Caccavale Riccardo, Ermini Mirko, Fedeli Eugenio, Finzi Alberto, Lippiello Vincenzo, Tavano Fabrizio
Department DIETI, Università degli Study di Napoli "Federico II", via Claudio 21, Naples, 80125 Italy.
Department Research and Development, Rete Ferroviaria Italiana, Via Curzio Malaparte 8, Firenze Osmannoro, 50145 Italy.
Appl Intell (Dordr). 2023 Apr 18:1-19. 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.
对火车站进行消毒是一个重要问题，主要是由于新冠疫情的最新发展。在这项工作中，我们基于分布式深度Q学习技术，提出了一种用于火车站消毒的多机器人方法。所提出的框架依赖于来自现有WiFi网络的匿名数据，以动态估计站内拥挤区域，并生成待消毒优先区域的热图。然后将这种热图提供给一组清洁机器人——每个机器人都配备有特定于机器人的卷积神经网络——这些机器人学习如何根据相关优先级有效地协作并对车站区域进行消毒。所提出的方法在意大利最大的火车站罗马中央车站提供的真实模拟场景中进行了评估。在此环境中，我们考虑了不同的案例研究，以评估该方法如何随机器人数量扩展，以及经过训练的系统在从该车站WiFi网络一天的数据记录中检索到的真实数据集上的表现。
