Comparative Analysis of A* and RRT* Pathfinding Algorithms for Autonomous Drone Navigation in Various Environments
Colin Brennan, Nicholas Ciordas, Samhita Pokkunuri, Nickolas Regas, Adam Wahid, Will Wands, Shreya Srikanth · 2024
The increased utilization of drones necessitates safe and efficient traversal through various environments. This research compares the A*and Rapidly-exploring Random Tree*(RRT*) pathfinding algorithms across four maze types: single solution, high-density, low-density, and ladder, with each maze type representing a distinct real-world environment. Four metrics were tested: flight time, flight distance, compute time, and error. A*outperformed RRT*in compute time for all maze types. RRT*outperformed $A^{*}$ in flight time and distance for low-density, high-density, and ladder mazes, while performing similarly to A*in single solution mazes. These findings can guide algorithm selection for specific situational needs.