RRT Path Finding Project for Autonomous Driving

This project implements the Rapidly-exploring Random Tree (RRT) algorithm for pathfinding in autonomous driving. The algorithm efficiently computes a collision-free path from a given starting point to a specified goal point on a map image, considering obstacles.

Example output

Table of Contents

• Introduction
• How to Use
• Installation
• Dependencies
• Running the Project
• Results

Introduction

The goal of this project is to provide a reliable and fast path finding solution for autonomous vehicles using the RRT algorithm. RRT is particularly well-suited for high-dimensional spaces and dynamic environments, making it an excellent choice for real-world autonomous driving scenarios.

How to Use

To use this project, follow the steps below:

1. Clone the repository to your local machine.

2. Ensure you have all the required dependencies installed. (Refer to the Dependencies section).

3. Prepare a map binary image where you want to find the path or use the one inside the maps directory. The map image should represent the environment with obstacles, and it should be in a common image format like PNG or JPEG and only contain pixel values as 255 (obstacle) and 0 (no-obstacle).

4. Run the main.py file with the required arguments in the following format:

python main.py start_x start_y goal_x goal_y map_image

• start_x and start_y are the x and y coordinates of the starting point, respectively.
• goal_x and goal_y are the x and y coordinates of the goal point, respectively.
• map_image is the path to the map image file.

5. The algorithm will compute the path, avoiding obstacles, and draw it on the map image.

6. The resulting map image with the path will be saved in the home directory.

Installation

To run this project, you need to have Python installed. Follow these steps to get started:

1. Clone the repository:

git clone https://github.com/TejasKalsait/Path_Finding_Autonomous_driving.git
cd Path_Finding_Autonomous_driving

2. Create a virtual environment (optional but recommended):

python -m venv venv

3. Activate the virtual environment:

• For Windows:

venv\Scripts\activate

• For macOS and Linux:

source venv/bin/activate

4. Install the required dependencies:

pip install -r requirements.txt

Dependencies

The project relies on the following Python libraries:

• OpenCV (cv2): For image processing and visualization.
• NumPy: For numerical operations.
• Python version > 3.8
• matplotlib

These dependencies are specified in the requirements.txt file and will be installed during the installation process.

Running the Project

To run the RRT path-finding algorithm and visualize the results, use the main.py script as explained in the How to Use section.

Results

Upon successful execution of the main.py script, the resulting map image will be saved in the same directory as the main.py file. The path will be drawn on the image, indicating the trajectory from the starting point to the goal point while avoiding obstacles.