Thesis (M.S.)--University of Nevada, Reno, 2008. / "December, 2008." Includes bibliographical references (leaves 46-47). Online version available on the World Wide Web.

Thesis (M.S.)--Brigham Young University. Dept. of Computer Science, 2008. / Includes bibliographical references (p. 101-107).

Thesis (M.S.)--Brigham Young University. Dept. of Mechanical Engineering, 2007. / Includes bibliographical references (p. 124-125).

Thesis (Ph. D.)--University of Florida, 1986. / Description based on print version record. Typescript. Vita. Includes bibliographical references (leaves 169-180).

Thesis (M.Sc.) - University of KwaZulu-Natal, Pietermaritzburg, 2010. / Full text also available online. Scroll down for electronic link.

Thesis (Ph. D.)--University of Wisconsin--Madison, 1975. / Typescript. Vita. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves 198-201).

Thesis (M.S.)--University of Wisconsin-Madison, 1984. / Typescript. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves 75-76).

Thesis (Ph. D.)--Lehigh University, 2006. / Includes vita. Includes bibliographical references (leaves 190-200).

There has been great interest in the use of small robotic helicopter vehicles over the last few years. Although there are regulatory issues involved in flying these that are still to be solved, they have the potential to provide a practical mobile aerial platform for a small fraction of the cost of a conventional manned helicopter. One potential class of applications for these is in searching for people, and this thesis explores a new generation of cameras which are suitable for this purpose. We propose HeatTrack, a novel algorithm to detect and track people in aerial imagery taken from a combined infrared/visible camera rig. A Local Binary Patterns (LBP) detector finds silhouettes in the infrared image which are used guide the search in the visible light image, and a Kalman filter combines information from both modalities in order to track a person more accurately than if only a single modality were available. We introduce a method for matching the thermal signature of a person to their corresponding patch in the visible modality, and show that this is more accurate than traditional homography-based matching. Furthermore, we propose a method for cancelling out camera motion which allows us to estimate a velocity for the person, and this helps in determining the location of a person in subsequent frames. HeatTrack demonstrates several advantages over tracking in the visible domain only, particularly in cases where the person shows up clearly in infrared. By narrowing down the search to the warmer parts of a scene, the detection of a person is faster than if the whole image were searched. The use of two imaging modalities instead of one makes the system more robust to occlusion; this, in combination with estimation of the velocity of a person, enables tracking even when information is lacking in either modality. To the best of our knowledge, this is the first published algorithm for tracking people in aerial imagery using a combined infrared/visible camera setup.

Motion planning is the field of computer science that aims at developing algorithmic techniques allowing the automatic computation of trajecto- ries for a mechanical system. The nature of such a system vary according to the fields of application. In computer animation it could be a humanoid avatar. In molecular biology it could be a protein. The field of application of this work being aerial robotics, the system is here a four-rotor UAV (Unmanned Aerial Vehicle) called quadrotor. The motion planning problem consists in computing a series of motions that brings the system from a given initial configuration to a desired final configuration without generating collisions with its environment, most of the time known in advance. Usual methods explore the system’s configuration space regardless of its dynamics. By construction the thrust force that allows a quadrotor to fly is tangential to its attitude which implies that not every motion can be performed. Furthermore, the magnitude of this thrust force and hence the linear acceleration of the center of mass are limited by the physical capabilities of the robot. For all these reasons, not only position and orientation must be planned, higher derivatives must be planned also if the motion is to be executed. When this is the case we talk of kinodynamic motion planning. A distinction is made between the local planner and the global planner. The former is in charge of producing a valid trajectory between two states of the system without necessarily taking collisions into account. The later is the overall algorithmic process that is in charge of solving the motion planning problem by exploring the state space of the system. It relies on multiple calls to the local planner. We present a local planner that interpolates two states consisting of an arbitrary number of degrees of freedom (dof) and their first and second derivatives. Given a set of bounds on the dof derivatives up to the fourth order (snap), it quickly produces a near-optimal minimum time trajectory that respects those bounds. In most of modern global motion planning algorithms, the exploration is guided by a distance function (or metric). The best choice is the cost-to-go, i.e. the cost associated to the local method. In the context of kinodynamic motion planning, it is the duration of the minimal-time trajectory. The problem in this case is that computing the cost-to-go is as hard (and thus as costly) as computing the optimal trajectory itself. We present a metric that is a good approximation of the cost-to-go but which computation is far less time consuming. The dominant paradigm nowadays is sampling-based motion planning. This class of algorithms relies on random sampling of the state space in order to quickly explore it. A common strategy is uniform sampling. It however appears that, in our context, it is a rather poor choice. Indeed, a great majority of uniformly sampled states cannot be interpolated. We present an incremental sampling strategy that significantly decreases the probability of this happening.