Development of Quadcopter for Autonomous Navigation
Persistent URL
Author(s)
Jones, Simon
Date Issued
April 22, 2024
Abstract
Autonomous navigation is necessary for a robotic system to interact with its surroundings in a real world environment, and it is necessary to realize technologies such as fully autonomous unmanned aerial vehicles (UAVs) and land vehicles. Reinforcement Learning (RL) has proven to be a novel and effective method for autonomous navigation and control, as it is capable of optimizing a method of converting its instantaneous state to an action at a point in time. Here we use a Deep Deterministic Policy Gradient (DDPG) RL algorithm to train the COEX Clover quadcopter system to perform autonomous navigation. With the advent of solid state lasers, miniaturized optical ranging systems have become ubiquitous for aerial robotics because of their low power and accuracy. By equipping the Clover with ten Time of Flight (ToF) ranging sensors, we supply continuous spatial data in combination with inertial data to determine the quadcopter’s state, which is then mapped to its control output. Our results suggest that, while the DDPG algorithm is capable of training a quadcopter system for autonomous navigation, its computation-heavy nature leads to delayed convergence, and relying on discretized algorithms may permit more rapid convergence across episodes.
Major
Physics
Computer Science
Honors
Computer Science, 2024
Physics, 2024
First Reader(s)
Jumadinova, Janyl A.
Willey, Daniel R.
Department
Physics
Computer and Information Science
Type of Publication
Senior Project Paper
File(s)![Thumbnail Image]()
Name
SeniorThesis-2.3.1.pdf
Size
5.88 MB
Format
Adobe PDF
Checksum (MD5)
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