Abstract

This research applies techniques from the field of optimization to spatial positioning and energy management in Unmanned Aerial Vehicles (UAVs). Two specific areas are treated: optimization of UAV view plans for 3D modeling of infrastructure, and trajectory optimization of solar powered high-altitude long-endurance (HALE) UAVs. Structure-from-Motion (SfM) is a computer vision technique for creating 3D models from 2D images. View planning is the process of planning image sets that will effectively model a given scene. First, a genetic algorithm based view planning approach is demonstrated. A novel terrain simulation environment is developed, and the algorithm is tested at multiple sites of interest. The genetic algorithm is compared quantitatively to traditional flights, and is found to yield terrain models with up to 43% greater accuracy than a standard grid flight pattern. Next a greedy heuristic planner is developed, and used to combine anomaly detection with automatic on-board 3D view planning for long linear infrastructure objects such as canals and pipelines. The proposed method is shown in simulation to decrease total flight time by up to 55%, while reducing the amount of image data to be processed by 89% and maintaining 3D model accuracy at areas of interest. The planning algorithm is then extended to select images of ground targets from an existing data set. The algorithm is tested on five different targets, and is shown to reduce processing time for target models by up to a factor of 50 with little decrease in accuracy. The second portion of the research demonstrates the use of nonlinear dynamic optimization to calculate energy optimal trajectories for a high-altitude, solar-powered Unmanned Aerial Vehicle (UAV). The objective is to maximize the total energy in the system while staying within a 3 km mission radius and meeting other system constraints. Solar energy capture is modeled using the vehicle orientation and solar position, and energy is stored both in batteries and in potential energy through elevation gain. Energy capture is maximized by optimally adjusting the angle of the aircraft surface relative to the sun. The UAV flight and energy system dynamics are optimized over a 24-hour period at an eight-second time resolution using Nonlinear Model Predictive Control (NMPC). Results of the simulated flights are presented for all four seasons, showing 8.2% increase in end-of-day battery energy for the most limiting flight condition of the wintersolstice.

Degree

PhD

College and Department

Ira A. Fulton College of Engineering and Technology; Chemical Engineering

Rights

http://lib.byu.edu/about/copyright/