A multi-robot deep Q-learning framework for priority-based sanitization of railway stations

Sanitizing railway stations is a relevant issue, primarily due to the recent evolution of the Covid-19 pandemic. In this work, we propose a multi-robot approach to sanitize railway stations based on a distributed Deep Q-Learning technique. The proposed framework relies on anonymous data from existing WiFi networks to dynamically estimate crowded areas within the station and to develop a heatmap of prioritized areas to be sanitized. Such heatmap is then provided to a team of cleaning robots - each endowed with a robot-specific convolutional neural network - that learn how to effectively cooperate and sanitize the station's areas according to the associated priorities. The proposed approach is evaluated in a realistic simulation scenario provided by the Italian largest railways station: Roma Termini. In this setting, we consider different case studies to assess how the approach scales with the number of robots and how the trained system performs with a real dataset retrieved from a one-day data recording of the station's WiFi network.

Automated summary

In this study, a multi-robot approach based on distributed deep Q-learning technique is proposed to sanitize railway stations. The approach utilizes anonymous data from existing WiFi networks to estimate crowded areas within the station and develops a prioritized heatmap for sanitation. A team of cleaning robots, each equipped with a robot-specific convolutional neural network, effectively cooperates to sanitize the station's areas according to the priorities. The approach is evaluated in a realistic simulation scenario at Rome Termini, the largest railway station in Italy, by considering different case studies and a real dataset from one-day data recording of the station's WiFi network.