提出可扩展的多智能体路径规划后处理流水线，支持超500智能体实时运行。
A Scalable Post-Processing Pipeline for Large-Scale Free-Space Multi-Agent Path Planning with PiBT
- 结合优先级继承与回溯（PiBT）和安全路径平滑策略
- 在8连通网格上实现超500智能体实时规划，路径长度接近最优
- 适合需要高可扩展性的机器人导航系统，尤其连续空间场景
自由空间多智能体路径规划在大规模下仍具挑战性。现有方法要么保证最优但难以扩展至数十个以上智能体，要么依赖网格假设，在连续空间中泛化能力差。本文提出一种混合规则式规划框架，融合优先级继承与回溯（PiBT）与新型安全感知路径平滑方法。该方法将PiBT扩展至8连通网格，并选择性应用基于绳拉的路径平滑，通过局部交互感知和基于安全时间路径规划（SIPP）的回退碰撞解决机制保障安全性。该设计在保持实时性能的同时有效缩短路径长度。实验表明，本方法可在大规模自由空间环境中支持超过500个智能体，相比现有任意角及最优方法，在运行时间上表现更优，且在稀疏域生成近最优轨迹。结果表明，该框架是突破网格约束、实现可扩展实时多智能体导航的有力候选方案。
原文摘要 · Abstract (English)
Free-space multi-agent path planning remains challenging at large scales. Most existing methods either offer optimality guarantees but do not scale beyond a few dozen agents, or rely on grid-world assumptions that do not generalize well to continuous space. In this work, we propose a hybrid, rule-based planning framework that combines Priority Inheritance with Backtracking (PiBT) with a novel safety-aware path smoothing method. Our approach extends PiBT to 8-connected grids and selectively applies string-pulling based smoothing while preserving collision safety through local interaction awareness and a fallback collision resolution step based on Safe Interval Path Planning (SIPP). This design allows us to reduce overall path lengths while maintaining real-time performance. We demonstrate that our method can scale to over 500 agents in large free-space environments, outperforming existing any-angle and optimal methods in terms of runtime, while producing near-optimal trajectories in sparse domains. Our results suggest this framework is a promising building block for scalable, real-time multi-agent navigation in robotics systems operating beyond grid constraints.
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