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\begin{document}

\title{Deep-Q-Network Hybridization with Extended Kalman Filter for Accelerate
Learning in Autonomous Navigation with the Auxiliary Security Module}



\author[1]{Carlos D. S. Bezerra}%
\author[2]{Flávio Vieira}%
\author[1]{Anderson da Silva Soares}%
\affil[1]{Universidade Federal de Goias Instituto de Informatica}%
\affil[2]{Universidade Federal de Goias - Campus Samambaia}%


\vspace{-1em}



  
  \date{May 17, 2024}


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\begin{abstract}
This article proposes an algorithm for autonomous navigation of mobile
robots that merges Reinforcement Learning with Extended Kalman Filter
(EKF) as a localization technique, namely, EKF-DQN, aiming to accelerate
learning and improve the reward values obtained in the process of
apprenticeship. More specifically, Deep Neural Networks (DQN -
\emph{Deep-Q-Networks}) are used to control the trajectory of an
autonomous vehicle in an indoor environment. Due to the ability of EKF
to predict states, this algorithm is proposed to be used as a learning
accelerator of the DQN network, predicting states ahead and inserting
this information in the memory replay. Aiming at the safety of the
navigation process, it is also proposed a visual safety system that
avoids collisions of the mobile vehicle with people moving in the
environment. The efficiency of the proposed algorithm is verified
through computer simulations using the CoppeliaSIM simulator with code
insertion in Python. The simulation results show that the EKF-DQN
algorithm accelerates the maximization of rewards obtained and provides
a higher success rate in fulfilling the proposed mobile robot mission
compared to the DQN and Q-Learning algorithms.%
\end{abstract}%



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