Files
Download Full Text (543.9 MB)
Publication Date
Summer 2025
Digital Publisher
Digital Commons at St. Mary's University
Collection
McNair Scholars Symposium
Keywords
Unmanned Aerial Vehicles (UAVs), Atmospheric Thermals, Convolutional Neural Network (CNN), Energy Conservation, Vertical error, Flight Endurance, Autonomous Soaring, Thermal Detection, Avian Flight
Description
Birds utilize atmospheric thermals, which are vertical columns of warm air generated by the uneven heating of the Earth's surface, to conserve significant energy during flight, particularly over long migratory routes. This natural energy-saving strategy presents a compelling model for enhancing the endurance of Unmanned Aerial Vehicles (UAVs). This study aims to apply this biomimetic concept to UAVs to conserve onboard energy and thereby extend the drone's operational flight time. The primary objective of this research is to develop and validate a method for a UAV to autonomously detect and utilize atmospheric thermals using a machine learning model to predict the presence of these updrafts in real-time. A Convolutional Neural Network (CNN) model was developed to predict whether the UAV is in an atmospheric thermal based on vertical error derived from its XYZ coordinates. Furthermore, the model incorporates gyroscope data (roll, pitch, and yaw) to determine the UAV's position and orientation within the thermal. The quantitative analysis revealed that the CNN model accurately predicted the presence of atmospheric thermals by correlating vertical error with the UAV's spatial coordinates and gyroscope data. The successful implementation of the CNN demonstrates that it is feasible for a UAV to autonomously identify and orient itself within atmospheric thermals using standard onboard sensors. This step advances energy-efficient UAVs mimicking bird soaring, using natural updrafts to extend flight time, reducing battery dependence, and enabling long missions like monitoring, surveillance, and sensing.
Disciplines
Artificial Intelligence and Robotics | Aviation | Computer and Systems Architecture | Electro-Mechanical Systems | Maintenance Technology | Other Mechanical Engineering
Format
MOV
Medium
Video
Size or Duration
15 minutes 28 seconds
City
San Antonio, Texas
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
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