  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.
