Design and Implementation of a Variable-Sweep Unmanned Aerial Vehicle for Ground Target Acquisition and Terminal Guidance Using Computer Vision Modeling

Abstract

Over the past several centuries, technology on the battlefield has progressed rapidly, with new capabilities able to eliminate targets from ranges never thought possible. However, as we move further into the Artificial Intelligence Era, a new type of weapon is emerging- unmanned aerial vehicle (UAV) drones. These lightweight infantry-based machines are able to target and strike with exceedingly high precision, often without fear of loss due to their low cost and maintenance. However, this area of development has been lacking, with more conventional weapons taking development time and research. This paper presents the design, implementation, and evaluation of a UAV drone capable of autonomously tracking and targeting ground locations. We explore how varied computer vision You Only Look Once (YOLO) models and Proportional Integral Derivative (PID) control values, used in conjunction with a variable-sweep wing UAV, affects targeting precision and accuracy when tracking moving ground targets under simulated aerodynamic conditions. We also explore which parameters result in improved tracking in comparison to human piloting. Testing is conducted through tracking tests aimed for single axis correction.