让机器人在人流中智能规划路线，避开拥堵，提升通行效率。
Congestion-Aware Robot Tour Planning in Crowded Environments
- 用人流预测模型预判行人轨迹，动态生成路径
- 在线构建马尔可夫决策过程，实时重规划路线
- 适合商场、博物馆等高密度人流场景的机器人导航
自主移动服务机器人常需在环境中完成一系列地点的巡游任务，如商场导览、仓储配送或博物馆讲解。然而在人流密集区域，行人的存在会触发机器人避障机制，导致速度下降。人群行为具有随机性且随时间变化。本文提出一种概率化巡游规划方法，显式建模人类拥堵情况。通过学习圆形线性流场（CLiFF）地图，基于初始观测预测行人轨迹，并利用这些预测构建并求解在线马尔可夫决策过程，高效引导机器人穿越环境。该方法具备可扩展性，能随新行人出现实时重规划。我们在真实商场人群数据集上进行了评估。
原文摘要 · Abstract (English)
Autonomous mobile service robots are often required to complete tours that require navigating through a set of locations in an environment. Example domains include guiding people through a shopping mall, delivering packages in a fulfilment centre, or giving guided tours in a museum. However, in crowded environments, the presence of people may negatively impact robot performance. For example, humans will activate robot collision avoidance manoeuvres that slow the robot down. Crowds move stochastically and vary throughout the day. In this paper we present a probabilistic tour planner for crowded environments which explicitly reasons over human congestion. We learn circular linear flow field (CLiFF) maps which predict human trajectories given an initial observation. We then use these predictions to build and solve a Markov decision process online which efficiently routes the robot through the environment. Our approach is scalable enough to re-plan as new people are observed. We evaluate our approach on a real-world crowd dataset in a shopping mall.
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