改进快速路径规划器PIBT，让机器人能高效完成旋转等复杂动作
Enhancing PIBT via Multi-Action Operations
- 引入多动作操作，让PIBT能规划连续动作而非单步决策
- 在保持毫秒级速度的同时，显著提升有朝向机器人的规划效果
- 适合需要快速响应的多智能体系统，如自动驾驶、仓储机器人
PIBT是一种基于规则的多智能体路径规划（MAPF）求解器，广泛用作当前先进方法中的低层规划器或动作采样器。其核心优势是极快的计算速度，可在毫秒内为数千个智能体完成下一步动作选择。然而，这种短时域设计在智能体具有朝向且需执行耗时旋转动作的场景下表现不佳。本文提出一种增强版PIBT，通过引入多动作操作来克服这一局限。我们详细阐述了对PIBT的改进措施，在保持其高效性的同时提升了性能。此外，当结合图引导技术和大邻域搜索优化时，该方法在在线LMAPF-T设置中达到当前最优水平。
原文摘要 · Abstract (English)
PIBT is a rule-based Multi-Agent Path Finding (MAPF) solver, widely used as a low-level planner or action sampler in many state-of-the-art approaches. Its primary advantage lies in its exceptional speed, enabling action selection for thousands of agents within milliseconds by considering only the immediate next timestep. However, this short-horizon design leads to poor performance in scenarios where agents have orientation and must perform time-consuming rotation actions. In this work, we present an enhanced version of PIBT that addresses this limitation by incorporating multi-action operations. We detail the modifications introduced to improve PIBT's performance while preserving its hallmark efficiency. Furthermore, we demonstrate how our method, when combined with graph-guidance technique and large neighborhood search optimization, achieves state-of-the-art performance in the online LMAPF-T setting.
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