# Autonomous Navigation of Robots: Optimization with DQN
## 1 Introduction
In this context, the aim of this research is to present an autonomous model capable of controlling and reconfiguring the route of a mobile robot in real time by learning from its previous paths; an autonomous learning method based on reinforcements was implemented, which must learn to avoid fixed and mobile obstacles, in order to reach the target position and obtain a positive reward. This study focuses on two main objectives. Firstly, it involves the calculation of Turtlebot3 Burger kinematics, which is crucial for determining the differential control of each wheel. This calculation is essential for accurately controlling the movement of the robot. The second objective of this research is to develop a control system based on DQN (Deep Q-Network) and reinforcement learning. The aim is to design a system that utilizes the most suitable optimization algorithm to minimize errors and enhance performance. Python was chosen as the programming language for this model due to its extensive documentation and support for the Keras library. Python and Keras provide a robust framework for network development and training. To facilitate communication between the simulated environment and the receiver and controller nodes, the ROS (Robot Operating System) API was utilized. This enables the exchange of position, odometry, and speed messages, ensuring seamless interoperability between components. The ROS APIoffers a convenient interface for integrating different modules and simplifies the overall system implementation. In the context of mobile robot control, the existing research has primarily focused on the use of predetermined paths for the robot's trajectory. While this approach has proved to be effective in many cases, it does not account for real-time methods for avoiding obstacles and reconfiguring the robot's route. This creates a significant research gap that needs to be addressed to develop more intelligent and efficient mobile robots. Therefore, this study aims to fill this research gap by presenting an autonomous learning method based on reinforcement learning and a control system based on the DQN algorithm. These components enable the mobile robot to learn from its previous paths, avoid obstacles, and reconfigure its route in real time, which leads to more efficient and intelligent mobile robots. The significance of this research lies in its contribution to the advancement of autonomous robotics by developing a more efficient and intelligent control system for mobile robots, which has implications for various industrial and service sectors. The research contribution of this study can be summarized in two highlights. Firstly, it presents an autonomous learning method based on reinforcements, which enables a mobile robot to learn from its previous paths and reconfigure its route in real time while avoiding obstacles. This method is critical for the development of more intelligent and efficient mobile robots that can adapt to their environments and perform their tasks autonomously. Secondly, this research introduces a control system that utilizes the DQN algorithm and reinforcement learning to enhance the robot's path planning by minimizing errors. The design of the control system is based on the most suitable optimization algorithm, and Python is chosen as the programming language for model development, including network training, due to its capabilities and the availability of extensive resources. The communication between the simulated environment and the receiver and controller nodes is established using the ROS API. This integration allows for seamless interoperability by enabling the exchange of commands. As a result, the mobile robot can effectively transmit and receive position, odometry, and speed messages. This integration significantly improves the robot's efficiency and accuracy in performing its tasks. The outcomes of this study contribute to the advancement of autonomous robotics by providing a more intelligent and efficient control system for mobile robots.