Abstract
This work is part of a research project during the COVID-19 pandemic that aims to design and develop a mobile autonomous robot for hospitals. In practice, implementing a navigation program directly on a physical robot is both expensive and hazardous. The solution is to perform a simulation using ROS (Robot Operating System), which offers several advantages that make it an appealing option for testing and development. In an unknown hospital environment, this paper presents a simulation of the navigation process of the autonomous robot Turtlebot3 by employing the Simultaneous Localization and Mapping (SLAM) algorithm, specifically the GMapping method, utilizing the distributed software framework of ROS. In a known hospital environment, we utilize trajectory planning algorithms designed for deterministic models. However, considering the inherent uncertainty in the environment and the inevitable inaccuracies of the models, we integrate the Markov decision process (MDP) by applying the classical Q-Learning algorithm. Through these simulations, our aim is to test and refine the navigation algorithms to enhance the performance of our mobile robot. Ultimately, the proposed simulation approach contributes to the development of robotic solutions that can assist in performing various routine tasks remotely. This saves time for healthcare personnel and, most importantly, ensures their safety.
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Acknowledgment
The Project COVID-19 (2020–2022) has been funded with the support from the National Center for Scientific and Technical Research (CNRST) and Ministry of Higher Education, Morocco.
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Ben Roummane, H., Daoui, C. (2023). Localization and Navigation of ROS-Based Autonomous Robot in Hospital Environment. In: El Ayachi, R., Fakir, M., Baslam, M. (eds) Business Intelligence. CBI 2023. Lecture Notes in Business Information Processing, vol 484 . Springer, Cham. https://doi.org/10.1007/978-3-031-37872-0_12
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