Lin, Kai Wun. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2011. / Includes bibliographical references (leaves 120-127). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.iii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Overview of the UAV Project --- p.1 / Chapter 1.2 --- Challenges on Vision System for UAV --- p.2 / Chapter 1.3 --- Contributions of this Work --- p.4 / Chapter 1.4 --- Organization of Thesis --- p.6 / Chapter 2 --- Image Sensor Selection and Evaluation --- p.8 / Chapter 2.1 --- Image Sensor Overview --- p.8 / Chapter 2.1.1 --- Comparing Sensor Features and Performance --- p.9 / Chapter 2.1.2 --- Rolling Shutter vsGlobal Shutter --- p.10 / Chapter 2.2 --- Sensor Evaluation through USB Peripheral --- p.11 / Chapter 2.2.1 --- Interfacing Image Sensor and USB Controller --- p.12 / Chapter 2.2.2 --- Image Sensor Configuration --- p.14 / Chapter 2.3 --- Image Data Transmitting and Processing --- p.17 / Chapter 2.3.1 --- Data Transfer Mode and Buffering on USB Controller --- p.18 / Chapter 2.3.2 --- Demosaicking of Bayer Image Data --- p.20 / Chapter 2.4 --- Splitting Images and Exposure Problem --- p.22 / Chapter 2.4.1 --- Buffer Overflow on USB Controller --- p.22 / Chapter 2.4.2 --- Image Luminance and Exposure Adjustment --- p.24 / Chapter 3 --- Embedded System for Vision Processing --- p.26 / Chapter 3.1 --- Overview of the Embedded System --- p.26 / Chapter 3.1.1 --- TI OMAP3530 Processor --- p.27 / Chapter 3.1.2 --- Gumstix Overo Fire Computer-on-Module --- p.27 / Chapter 3.2 --- Interfacing Camera Module to the Embedded System --- p.28 / Chapter 3.2.1 --- Image Signal Processing Subsystem --- p.29 / Chapter 3.2.2 --- Camera Module Adapting Board --- p.30 / Chapter 3.2.3 --- Image Sensor Driver and Program Development --- p.31 / Chapter 3.3 --- View-stabilizing Biaxial Camera Platform --- p.34 / Chapter 3.3.1 --- The New Camera System iv --- p.35 / Chapter 3.3.2 --- View-stabilizing Pan-tilt Platform --- p.41 / Chapter 3.4 --- Overall System Architecture and UAV Integration --- p.46 / Chapter 4 --- Target Tracking and Geo-locating --- p.50 / Chapter 4.1 --- Camera Calibration --- p.51 / Chapter 4.1.1 --- The Perspective Camera Model --- p.51 / Chapter 4.1.2 --- Camera Lens Distortions --- p.53 / Chapter 4.1.3 --- Calibration Toolbox and Results --- p.54 / Chapter 4.2 --- Selection of Object Features and Trackers --- p.56 / Chapter 4.2.1 --- Harris Corner Detection --- p.58 / Chapter 4.2.2 --- Color Histogram --- p.59 / Chapter 4.2.3 --- KLT and Mean-shift Tracker --- p.59 / Chapter 4.3 --- Target Auto-centering --- p.64 / Chapter 4.3.1 --- Formulation of the PID Controller --- p.65 / Chapter 4.3.2 --- Control Gain Settings and Tuning --- p.69 / Chapter 4.4 --- Geo-locating of Tracked Target --- p.69 / Chapter 4.4.1 --- Coordinate Frame Transformation --- p.70 / Chapter 4.4.2 --- Depth Estimation and Target Locating --- p.74 / Chapter 4.5 --- Results and Discussion --- p.77 / Chapter 5 --- Real-time Aerial Mosaic Building --- p.89 / Chapter 5.1 --- Motion Model Selection --- p.90 / Chapter 5.1.1 --- Planar Perspective Motion Model --- p.90 / Chapter 5.2 --- Feature-based Image Alignment --- p.91 / Chapter 5.2.1 --- Image Preprocessing --- p.91 / Chapter 5.2.2 --- Feature Extraction and Matching --- p.92 / Chapter 5.2.3 --- Image Alignment using RANSAC Algorithm --- p.94 / Chapter 5.3 --- Image Composition --- p.95 / Chapter 5.3.1 --- Image Blending with Distance Map --- p.96 / Chapter 5.3.2 --- Overall Stitching Process --- p.98 / Chapter 5.4 --- Mosaic Simulation using Google Earth --- p.99 / Chapter 5.5 --- Results and Discussion --- p.100 / Chapter 6 --- Conclusion and Further Work --- p.108 / Chapter A --- System Schematics --- p.111 / Chapter B --- Image Sensor Sensitivity --- p.118 / Bibliography --- p.120

The thesis deals with the design and implementation of an Adaptive Flight Control technique for Unmanned Aerial Vehicles (UAVs). The application of UAVs has been increasing exponentially in the last decade both in Military and Civilian fronts. These UAVs fly at very low speeds and Reynolds numbers, have nonlinear coupling, and tend to exhibit time varying characteristics. In addition, due to the variety of missions, they fly in uncertain environments exposing themselves to unpredictable external disturbances. The successful completion of the UAV missions is largely dependent on the accuracy of the control provided by the flight controllers. Thus there is a necessity for accurate and robust flight controllers. These controllers should be able to adapt to the changes in the dynamics due to internal and external changes. From the available literature, it is known that, one of the better suited adaptive controllers is the model based controller. The design and implementation of model based adaptive controller is discussed in the thesis. A critical issue in the design and application of model based control is the online identification of the UAV dynamics from the available sensors using the onboard processing capability. For this, proper instrumentation in terms of sensors and avionics for two platforms developed at UNSW@ADFA is discussed. Using the flight data from the remotely flown platforms, state space identification and fuzzy identification are developed to mimic the UAV dynamics. Real time validations using Hardware in Loop (HIL) simulations show that both the methods are feasible for control. A finer comparison showed that the accuracy of identification using fuzzy systems is better than the state space technique. The flight tests with real time online identification confirmed the feasibility of fuzzy identification for intelligent control. Hence two adaptive controllers based on the fuzzy identification are developed. The first adaptive controller is a hybrid indirect adaptive controller that utilises the model sensitivity in addition to output error for adaptation. The feedback of the model sensitivity function to adapt the parameters of the controller is shown to have beneficial effects, both in terms of convergence and accuracy. HIL simulations applied to the control of roll stabilised pitch autopilot for a typical UAV demonstrate the improvements compared to the direct adaptive controller. Next a novel fuzzy model based inversion controller is presented. The analytical approximate inversion proposed in this thesis does not increase the computational effort. The comparisons of this controller with other controller for a benchmark problem are presented using numerical simulations. The results bring out the superiority of this technique over other techniques. The extension of the analytical inversion based controller for multiple input multiple output problem is presented for the design of roll stabilised pitch autopilot for a UAV. The results of the HIL simulations are discussed for a typical UAV. Finally, flight test results for angle of attack control of one of the UAV platforms at UNSW@ADFA are presented. The flight test results show that the adaptive controller is capable of controlling the UAV suitably in a real environment, demonstrating its robustness characteristics.

Thesis (M.S.)--Brigham Young University. Dept. of Electrical and Computer Engineering, 2006. / Includes bibliographical references (p. 139-140).

Thesis (M.S.)--Oregon State University, 2010. / Printout. Includes bibliographical references (leaves 47-49). Also available on the World Wide Web.

Thesis (M.S. in Information Technology Management)--Naval Postgraduate School, June 2004. / Thesis advisor(s): Alex Bordetsky. Includes bibliographical references (p. 51-52). Also available online.

Thesis--School of Advanced Airpower Studies, Maxwell Air Force Base, Ala., 1994-95. / Title from title screen (viewed Oct. 28, 2003). "February 1996." Includes bibliographical references.

Thesis (M. S.)--Mechanical Engineering, Georgia Institute of Technology, 2010. / Committee Co-Chair: Johnson, Eric; Committee Co-Chair: Lipkin, Harvey; Committee Member: Sadegh, Nader. Part of the SMARTech Electronic Thesis and Dissertation Collection.

Thesis (M.S. in Aeronautical Engineering)--Naval Postgraduate School, March 2003. / Thesis advisor(s): Richard M. Howard, Vladimir N. Dobrokhodov. Includes bibliographical references (p. 99-100). Also available online.

M. Tech. Electronic Engineering. / Discusses the main objective of this research to develop a robust image-based algorithm to detect and determine the orientation of small helipad using shape descriptors and associated pre-processing techniques.

Unmanned Aerial Vehicles ("UAV") have been a part of aviation from the infancy of manned aviation; yet, have not reached their fullest potential as they are not integrated into the national airspace system ("NAS"). However, we are at the edge of technological breakthroughs to make integration a reality. Nevertheless, the regulatory construct necessary to provide safe integration of UAVs is unfinished. This thesis looks at necessary regulatory changes within the United States to allow for integration of the UAV into the NAS. I will first define the UAV and look at its historical roots. Then, I will review existing regulations and directives of manned flight that would apply to UAVs, as well as various rules specifically for UAVs that now exist. Through this examination, I will review the gaps and offer recommendations to fill regulatory holes in hopes to provide a useful contribution to the eventual integrated flight of UAVs.