机器人在人群里拦截移动目标，用在线规划提升安全与效率
Beyond Fixed Goal Delivery: Online POMDP Planning for Target Interception in Crowds
- 将人群拦截建模为部分可观马尔可夫决策过程，采用树搜索在线求解
- 高密度人群下统一规划比分步规划安全率低31个百分点，耗时多44%
- 适合需要实时避障的智能体导航场景，如服务机器人或自动驾驶
在拥挤环境中实现目标拦截需在移动目标的同时避开多个不确定的人类障碍。由于人类行为意图不可直接观测，机器人必须推断多种可能的未来交互结果。本文将人群中的拦截问题建模为部分可观马尔可夫决策过程（POMDP），并在固定计算预算下通过树搜索在线求解。动作空间结构直接影响搜索树形态及计算资源分配。我们对比了两种策略：先规划路径再调节速度的顺序式规划器，以及在树搜索中联合分支转向与速度的统一规划器。在最多200人的仿真中，低密度下两者表现相近，但随密度升高差异显著：最高密度下，顺序规划器安全拦截率低31个百分点，耗时增加44%，揭示了路径受限结构的根本缺陷。
原文摘要 · Abstract (English)
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/
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