Abstract
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.
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