让机器人在不确定的动态环境中安全避障，还带统计保证。
Multi-Agent Path Finding Among Dynamic Uncontrollable Agents with Statistical Safety Guarantees
- 用学习模型预测不可控智能体行为，再用统计方法量化误差。
- 在仓库和游戏地图上，碰撞率显著降低，吞吐量与现有方法相当。
- 适合需要安全保证的长期任务，如仓储机器人调度。
现有多智能体路径规划（MAPF）求解器未考虑不可控智能体的不确定性行为。本文提出一种新型增强冲突搜索（ECBS）变体，适用于静态与长期任务场景下的动态环境。方法包括：(1) 训练一个学习预测器以估计不可控智能体的移动；(2) 使用符合性预测（Conformal Prediction, CP）量化预测误差；(3) 将不确定性区间整合至改进的ECBS求解器中。该方法可处理不可控智能体的不确定性，为一次性任务提供碰撞规避的统计保证，并通过滚动时域将长期任务分解为一系列一次性实例实现可扩展性。我们在仓库与游戏地图上测试了所提算法（CP-Solver），在保持竞争吞吐量的同时显著减少碰撞事件。
原文摘要 · Abstract (English)
Existing multi-agent path finding (MAPF) solvers do not account for uncertain behavior of uncontrollable agents. We present a novel variant of Enhanced Conflict-Based Search (ECBS), for both one-shot and lifelong MAPF in dynamic environments with uncontrollable agents. Our method consists of (1) training a learned predictor for the movement of uncontrollable agents, (2) quantifying the prediction error using conformal prediction (CP), a tool for statistical uncertainty quantification, and (3) integrating these uncertainty intervals into our modified ECBS solver. Our method can account for uncertain agent behavior, comes with statistical guarantees on collision-free paths for one-shot missions, and scales to lifelong missions with a receding horizon sequence of one-shot instances. We run our algorithm, CP-Solver, across warehouse and game maps, with competitive throughput and reduced collisions.
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