发表机构
Dept. of Aerospace Engineering Sciences; Dept. of Computer Science, University of Colorado Boulder（航空航天工程科学系; 科罗拉多大学博尔德分校计算机科学系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究拥挤环境中目标拦截问题，将其建模为部分可观测马尔可夫决策过程，用树搜索在线求解。对比顺序路径速度规划器和统一规划器，发现高密度时前者安全拦截率低、耗时多，揭示了空间限制的结构局限性。
AI 中文摘要
在拥挤环境中的目标拦截要求在多个不确定的人类智能体之间导航时到达移动目标。由于人类导航意图不可直接观测，机器人必须对多种可能的未来交互结果进行推理。我们将人群中的拦截问题表述为部分可观测马尔可夫决策过程，并在固定计算预算下使用树搜索在线求解。在此设置下，动作空间结构直接塑造搜索树以及计算资源的分配方式。我们对顺序路径速度规划器（先规划空间路径再沿其调整速度）和在树搜索中联合对转向和速度进行分支的统一规划器进行了对比。在多达200个人的模拟中，在低密度人群时两种方法表现相似，但随着密度增加差异显著。在最高人群密度下，顺序规划器的安全拦截率低31个百分点，所需时间比统一转向速度规划器多44%，揭示了空间限制的结构局限性。
英文摘要
Target interception in crowded environments requires reaching a moving objective while navigating among multiple uncertain human agents. Since human navigation intent is not directly observable, the robot must reason over multiple possible future interaction outcomes. We formulate interception in crowds as a partially observable Markov decision process and solve it online using tree search under a fixed computational budget. In this setting, the action-space structure directly shapes the search tree and how computational effort is allocated. We perform a controlled comparison between a sequential path-speed planner, which first plans a spatial path and then modulates speed along it, and a unified planner that jointly branches over steering and speed within tree search. Across simulations with up to 200 humans, both approaches perform similarly at low crowd density but diverge sharply as density increases. At the highest crowd density, the sequential planner has a safe-interception rate 31 percentage points lower and requires 44% more time than the unified steering-speed planner, revealing a structural limitation of spatial restriction. Project webpage: https://tic-planning.github.io/
CommentsAccepted for publication at the 2026 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2026)
