Geodesic
Point of Interest Coverage with Distributed Multi-Unmanned Aerial Vehicles on Dynamic Environment
Computer Science and Information Systems, Tome 20 (2023) no. 3.

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Mobile agents, which learn to optimize a task in real time, can adapt to dynamic environments and find the optimum locations with the navigation mechanism that includes a motion model. In this study, it is aimed to effectively cover points of interest (PoI) in a dynamic environment by modeling a group of unmanned aerial vehicles (UAVs) on the basis of a learning multi-agent system. Agents create an abstract rectangular plane containing the area to be covered, and then decompose the area into grids. An agent learns to locate on a center of grid that are closest to it, which has the largest number of PoIs to plan its path. This planning helps to achieve a high fairness index by reducing the number of common PoIs covered. The proposed method has been tested in a simulation environment and the results are presented by comparing with similar studies. The results show that the proposed method outperforms existing similar studies and is suitable for area coverage applications.
Keywords: unmanned aerial vehicle, multi-agent system, reinforcement learning, dynamic-area coverage, grid decomposition 
@article{CSIS_2023_20_3_a10,
     author = {Fatih Aydemir and Aydin Cetin},
     title = {Point of {Interest} {Coverage} with {Distributed} {Multi-Unmanned} {Aerial} {Vehicles} on {Dynamic} {Environment}},
     journal = {Computer Science and Information Systems},
     publisher = {mathdoc},
     volume = {20},
     number = {3},
     year = {2023},
     url = {http://geodesic.mathdoc.fr/item/CSIS_2023_20_3_a10/}
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Fatih Aydemir; Aydin Cetin. Point of Interest Coverage with Distributed Multi-Unmanned Aerial Vehicles on Dynamic Environment. Computer Science and Information Systems, Tome 20 (2023) no. 3. http://geodesic.mathdoc.fr/item/CSIS_2023_20_3_a10/