TurtleBot Maze Navigation using A* planning (3rd place)

Overview

This project focused on implementing the A* path planning algorithm for a TurtleBot Waffle robot. The goal was to develop an efficient and robust path planning system capable of navigating the robot through various environments, optimizing its route to a designated target. This project was part of a course competition where we achieved 3rd place, highlighting the effectiveness of our approach.

Project Implementation

We built a comprehensive path planning solution utilizing the A* algorithm, known for its balance between performance and accuracy in finding the shortest path in a grid-based search space. The implementation involved several key components:

1. Map Representation: The environment was represented as a grid, where each cell indicated whether it was navigable or an obstacle. This grid-based representation was crucial for the A* algorithm to evaluate potential paths.

2. Heuristic Design: We employed a heuristic function to estimate the cost from the current position to the target. This function played a pivotal role in guiding the A* algorithm efficiently towards the goal, minimizing the computational load while ensuring optimal pathfinding.

3. TurtleBot Waffle Integration: The A* algorithm was integrated into the TurtleBot Waffle's ROS framework, allowing real-time path planning and execution. This integration ensured seamless communication between the algorithm and the robot's hardware, facilitating accurate and responsive navigation.
   
   

Competition Performance

In the course-wide competition, our A* path planning solution was tested against various scenarios, including maze-like environments and open spaces with scattered obstacles. Our approach demonstrated a strong ability to navigate efficiently, earning us 3rd place. This achievement was a testament to the robustness of our algorithm and the effectiveness of our implementation.

Project Outcomes and Learnings

Through this project, we gained significant insights into path planning and robotics. Key outcomes included:

• Enhanced Understanding of A Algorithm*: By implementing the A* algorithm, we deepened our understanding of its mechanics, strengths, and limitations in real-world robotics applications.
  
  

• Hands-on Experience with TurtleBot Waffle: Working with the TurtleBot Waffle robot provided practical experience in integrating software algorithms with robotic hardware, an essential skill in robotics development.
  
  

• Problem-Solving and Optimization: The competition setting required us to continually refine our approach, optimizing the algorithm's performance to handle diverse and challenging scenarios effectively.
  
  

• Collaboration and Teamwork: The project fostered a collaborative environment where we worked closely to brainstorm, develop, and test our solution, enhancing our teamwork and project management skills.
  
  

This project not only showcased our ability to apply theoretical concepts in a practical context but also prepared us for future challenges in robotics and autonomous systems.