Path Planning for Aerial Robots

This project focuses on autonomous navigation for Unmanned Aerial Vehicles (UAVs) using sampling-based algorithms. I implemented RRT (Rapidly-exploring Random Tree) and RRT* from scratch to solve pathfinding challenges in both 2D and 3D environments.

Key Features:

• Algorithm Implementation: Built custom Python implementations of RRT and RRT* to benchmark exploration efficiency and path optimality.

• 3D Drone Navigation: Extended the 2D logic to a 3D simulation using Matplotlib, allowing for complex aerial maneuvers.

• Dynamic Obstacle Avoidance: Engineered a replanning module that allows the drone to detect and navigate around moving hazards in real-time, ensuring mission safety in dynamic environments.

• Visualization: Developed lightweight, real-time visualizations using Pygame (for 2D) to demonstrate node exploration and path smoothing.