Date of Graduation

5-2024

Document Type

Thesis

Degree Name

Bachelor of Science in Computer Engineering

Degree Level

Undergraduate

Department

Computer Science and Computer Engineering

Advisor/Mentor

Nelson, Alexander

Committee Member/Reader

Andrews, David

Committee Member/Second Reader

Farnell, Chris

Abstract

The management of weeds in crop fields is a continuous agricultural problem. The use of herbicides is the most common solution, but herbicidal resistance decreases effectiveness, and the use of herbicides has been found to have severe adverse effects on human health and the environment. The use of autonomous drone systems for weed elimination is an emerging solution, but challenges in GPS-based localization and navigation can impact the effectiveness of these systems. The goal of this thesis is to evaluate techniques for minimizing localization errors of drones as they attempt to eliminate weeds. A simulation environment was created to model garden traversal and weed elimination both with and without travel error.
Attempts were then made to reduce this error using simulated GPS localization, and further reductions on localization errors were made using an extended Kalman filter (EKF). Results showed that travel errors can lead to significant reductions in a drone system’s ability to traverse a space and eliminate weeds. The use of an EKF significantly reduces localization error when traveling along a path. This implies that integrating an EKF into autonomous systems can enhance reliability and reduce overall error, leading to higher weed elimination rates. Future work could include real-world testing drone testing and exploration into more advanced GPS technologies.

Keywords

agriculture, drones, simulations

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