# Autonomous Navigation of Robots: Optimization with DQN
## 5 Design and Execution Strategy for the Control System: A Comprehensive Overview
### Understanding the Robot's Environment: Utilizing Lidar Sensor for State Perception  
The robot state plays a crucial role in the control system, as it encompasses the observations made during the interactions between the Turtlebot3 Burger and its environment. By capturing and analyzing the state information, the control system can make informed decisions for effective robot behavior. To enable state perception, the Turtlebot3 Burger is equipped with a 2D laser distance sensor known as Lidar or LDS. This sensor provides a comprehensive view of the robot's surroundings, allowing it to sample the environment in a 360-degree range and detect objects located between 120 mm and 3500 mm. The Lidar is mounted on a rotating head, which offers the flexibility to adjust the sample size based on the specific requirements of the application. In the simulation environment, Gazebo, a carefully chosen configuration of 11 Lidar points is utilized. This choice strikes a balance between the utilization of computational resources and the inputs required for the control system. By carefully selecting the number of Lidar points, the control system can effectively capture relevant information about the environment while maintaining computational efficiency. With this comprehensive perception of the robot's surroundings, the control system can leverage the state information to analyze the environment, identify obstacles, and plan optimal trajectories for obstacle avoidance. The accurate and detailed perception obtained through the Lidar sensor greatly enhances the overall capabilities of the robot and facilitates its safe and efficient navigation in complex environments. By leveraging the state perception capabilities, the control system can ensure that the robot interacts with its surroundings in a way that aligns with its objectives, such as avoiding obstacles and efficiently navigating through its environment. The precise representation of the robot state, facilitated by the Lidar sensor, lays the foundation for effective decision making and control actions to be taken by the robot. \[State=LDS(11 values)+Distance(1)+Angle(1) \tag{8}\]  
The \(11\) sensor values obtained from the Lidar sensor are crucial for capturing the robot's perception of its environment. These sensor values are transmitted through the Robot Operating System (ROS) framework, where they are published in a designated topic by a specific node. This allows other components of the system to access and utilize this valuable information. In addition to the sensor values, the state representation includes two additional components. These components are derived from the angle between the rotation of the robot and the distance to the target. The calculation of this angle relies on the availability of odometry information, which provides the position of the robot with respect to its reference frame. The odometry data are published in the topic called \"/ odom,\" allowing the control system to access and incorporate it into the state representation. By combining the \(11\) sensor values with the angle and odometry information, the state representation comprises a total of \(13\) components. This extended state representation provides a more comprehensive understanding of the robot's surroundings and its position relative to the target. Figures 6 and 7 visually illustrate the relationship between the sensor values, angle, and odometry in the state representation. Equation (8) represents the mathematical formulation of the state, highlighting the individual components that contribute to its definition. The integration of sensor values, angle, and odometry information into the state representation enables the control system to make informed decisions based on a more comprehensive understanding of the robot's current state and its relation to the target. This enhanced state representation is vital for effective path planning, obstacle avoidance, and overall navigation performance of the Turtlebot3 Burger in its environment.