进可长期提升任务完成吞吐量。我们提出LLLG，一种引入局部导引的LaCAM终身版本，采用滚动时域窗口式规划框架，并在每个时间步利用前序解进行热启动导引。该方法具备高效可扩展性，即使在紧凑密集环境中仍能维持高吞吐量，并超越现有规划器，从而推动实时终身制MAPF前沿发展。