Cetin, HalilDurdu, Akif2020-03-262020-03-262014978-1-4799-4874-12165-0608https://hdl.handle.net/20.500.12395/3105722nd IEEE Signal Processing and Communications Applications Conference (SIU) -- APR 23-25, 2014 -- Karadeniz Teknik Univ, Trabzon, TURKEYRobotic systems which rapidly continue its development are increasingly used in our daily life. Mobile robots both draw the map where they may move in their environment and reach to the determined target in the shortest time by going on the shortest way in their prepared map. In this paper, Q-learning-based path planning algorithm is presented to find a target in the maps which are obtained by mobile robots. Q-learning is a kind of reinforcement learning algorithm that detects its environment and shows a system which makes decisions itself that how it can learn to make true decisions about reaching its target. The fact that a mobile robot truly finds targets that are located on different points in a few sample maps by processing our proposed Q-learning-based path planning algorithm is shown at the end of the paper.trinfo:eu-repo/semantics/closedAccessQ-learningsimultaneously localization and mappingpath planningConference Object21622165WOS:000356351400520N/A