Path Planning using A-star Algorithm

Introduction to the Project

In this project, the A-star motion planning algorithm was used on a point robot and rigid robot to navigate in a configuration space consisting of static obstacles.

Features

• Interactive GUI: A modern dark-themed graphical interface built with tkinter
• Click-to-Set Points: Left-click to set start point, right-click to set goal point
• Real-time Visualization: Watch the A* algorithm explore the space and find the optimal path
• Adjustable Parameters:
  • Robot Radius
  • Clearance
  • Step Size
  • Animation Speed
• Multiple Obstacle Types: Circle, ellipse, triangles, rhombus, rectangle, and polygon obstacles

Results

Interactive GUI showing the obstacle map:

A* algorithm visualization with explored region (blue) and optimal path (yellow/white):

Software Required

To run the .py files, use Python 3. The following libraries are required:

• numpy
• OpenCV (cv2)
• tkinter (included with Python)
• heapq (included with Python)

Instructions for running the code

GUI Mode (Recommended)

To run the interactive GUI visualizer:

cd Code
python gui_main.py

How to use the GUI:

1. Adjust robot radius, clearance, and step size using the sliders
2. Left-click on the canvas to set the start point (green)
3. Right-click on the canvas to set the goal point (orange)
4. Click "RUN" to start the A* path planning algorithm
5. Watch the exploration (blue) and optimal path (yellow) animate in real-time
6. Click "RESET" to clear and try again

Command Line Mode

To run the original command-line version:

cd Code
python astar.py

You will be prompted to enter:

• Start node coordinates (x, y)
• Goal node coordinates (x, y)
• Robot radius
• Clearance
• Step size

Project Structure

Code/
├── astar.py        # Command-line A* implementation
├── utils.py        # AStar class with path planning logic
├── gui_main.py     # Main GUI application entry point
├── gui_canvas.py   # Canvas rendering for visualization
└── gui_config.py   # GUI configuration and color scheme