Vision-Guided-Autonomous-Road-Following-Robot

This repository contains the source code for a vision-based navigation system implemented in Python using the Robot Operating System (ROS). The project focuses on enabling a robot to autonomously follow road lanes by processing camera images and making real-time navigation decisions.

Overview

This repository includes two key components:

1. Lanes Detection and heading angle error calculation Node (lanes_detection_and_goal_angle_calculation.py):

   • Subscribes to the camera topic for receiving image messages.
   • Applies advanced image processing techniques.
   • Implements a perspective transformation to obtain a top-down view of the road.
   • Uses Hough lines for accurate left and right lane detection, determining their slopes and intercepts.
   • Calculates the average lane position for stable road following and calculates the error angle of the heading line and the desired direction
   • Publishes line detection information to the /line_follower/detector topic.

2. Line Following Controller Node (line_controller.py):

   • Subscribes to line detection information from /line_follower/detector.
   • Determines the goal angle based on the detected line's orientation.
   • Implements a trigonometrical controller or kp classical controller to derive precise linear and angular velocities for the robot.
   • Publishes velocity commands to /cmd_vel to control the robot's movement.

Dependencies

• Ubuntu 18.04 bionic
• ROS (Robot Operating System) Melodic
• Python 2.7
• OpenCV
• NumPy

Installation

• Create a New Catkin Workspace:

  • mkdir -p ~/catkin_ws/src
  • cd ~/catkin_ws
  • catkin_make

• Clone the Repository into src Directory:

  • cd ~/catkin_ws/src
  • git clone <repository_url>

• Build the Catkin Workspace:

  • cd ~/catkin_ws
  • catkin_make

• Source the Catkin Workspace:

  • source devel/setup.bash

• optional:Install System Dependencies with rosdep

  • Replace <your_ros_distro> with your ROS distribution (e.g., melodic, noetic).

  rosdep install --from-paths src --ignore-src --rosdistro <your_ros_distro> -y

Run the ROS Package:

• Navigate to your workspace. For every new terminal you open source it.

• 1st terminal: roslaunch .launch , # launch your turtlebot

• 2nd terminal: rosrun line_detection_package line_detection_and_goal_angle_calculation.py

• 3d terminal: rosrun line_detection_package road_following_controller.py

Customization

• Experiment with vision-preprocessing (line_detection_and_goal_angle_calculation.py), and controller parameters (road_following_controller.py ) to suit your robot's specifications.
• Also, experiment with the camera's physical position to ensure road lanes are within the region of interest and have a clear visual perspective.
• Modify the vision processing pipeline in line_detection_and_goal_angle_calculation.py for improved lane detection. Tweak parameters to achieve optimal performance based on your environment.

Authors

Michail Potamitis

License

This project is licensed under the MIT License.