Preprint / Version 1

Combining current existing RRT path-generating methods to optimize simulated UAV flight

##article.authors##

  • Jesse Yan Westview High School

DOI:

https://doi.org/10.58445/rars.3566

Keywords:

computer science, Python

Abstract

Rapidly-exploring Random Tree (RRT) path-planning algorithms are widely used in the field of drones and other unmanned aerial vehicles (UAVs). RRT is a sampling-based algorithm that incrementally builds a tree through a state space by randomly selecting points and extending the tree toward them. However, as the state space is sampled randomly, the resulting paths are often inefficient and non-optimal, particularly in large or complex environments.


The purpose of this project was to improve upon existing RRT path-planning algorithms by combining multiple additional functionalities together in order to generate more optimized and feasible paths in 3D space within the same amount of time or less.

References

Karaman, S., & Frazzoli, E. (2013). Sampling-based algorithms for optimal motion planning. Massachusetts Institute of Technology, Computer Science and Artificial Intelligence Laboratory.

https://dspace.mit.edu/handle/1721.1/79884

MDPI. (2023). Rapidly exploring random tree algorithm definitions and applications. Agriculture, 13(2), 354.

https://www.mdpi.com/2077-0472/13/2/354

Open Source Robotics Foundation. (2020). ROS Noetic Ninjemys.

https://wiki.ros.org/noetic

Open Source Robotics Foundation. (2021). Gazebo 11 release.

https://classic.gazebosim.org/blog/gazebo11

PX4 Development Team. (2024). PX4 autopilot [Software].

https://github.com/PX4/PX4-Autopilot

Downloads

Posted

2026-01-03

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License

Copyright (c) 2026 Jesse Yan

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