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Sub-system Integration and Health Dashboard for Autonomous Mobile Robots

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Optimization, Learning Algorithms and Applications (OL2A 2023)


Data visualization has become increasingly important to improve equipment monitoring, reduce operational costs and increase process efficiency with the ever-increasing amount of data being generated and collected in various fields. This paper proposes the development of a health monitoring system for an Autonomous Mobile Robot (AMR) that allows data acquisition and analysis for decision-making. The implementation of the proposed system showed favourable results in data acquisition, analysis, and visualization for decision-making. Through the use of a hybrid control architecture, the data acquisition and processing demonstrated efficiency without significant impact on battery consumption or resource usage of the AMR embedded microcomputer. The developed dashboard proved to be efficient in navigating and visualizing the data, providing important tools for the platform manager’s decision-making. This work contributes to the health monitoring of devices based on Robot Operating System (ROS), which may be of interest to professionals and researchers in fields related to robotics and automation. Furthermore, the system presented will be open source, making it accessible and adaptable for use in different contexts and applications.

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The authors are grateful to the Foundation for Science and Technology (FCT, Portugal) for financial support through national funds FCT/MCTES (PIDDAC) to CeDRI (UIDB/05757/2020 and UIDP/05757/2020) and SusTEC (LA/P/0007/2021).

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Correspondence to André França .

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França, A., Loures, E., Jorge, L., Mendes, A. (2024). Sub-system Integration and Health Dashboard for Autonomous Mobile Robots. In: Pereira, A.I., Mendes, A., Fernandes, F.P., Pacheco, M.F., Coelho, J.P., Lima, J. (eds) Optimization, Learning Algorithms and Applications. OL2A 2023. Communications in Computer and Information Science, vol 1982 . Springer, Cham.

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