Author ORCID Identifier

https://orcid.org/0000-0001-8718-1390

Semester

Spring

Date of Graduation

2026

Document Type

Dissertation

Degree Type

PhD

College

Statler College of Engineering and Mineral Resources

Department

Mechanical and Aerospace Engineering

Committee Chair

Guilherme Pereira

Committee Member

Jason Gross

Committee Member

Yu Gu

Committee Member

Xi Yu

Committee Member

Alan McKendall

Abstract

Unmanned Aerial Vehicles (UAVs) have become essential for data acquisition in complex 3D environments. However, traditional Coverage Path Planning (CPP) methodologies often rely on a sequential pipeline that isolates viewpoint generation from flight path routing. This decoupling fails to account for the interdependence between viewpoint distribution and minimum flight paths, effectively restricting the search space and preventing the identification of a global optimum. This dissertation proposes a unified multi-objective framework for joint coverage and motion planning. The primary contribution of this work is the transition from isolated, sequential steps to a simultaneous optimization of the number and position of viewpoints and the trajectory between them using a customized Non-dominated Sorting Genetic Algorithm II (NSGA-II). By employing a variable-size genome representation, the framework concurrently evolves the number of required camera viewpoints and their visiting sequence. This allows the system to natively handle the inherent conflict between minimizing coverage error and minimizing operational costs, such as path distance/energy or trajectory energy. Experimental results demonstrate that this unified approach scales effectively to high-dimensional search spaces, identifying superior trade-off solutions that are often inaccessible to traditional decoupled planners. The scalability and robustness of the framework are validated through simulations of large-scale structures and real-world data. This work establishes a comprehensive foundation for autonomous 3D inspections, providing a diverse set of Pareto-optimal mission profiles that can be adapted to real-time operational constraints.

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