PPUM-Aided Receding Horizon Optimization for Robot Path planning in Uncertain Environments

The ability to understand spatialtemporal patterns for crowds of people is crucial for achieving long-term autonomy of mobile robots deployed in human environments. The traditional historical data-driven memory models may be inadequate due to their limitations in adapting to anomalies or unexpected events in crowd behavior. 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 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 humans in crowded environments with anomalies. In simulations, 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