A congestion-aware path planning method considering crowd spatial-temporal anomalies for long-term autonomy of mobile robots

Abstract

A congestion-aware path planning method is pre-sented for mobile robots during long-term deployment in human occupied environments. With known spatial-temporal crowd patterns, the robot will navigate to its destination via less congested areas. Traditional traffic-aware routing methods do not consider spatial-temporal anomalies of macroscopic crowd behaviour that can deviate from the predicted crowd spatial distribution. The proposed method improves long-term path planning adaptivity by integrating a partially updated memory (PUM) model that utilizes observed anomalies to generate a multi-layer crowd density map to improve estimation accuracy. Using this map, we are able to generate a path that has less chance to encounter the crowded areas. Simulation results show that our method outperforms the benchmark congestion-aware routing method in terms of reducing the probability of robot's proximity to dense crowds.