Thesis (Ph. D.)--West Virginia University, 2002. / Title from document title page. Document formatted into pages; contains xviii, 203 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 197-199).

Thesis (degree of Aeronautical and Astronautical Engineer)--Naval Postgraduate School, March 1992. / Thesis Advisor: Howard, Richard M. "March, 1992." Description based on title screen as viewed on March 10, 2009. Includes bibliographical references (p. 75-76). Also available in print.

Thesis (M.S.)--Brigham Young University. Dept. of Mechanical Engineering, 2007. / Includes bibliographical references (p. 89-91).

Thesis (Ph. D.)--Electrical and Computer Engineering, Georgia Institute of Technology, 2007. / Vachtsevanos, George, Committee Chair ; Heck, Bonnie, Committee Member ; Vela, Patricio, Committee Member ; Yezi, Anthony, Committee Member ; Johnson, Eric, Committee Member.

Thesis (M.S.)--West Virginia University, 2007. / Title from document title page. Document formatted into pages; contains viii, 53 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 48-53).

Dissertation (PhD)--University of Stellenbosch, 2008. / Bibliography. Also available via the Internet.

University of Macau / Faculty of Science and Technology / Department of Electromechanical Engineering

With large-scale implementation of drones having begun and numerous companies competing to be among the original players in the market, there lies a large potential for novel drone designs to be created and flown. These novel designs are the ones that were largely ignored in the previous century due to the physical constraints of having a crewed cockpit, but uncrewed aerial vehicles, or UAVs, have opened a floodgate of potential design spaces that may be explored which were previously impossible. The hybrid vertical take-off and landing, VTOL, UAV is one aircraft that presents a potential solution to the classic trade-off of the traditional VTOL's range and endurance limitations versus the fixed wing's required infrastructure. An aircraft known as the Flite Test Spear is used to examine fuzzy logic control and is one such hybrid VTOL that uses large control surfaces and throttle control to maneuver itself for take-offs and landings in a tail-sitting orientation before transitioning to forward, fixed-wing flight. Current flight controllers used in operation on hybrid VTOL aircraft rely on a control law state machine where given a pre-identified aircraft state, the controller enters a transitioning maneuver that takes the aircraft from a VTOL to fixed wing flight regime, or vice versa. Each flight regime is operated by a PID controller with different gains and control input realizations. A modification to this principle is first examined by using fuzzy logic PID gain modification for increased response time and reduced overshoot. Reducing overshoot is of particular interest in this case as, on an aircraft such as this, it has the potential for entering undesirable and unrecoverable states, especially during its transition. Secondly, a mixing of the two flight controllers using a fuzzy logic system was implemented to combine the two controllers' outputs and potentially smooth this transition for safer, more efficient flight. The fuzzy logic controlled mixing of the two VTOL and fixed wing controllers was not proven to provide a more desirable response within the scope of the simulation, however, performed equally as well to that of the current state machine response. The gain scheduling fuzzy systems implemented in the controller have shown to decrease overshoot of the aircraft when given commands to different states, but respond slower than their conventional counterparts. Promise in the reduction of the overshoot error and their lightweight construction leads to the conclusion that implementation on a prototype aircraft would be worthwhile for further testing. / Master of Science / Drones play a larger role in our daily lives than they ever have before. With the work being performed by Google Wing to begin last-mile delivery of household consumer goods to the success of companies like Zipline and Swoop Aero in delivering emergency medical supplies to remote locations in low-resource areas, drones are being increasingly deployed, and their use will continue to grow if current trends continue. Like all burgeoning markets, competition is driving innovation to seek new market sectors and pushing the stagnant players out. In order to distinguish themselves, many companies have been creating their own drones for whichever sector of the drone market they wish to compete in. Whether that be consumer good delivery or aerial imagery, these challenges create numerous problems that some drones handle better than others. This has led to a large investment into the research and development of drones that best suit the needs of whatever mission has to be performed. Drones that act similarly to conventional aircraft such as planes or helicopters may serve as the best solution for a variety of problem statements, but because the need for a pilot in the cockpit is no longer necessary, solutions that were previously impossible to implement due to human factors can now be explored fully. With such an explosion in the design space of drones, the control algorithms needed to operate them must follow suit. This paper attempts to explain an alternative to one of the most common controllers in use today known as the PID. A modification to this controller using a technique known as fuzzy logic is made to increase the performance of experimental drone types without the need of an extensive, costly research and development phase that is necessary for crewed aircraft.

This work discusses the algorithms and implementation of a multi-robot system for locating radioactive sources. The estimation algorithm presented in this work is able to fuse measurements collected by γ-ray spectrometers carried by an unmanned aerial and unmanned ground vehicle into a single consistent estimate of the probability distribution over the position of a point source in an environment. By constructing a set of hypotheses on the position of the point source, this method converts a non-linear problem into many independent linear ones. Since the underlying model is probabilistic, candidate paths may be evaluated by their expected reduction in uncertainty, allowing the algorithm to select good paths for vehicles to take. An initial hardware test conducted at Savannah River National Laboratory served as a proof of concept and demonstrated that the algorithm successfully locates a radioactive source in the environment, and moves the vehicle to that location. This approach also demonstrated the capability to utilize radiation data collected from an unmanned aerial vehicle to aid the ground vehicle’s exploration. Subsequent numerical experiments characterized the performance of several reward functions and different exploration algorithms in scenarios covering a range of source strengths and region sizes. These experiments demonstrated the improved performance of planning-based algorithms over the myopic method initially tested in the hardware experiments. / Doctor of Philosophy / This work discusses the use of unmanned aerial and ground vehicles to autonomously locate radioactive materials. Using radiation detectors onboard each vehicle, they are commanded to search the environment using a method that incorporates measurements as they are collected. A mathematical model allows measurements taken from different vehicles in different positions to be combined together. This approach decreases the time required to locate sources by using previously collected measurements to improve the quality of later measurements. This approach also provides a best estimate of the location of a source as data is collected. This algorithm was tested in an experiment conducted at Savannah River National Laboratory. Further numerical experiments were conducted testing different reward functions and exploration algorithms.

L'objectif de cette thèse est d'optimiser la couverture visuelle d'une zone vaste et complexe de façon à ce que ses images puissent composer une mosaïque à partir d'un drone.Pour trouver les meilleurs points de passage, deux méthodes ont été étudiées : l'optimisation par essaims particulaires (PSO) et les algorithmes génétiques (GA).Notre étude a prouvé que le GA est la méthode offrant de meilleures performances en raison de ses performances et de sa capacité d'adaptation.Après avoir réalisé des expériences pour comparer les algorithmes, une hybridation de GA et PSO a été réalisée et étudiée.La méthode proposée peut être appliquée sur de grandes surfaces de formes irrégulières, comme les terrains agricoles, et fournit un nombre réduit de points de passage qui doivent être survolés par un véhicule aérien de type drone (UAV).Des essais ont été réalisés pour simuler le vol d'un drone dans un environnement intérieur, les images générées pendant la simulation sont utilisées pour représenter une image de la totalité de l'environnement sous la forme d'une mosaïque.La méthode proposée est également appliquée dans de vastes zones extérieures. Des images satellitaires sont utilisées pour visualiser la couverture du trajet qui a été planifié.Les expériences valident l'efficacité de la méthode proposée pour trouver le nombre et la position des points de passage. / The goal of this paper is to optimize the coverage of a vast and complexarea such that its mosaic image can be created. To find the best waypoints, twomethods have been investigated: Particle Swarm Optimization (PSO) and GeneticAlgorithms (GA). Our investigation proved that GA is a better method due toits performance and adaptability. After having performed experiments to compare the algorithms, a hybridization of GA and PSO is investigated.The proposed method can be applied on large areas with irregular shapes, such as agricultural fields, and it provides a minimized number of waypoints that must be flown over by the Unmanned Aerial Vehicle (UAV). The experiments were made to simulate the flight of the UAV in an indoor environment, and the images generated during the simulated flight have been used to show the final mosaic. The proposed method is also applied in the vast outdoor area using satellite images to visualize the final result of the coverage path planning. The experiments validate the efficiency of the proposed method for finding the number and the poses of the waypoints. The solution proposed to approach the problem of coverage path planning is rather different than the stateof the art by dividing the Coverage Path Planning on independent sub-problems to optimize and then using GA and later on GAPSO.