The ability to understand spatial-temporal patterns for crowds of people is crucial for achieving long-term autonomy of mobile robots deployed in human environments. However, traditional historical data-driven memory models are inadequate for handling anomalies, resulting in poor reasoning by robot in estimating the crowd spatial distribution. In this article, a Receding Horizon Optimization (RHO) formulation is proposed that incorporates a Probability-related Partially Updated Memory (PPUM) for robot path planning in crowded environments with uncertainties. The PPUM acts as a memory layer that combines real-time sensor observations with historical knowledge using a weighted evidence fusion theory to improve robot's adaptivity to the dynamic environments. RHO then utilizes the PPUM as a informed knowledge to generate a path that minimizes the likelihood of encountering dense crowds while reducing the cost of local motion planning. The proposed approach provides an innovative solution to the problem of robot's long-term safe interaction with human in uncertain crowded environments. In simulation, the results demonstrate the superior performance of our approach compared to benchmark methods in terms of crowd distribution estimation accuracy, adaptability to anomalies and path planning efficiency.

翻译：理解人群时空模式对于部署在人类环境中的移动机器人实现长期自主性至关重要。然而，传统的基于历史数据的记忆模型难以处理异常情况，导致机器人在估计人群空间分布时推理能力不足。本文提出了一种结合概率部分更新记忆（PPUM）的滚动时域优化（RHO）方法，用于在存在不确定性的拥挤环境中进行机器人路径规划。PPUM作为记忆层，通过加权证据融合理论将实时传感器观测与历史知识相结合，以增强机器人对动态环境的适应性。RHO随后将PPUM作为先验知识，生成能够最小化遭遇密集人群概率的路径，同时降低局部运动规划的成本。所提方法为机器人在不确定拥挤环境中与人类实现长期安全交互提供了创新解决方案。仿真结果表明，在人群分布估计精度、异常适应性和路径规划效率方面，该方法相较于基准方法具有更优性能。