Sanitizing railway stations is a relevant issue especially 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 framework relies on anonymous information from existing WiFi networks to localize passengers inside the station and to develop a map of possible risky areas to be sanitized. Starting from this map, a swarm of cleaning robots, each one endowed with a robot-specific convolutional neural network, learns how to on-line cooperate inside the station in order to maximize the sanitized area depending on the presence of the passengers.

Multi-robot Sanitization of Railway Stations Based on Deep Q-Learning / Caccavale, R.; Cala, V.; Ermini, M.; Finzi, A.; Lippiello, V.; Tavano, F.. - 3162:(2022), pp. 34-39. (Intervento presentato al convegno 8th Italian Workshop on Artificial Intelligence and Robotics, AIRO 2021 nel 2021).

Multi-robot Sanitization of Railway Stations Based on Deep Q-Learning

Caccavale R.;Finzi A.;Lippiello V.;Tavano F.
2022

Abstract

Sanitizing railway stations is a relevant issue especially 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 framework relies on anonymous information from existing WiFi networks to localize passengers inside the station and to develop a map of possible risky areas to be sanitized. Starting from this map, a swarm of cleaning robots, each one endowed with a robot-specific convolutional neural network, learns how to on-line cooperate inside the station in order to maximize the sanitized area depending on the presence of the passengers.
2022
Multi-robot Sanitization of Railway Stations Based on Deep Q-Learning / Caccavale, R.; Cala, V.; Ermini, M.; Finzi, A.; Lippiello, V.; Tavano, F.. - 3162:(2022), pp. 34-39. (Intervento presentato al convegno 8th Italian Workshop on Artificial Intelligence and Robotics, AIRO 2021 nel 2021).
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/920348
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