#Overview This project implements a control system for a robotic navigation task using Extended Kalman Filter (EKF). The goal is to navigate a robot from an initial position to a desired target position in a two-dimensional space, while accounting for process and measurement noise.

#Features Simulation of robot movement using feedback linearization control. Implementation of Extended Kalman Filter for state estimation under noise. Visualization of the robot's trajectory and orientation over time.

#Requirements Python 3.x NumPy Matplotlib

#Installation Clone the repository to your local machine: git clone https://github.com/dark-syndrome/EKF_control_simulation

#Usage: Navigate to the project directory and run the main Python script: python main.py This will execute the simulation and display the resulting plots.

#Files in the Repository main.py: The main script that runs the simulation and visualization. ekf_control_module.py: Module containing the manrobot_with_control function for robot control and EKF implementation. plot_utils.py: Utility script for generating plots of the robot's trajectory and orientation.

#Configuration You can adjust the simulation parameters in main.py, such as the initial state, target position, time step, and total simulation time.

#Author Akash Deshapathi