Date of Award
3-2025
Document Type
Thesis
Degree Name
Master of Science in Computer Science
Department
Department of Electrical and Computer Engineering
First Advisor
Daniel J. Broyles, PhD
Abstract
This thesis addresses the challenge of generating optimized UAV waypoints for complete coverage of complex 3D environments, utilizing graph-based computational techniques. The proposed framework replaces computationally intensive steps—triangulation and three-coloring—within the Vantage Waypoint Set Generation Algorithm (VWSGA) pipeline with Graph Neural Networks (GNNs). By learning structural patterns, the GNN achieves scalable and robust triangulation and node classification, enabling enhanced coverage planning in irregular geometries. A novel penalty mechanism ensures alignment with graph structure during adjacency prediction. Experimental results demonstrate the effectiveness of GNNs in balancing accuracy, computational efficiency, and adaptability, advancing UAV coverage optimization.
AFIT Designator
AFIT-ENG-MS-25-M-003
Recommended Citation
Tsfaty, Gal, "Graph Neural Network-Based UAV Coverage Planning for Robust and Efficient 3D Environments" (2025). Theses and Dissertations. 8216.
https://scholar.afit.edu/etd/8216
Comments
An embargo was observed for posting this work.
Distribution Statement A: Distribution Unlimited. Approved for public release. PA case number: 88ABW-2025-0192