ORCID

0009-0007-3274-4935

Keywords

Autonomous control, Path planning, Unmanned aerial vehicles, Real-time 3D model updating, Clustering

Abstract

This dissertation focuses on advancing control systems and path planning techniques for Unmanned Aerial Vehicles (UAVs), particularly in drone swarm operations and real-time digital twin (DT) integration. The research addresses key challenges in scalability, real-time responsiveness, and system adaptability to improve the efficiency and situational awareness of UAV systems. A comprehensive review of existing methods highlights the importance of swarm intelligence and nature-inspired algorithms. The study introduces a real-time DT that integrates multiple drone video feeds into a unified 3D environment. Additionally, the Camera-based Adaptive Line Formation and Dynamic Leader-Following Optimization (CALF-DLFO) strategy enhances drone swarm coverage and reduces update latency. A user study further shows that CALF-DLFO and parallel control interfaces significantly improve user situational awareness and system responsiveness. Furthermore, a novel Clustered Temporal Path Planning (CTPP) algorithm is proposed to optimize UAV swarm missions by integrating temporal data analysis, clustering, and trajectory optimization. Finally, a multi-camera simulation system is evaluated, revealing performance limitations as scene complexity and camera numbers increase. This research contributes to the development of more adaptive and efficient UAV swarm control systems, with implications for applications such as surveillance and environmental monitoring in dynamic, real-time environments.

Completion Date

2024

Semester

Fall

Committee Chair

Reiners, Dirk

Degree

Doctor of Philosophy (Ph.D.)

College

College of Engineering and Computer Science

Department

Computer Science

Format

PDF

Identifier

DP0029696

Document Type

Thesis

Campus Location

Orlando (Main) Campus

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