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

• Published in: Applied intelligence (Dordrecht, Netherlands) Vol. 53; no. 17; pp. 20595 - 20613
• Main Authors: Caccavale, Riccardo, Ermini, Mirko, Fedeli, Eugenio, Finzi, Alberto, Lippiello, Vincenzo, Tavano, Fabrizio
• Format: Journal Article
• Language: English
• Published: New York Springer US 01-09-2023; Springer Nature B.V
• Subjects: Artificial Intelligence; Artificial neural networks; Computer Science; Data recording; Learning; Machines; Manufacturing; Mechanical Engineering; Multiple robots; Processes; Railway stations; Robots; Transportation terminals; Multi-agent; Decentralized; Sanitization; Heatmap; Deep Q-network; Convolutional neural network
• ISSN: 0924-669X; 1573-7497; 1573-7497
• DOI: 10.1007/s10489-023-04529-0

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.