Date of Award
Spring 4-13-2026
Document Type
Dissertation
Publication Status
Version of Record
Submission Date
April 2026
Department
Computer and Electrical Engineering and Computer Science
College Granting Degree
College of Engineering and Computer Science
Department Granting Degree
Electrical Engineering and Computer Science
Degree Name
Doctor of Philosophy (PhD)
Thesis/Dissertation Advisor [Chair]
Imadeldin Mahgoub
Abstract
Modern world is being heavily impacted and increasingly being shaped by autonomous systems and connected devices. This necessity to utilize these devices has made it of utmost urgency to develop intelligent, adaptive, and secure drone operations. This dissertation investigates how nature-inspired algorithms (NIAs) can significantly enhance the path and motion planning mechanisms of Unmanned Aerial Vehicles (UAVs), specially in complex, dense, and adversarial environments.
The work in this dissertation is motivated by two critical challenges facing UAV systems. The first one is being the need for efficient and adaptive path planning in static environments. The second one is the motion planning in dynamic and hostile 3D environments for single and multi-UAV.
The methods that are utilized to approach these challenges in UAVs are inspired by biological processes and flock collective behaviors found in nature such as the foraging of ants, flocking of birds, and the leaping of frogs. This research explores how NIAs such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Shuffled Frog Leaping Algorithm (SFLA) are capable of providing practical solutions to these issues.
The dissertation is structured into three core contributions. First, we conducted a survey and proposed an expanded taxonomy of NIAs applied in Internet of Vehicles (IoV) environments. In addition, we identified overlooked domains such as clustering and provided a mapping of algorithms to appropriate use cases. Second, we proposed two hybrid algorithms: GBGA-PSO-ACO algorithm and enhanced GA-PSO-ACO algorithm called E-GPA. These two hybrid frameworks improve UAV path planning in static environments for a single UAV. Results of experiments demonstrate enhanced convergence, and shorter paths. Third, we proposed an adaptive SFLA model designed for motion planning in dynamic and adversarial scenarios in 3D environments that minimize detection risk and improve mission success.
By combining NIAs with domain-specific application, this research provides new tools and perspectives for designing resilient, intelligent, and adaptable UAV path and motion planning systems.
Recommended Citation
Alshammari, Thamer, "NATURE-INSPIRED ALGORITHMS FOR UAVS PATH AND MOTION PLANNING" (2026). Electronic Theses and Dissertations. 242.
https://digitalcommons.fau.edu/etd_general/242