A1 Refereed original research article in a scientific journal
A Two-Layer Control Framework for Persistent Monitoring of a Large Area With a Robotic Sensor Network
Authors: Atman, Made Widhi Surya; Fikri, Muhamad Rausyan; Priandana, Karlisa; Gusrialdi, Azwirman
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Publication year: 2024
Journal: IEEE Access
Journal name in source: IEEE Access
Volume: 12
First page : 4153
Last page: 4165
ISSN: 2169-3536
eISSN: 2169-3536
DOI: https://doi.org/10.1109/ACCESS.2023.3349319
Web address : https://doi.org/10.1109/access.2023.3349319
Abstract
The deployment of a group of robots equipped with sensors for monitoring, also known as a robotic sensor network, is a promising technological solution to solve time-critical societal and environmental issues. This paper considers the problem of deploying a robotic sensor network to persistently and effectively monitor multiple locations of interest in a large field represented by grids. To this end, we propose a novel two-layer control framework where the first layer (i.e., task allocation strategy) encapsulates the targeted grids into a set of tasks (small regions of interest) followed by optimally allocating the robots to each task based on their initial position (location of their base stations) and sensing capabilities. The second layer (i.e., persistent monitoring algorithm) generates each robot's motion control input to ensure persistent monitoring over the designated region of interest. The proposed framework is demonstrated and evaluated via numerical simulations. It is shown that the proposed control framework improves real-time monitoring in terms of both the coverage performance and travel distance of the robots.
The deployment of a group of robots equipped with sensors for monitoring, also known as a robotic sensor network, is a promising technological solution to solve time-critical societal and environmental issues. This paper considers the problem of deploying a robotic sensor network to persistently and effectively monitor multiple locations of interest in a large field represented by grids. To this end, we propose a novel two-layer control framework where the first layer (i.e., task allocation strategy) encapsulates the targeted grids into a set of tasks (small regions of interest) followed by optimally allocating the robots to each task based on their initial position (location of their base stations) and sensing capabilities. The second layer (i.e., persistent monitoring algorithm) generates each robot's motion control input to ensure persistent monitoring over the designated region of interest. The proposed framework is demonstrated and evaluated via numerical simulations. It is shown that the proposed control framework improves real-time monitoring in terms of both the coverage performance and travel distance of the robots.
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