A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä
A Two-Layer Control Framework for Persistent Monitoring of a Large Area With a Robotic Sensor Network
Tekijät: Atman, Made Widhi Surya; Fikri, Muhamad Rausyan; Priandana, Karlisa; Gusrialdi, Azwirman
Kustantaja: Institute of Electrical and Electronics Engineers (IEEE)
Julkaisuvuosi: 2024
Journal: IEEE Access
Tietokannassa oleva lehden nimi: IEEE Access
Vuosikerta: 12
Aloitussivu: 4153
Lopetussivu: 4165
ISSN: 2169-3536
eISSN: 2169-3536
DOI: https://doi.org/10.1109/ACCESS.2023.3349319
Verkko-osoite: https://doi.org/10.1109/access.2023.3349319
Tiivistelmä
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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