This is my implementation of a path planning algorithm known as the "Rapidly Exploring Random Tree (RRT)" algorithm.
┣ 📂Images
┣ 📂Example1_3_Obstacles # 3 Obstacles Case
┃ ┗ 📜Grid.npy # Numpy grid of the image
┃ ┗ 📜Obstacles.png # Image with obstacles.
┃ ┗ 📜Result.png # Output screenshot
┃ ┗ 📜Squared_Obstacles_Image.png # Obstacle Image In Square Dimensions
┣ 📂Example2_4_Obstacles # 4 Obstacles Case
┃ ┗ 📜Grid.npy # Numpy grid of the image
┃ ┗ 📜Obstacles.png # Image with obstacles.
┃ ┗ 📜Result.png # Output screenshot
┃ ┗ 📜Squared_Obstacles_Image.png # Obstacle Image In Square Dimensions
┣ 📂Example3_5_Obstacles # 5 Obstacles Case
┃ ┗ 📜Grid.npy # Numpy grid of the image
┃ ┗ 📜Obstacles.png # Image with obstacles.
┃ ┗ 📜Result.png # Output screenshot
┃ ┗ 📜Squared_Obstacles_Image.png # Obstacle Image In Square Dimensions
┣ 📂Example4_6_Obstacles # 6 Obstacles Case
┃ ┗ 📜Grid.npy # Numpy grid of the image
┃ ┗ 📜Obstacles.png # Image with obstacles.
┃ ┗ 📜Result.png # Output screenshot
┃ ┗ 📜Squared_Obstacles_Image.png # Obstacle Image In Square Dimensions
┣ 📂Example5_8_Obstacles # 8 Obstacles Case
┃ ┗ 📜Grid.npy # Numpy grid of the image
┃ ┗ 📜Obstacles.png # Image with obstacles.
┃ ┗ 📜Result.png # Output screenshot
┃ ┗ 📜Squared_Obstacles_Image.png # Obstacle Image In Square Dimensions
┣ 📜RRT.png # RRT algorithm flowchart
┣ 📂Videos
┃ ┗ 📜Example1.mp4 # Video of the simulation for 3 obstacles case.
┃ ┗ 📜Example2.mp4 # Video of the simulation for 4 obstacles case.
┃ ┗ 📜Example3.mp4 # Video of the simulation for 5 obstacles case.
┃ ┗ 📜Example4.mp4 # Video of the simulation for 6 obstacles case.
┃ ┗ 📜Example5.mp4 # Video of the simulation for 8 obstacles case.
┣ 📜Generate_Final_Grid.py # Converts the image with obstacles into a numpy array/grid.
┣ 📜Generate_Obstacles.py # Generates an image with black rectangular obstacles.
┣ 📜LICENSE
┣ 📜README.md
┣ 📜RRT.py # RRT algorithm
┣ 📜Squaring_Figure.py # Converts the rectangular obstacles image to a square
┣ 📜start.py # Runs all three required python files in appropriate sequence.
Python
Numpy
Pillow
Matplotlib
- Just run the
start.py
file to start the simulation using the commandpython3 start.py
.