Research in the areas of target detection, tracking, and geo-location is most important for enabling an unmanned aerial vehicle (UAV) platform to autonomously execute a mission or task without the need for a pilot or operator. Small-class UAVs and video camera sensors complemented with "soft sensors" realized only in software as a combination of a priori knowledge and sensor measurements are called upon to replace the cumbersome precision sensors on-board a large class UAV. The objective of this research is to develop a geo-location solution for use on-board multiple UAVs with mounted video camera sensors only to accurately geo-locate and track a target. This research introduces an estimation solution that combines the power of the particle filter with the utility of the video sensor as a general solution for passive target geo-location on-board multiple UAVs. The particle filter is taken advantage of, with its ability to use all of the available information about the system model, system uncertainty, and the sensor uncertainty to approximate the statistical likelihood of the target state. The geo-location particle filter is tested online and in real-time in a simulation environment involving multiple UAVs with video cameras and a maneuvering ground vehicle as a target. Simulation results show the geo-location particle filter estimates the target location with a high accuracy, the addition of UAVs or particles to the system improves the location estimation accuracy with minimal addition of processing time, and UAV control and trajectory generation algorithms restrict each UAV to a desired range to minimize error.

One of the basic problems which have to be solved by Unmanned Automated Vehicles (UAV) involves the computation of a motion plan that would enable the system to reach a target given a set of initial conditions in presence of uncertainties on the vehicle dynamics and in the environment. Recent research efforts in this area have relied on deterministic models. To address the problem of inevitable uncertainties, a low-level control layer is typically used to ensure proper robust trajectory tracking. Such decision-tracking algorithms correct model disturbances a posteriori, while the whole movement planning is done in a purely deterministic fashion. We argue that the decision making process that takes place during movement planning, as performed by experienced human pilots, is not a purely deterministic operation, but is heavily influenced by the presence of uncertainties and reflects a risk-management policy. This research aims at addressing these uncertainties and developing an optimal control strategy that would account for the presence of system uncertainties. The underlying description of UAV trajectories will be based on a modeling language, the Maneuver Automaton, that takes into full account the vehicle dynamics, and hence guarantees flyable and trackable paths and results in a discretized solution space. Two optimal control problems, a nominal problem omitting uncertainties and a robust problem addressing the presence of uncertainties, will be defined and compared throughout this work. The incorporation of uncertainties, will ensure that the generated motion planning policies will maximize the probability to meet mission goals, weighing risks against performance.

Cirrus clouds cover between 20% - 50% of the globe and are an essential component in the climate. The improved understanding of ice cloud microphysical properties is contingent on acquiring and analyzing in-situ and remote sensing data from cirrus clouds. In ??u observations of microphysical properties of ice and mixed-phase clouds using the mini-Video Ice Particle Sizer (mini-VIPS) aboard robotic unmanned aerial vehicles (UAVs) provide a promising and powerful platform for obtaining valuable data in a cost-effective, safe, and long-term manner. The purpose of this study is to better understand cirrus microphysical properties by analyzing the effectiveness of the mini-VIPS/UAV in-situ platform. The specific goals include: (1) To validate the mini-VIPS performance by comparing the mini-VIPS data retrieved during an Artic UAV mission with data retrieved from the millimeterwavelength cloud radar (MMCR) at the Barrow ARM/CART site. (2) To analyze mini-VIPS data to survey the properties of high latitude mixedphase clouds The intercomparison between in-situ and remote sensing measurements was carried out by comparing reflectivity values calculated from in-situ measurements with observations from the MMCR facility. Good agreement between observations and measurements is obtained during the time frame where the sampled volume was saturated with respect to ice. We also have 1 2 shown that the degree of closure between calculated and observed reflectivity strongly correlates with the assumption of ice crystal geometry observed in the mini-VIPS images. The good correlation increases the confidence in mini-VIPS and MMCR measurements. Finally, the size distribution and ice crystal geometry obtained from the data analysis is consistent with published literature for similar conditions of temperature and ice supersaturation.

In recent years, Unmanned Aerial Vehicles (UAVs) have been used extensively in military conflict situations to execute intelligence, surveillance and reconnaissance missions. However, most of the current UAV platforms have limited collaborative capabilities, and consequently they must be controlled individually by operators on the ground. The purpose of the research presented in this thesis is to derive algorithms that can enable multiple UAVs to reason about the movements of multiple ground targets and autonomously coordinate their efforts in real-time to ensure that the targets do not escape. By improving the autonomy of multivehicle systems, the workload placed on the command and control operators is reduced significantly. To derive effective adversarial control algorithms, the adversarial scenario is modeled as a multiplayer differential game. However, due to the inherent computational complexity of multiplayer differential games, three less computationally demanding differential pursuit-evasion game-based algorithms are presented. The purpose of the algorithms is to quickly derive interception strategies for a team of autonomous vehicles. The algorithms are applicable to scenarios with different base assumptions, that is, the three algorithms are meant to complement one another by addressing different types of adversarial problems.

Recently there has been a tremendous growth of research emphasizing control of unmanned aerial vehicles (UAVs) either in isolation or in teams. As a matter of fact, UAVs increasingly find their way to applications, especially in military and law enforcement (e.g., reconnaissance, remote delivery of urgent equipment/material, resource assessment, environmental monitoring, battlefield monitoring, ordnance delivery, etc.). This trend will continue in the future, as UAVs are poised to replace the human-in-the-loop during dangerous missions. Civilian applications of UAVs are also envisioned such as crop dusting, geological surveying, search and rescue operations, etc. In this thesis we propose a new online multiresolution path planning algorithm for a small UAV with limited on-board computational resources. The proposed approach assumes that the UAV has detailed information of the environment and the obstacles only in its vicinity. Information about far-away obstacles is also available, albeit less accurately. The proposed algorithm uses the fast lifting wavelet transform (FLWT) to get a multiresolution cell decomposition of the environment, whose dimension is commensurate to the on-board computational resources. A topological graph representation of the multiresolution cell decomposition is constructed efficiently, directly from the approximation and detail wavelet coefficients. Dynamic path planning is sequentially executed for an optimal path using the A* algorithm over the resulting graph. The proposed path planning algorithm is implemented on-line on a small autopilot. Comparisons with the standard D*-lite algorithm are also presented. We also investigate the problem of generating a smooth, planar reference path from a discrete optimal path. Upon the optimal path being represented as a sequence of cells in square geometry, we derive a smooth B-spline path that is constrained inside a channel that is induced by the geometry of the cells. To this end, a constrained optimization problem is formulated by setting up geometric linear constraints as well as boundary conditions. Subsequently, we construct B-spline path templates by solving a set of distinct optimization problems. For an application to the UAV motion planning, the path templates are incorporated to replace parts of the entire path by the smooth B-spline paths. Each path segment is stitched together while preserving continuity to obtain a final smooth reference path to be used for path following control. The path following control for a small fixed-wing UAV to track the prescribed smooth reference path is also addressed. Assuming the UAV is equipped with an autopilot for low level control, we adopt a kinematic error model with respect to the moving Serret-Frenet frame attached to a path for tracking controller design. A kinematic path following control law that commands heading rate is presented. Backstepping is applied to derive the roll angle command by taking into account the approximate closed-loop roll dynamics. A parameter adaptation technique is employed to account for the inaccurate time constant of the closed-loop roll dynamics during actual implementation. Finally, we implement the proposed hierarchical path control of a small UAV on the actual hardware platform, which is based on an 1/5 scale R/C model airframe (Decathlon) and the autopilot hardware and software. Based on the hardware-in-the-loop (HIL) simulation environment, the proposed hierarchical path control algorithm has been validated through the on-line, real-time implementation on a small micro-controller. By a seamless integration of the control algorithms for path planning, path smoothing, and path following, it has been demonstrated that the UAV equipped with a small autopilot having limited computational resources manages to accomplish the path control objective to reach the goal while avoiding obstacles with minimal human intervention.

This thesis presents an integrated framework for online obstacle avoidance of rotary-wing unmanned aerial vehicles (UAVs), which can provide UAVs an obstacle field navigation capability in a partially or completely unknown obstacle-rich environment. The framework is composed of a LIDAR interface, a local obstacle grid generation, a receding horizon (RH) trajectory optimizer, a global shortest path search algorithm, and a climb rate limit detection logic. The key feature of the framework is the use of an optimization-based trajectory generation in which the obstacle avoidance problem is formulated as a nonlinear trajectory optimization problem with state and input constraints over the finite range of the sensor. This local trajectory optimization is combined with a global path search algorithm which provides a useful initial guess to the nonlinear optimization solver. Optimization is the natural process of finding the best trajectory that is dynamically feasible, safe within the vehicle's flight envelope, and collision-free at the same time. The optimal trajectory is continuously updated in real time by the numerical optimization solver, Nonlinear Trajectory Generation (NTG), which is a direct solver based on the spline approximation of trajectory for dynamically flat systems. In fact, the overall approach of this thesis to finding the optimal trajectory is similar to the model predictive control (MPC) or the receding horizon control (RHC), except that this thesis followed a two-layer design; thus, the optimal solution works as a guidance command to be followed by the controller of the vehicle. The framework is implemented in a real-time simulation environment, the Georgia Tech UAV Simulation Tool (GUST), and integrated in the onboard software of the rotary-wing UAV test-bed at Georgia Tech. Initially, the 2D vertical avoidance capability of real obstacles was tested in flight. Then the flight test evaluations were extended to the benchmark tests for 3D avoidance capability over the virtual obstacles, and finally it was demonstrated on real obstacles located at the McKenna MOUT site in Fort Benning, Georgia. Simulations and flight test evaluations demonstrate the feasibility of the developed framework for UAV applications involving low-altitude flight in an urban area.

Mining is one of the main economic sectors in South Africa. Mining activity contains hazards such collapsing of structures, presence of dangerous gases, accidental explosions and fires. Even though most of these hazards are identified and minimized sometimes accidents occur. These accidents lead to human injuries, direct fatalities and fatalities resulting from delays in victims getting medical attention as a result of delays in search and rescue missions. The rescue missions in underground mines present challenges where rescuers are not sure which locations are victims in, what the area conditions like in the rescue path. A quad rotor unmanned aerial vehicle (UAV) for search and rescue missions is presented. The UAV is controlled from a remote location over Wi-Fi. The communication allows data relay to the ground control station. The communication system is tested on the university’s Wi-Fi network. The UAV also contains a vision system that contains a human detection algorithm to give an indication of human presence to rescuers. The human detection system is based on Haar- Cascade classifiers. The model developed was found to have a false alarm rate of 5×10-3% after training. The model was further tested on streaming data and the overall average positive human detection was found to be 97 %. In the same tests overall false average detection was found to be 2.5 %. The video feed is streamed from the UAV to the ground station (GS) and the flight control instructions are sent to the UAV from the GS via Wi-Fi. / Thesis (M.Sc.Eng)-University of KwaZulu-Natal, Durban, 2013.

Thesis (M.App.Sc.) - Carleton University, 2005. / Includes bibliographical references (p. 172-178). Also available in electronic format on the Internet.

Thesis (M.S. in Electrical Engineering)--Naval Postgraduate School, December 2004. / Thesis advisor(s): David C. Jenn. Includes bibliographical references (p. 93-94). Also available online.

Thesis (MScEng (Electrical and Electronic Engineering))--University of Stellenbosch, 2011. / ENGLISH ABSTRACT: The development of a control allocation system for use as part of a fault-tolerant control (FTC) system in unmanned aerial vehicles (UAVs) is presented. This system plays a vital role in minimising the possibility that a fault will necessitate the reconfiguration of the control, guidance or navigation systems of the aircraft by minimising the difference between the desired and achievable aircraft performance parameters. This is achieved by optimising the allocation of control effort commanded by the virtual actuators to the physical actuators present on the aircraft. A simple general six degree of freedom aircraft model is presented that contains all of the relevant terms needed to find the trim biases of the aircraft actuators and evaluate the performance of the virtual actuators. This model was used to develop a control allocation formulation that optimises the performance of the virtual actuators of the aircraft while minimising adverse effects and avoiding actuator saturation. The resulting problem formulation was formulated as a multi-objective optimisation problem which was solved using the sequential quadratic programming method. The control allocation system was practically implemented and tested. A number of failure categories of varying severity were defined and two aircraft with different levels of actuator redundancy were used to test the system. The control allocation algorithm was evaluated for each failure category, aircraft test case and for a number of differing control allocation system configurations. A number of enhancements were then made to the control allocation system which included adding frequency-based allocation and adapting the algorithm for an unconventional ducted-fan UAV. The control allocation system is shown to be applicable to a number of different conventional aircraft configurations with no alterations as well as being applicable to unconventional aircraft with minor alterations. The control allocation system is shown to be capable of handling both single and multiple actuator failures and the importance of actuator redundancy is highlighted as a factor that influences the effectiveness of control allocation. The control allocation system can be effectively used as part of a FTC system or as a tool that can be used to investigate control allocation and aircraft redundancy. / AFRIKAANSE OPSOMMING: Die ontwikkeling van ’n beheertoekenning sisteem vir gebruik as deel van ’n fout verdraagsame beheersisteem in onbemande lugvaartuie word voorgelê. Hierdie sisteem speel ’n essensiële rol in die vermindering van die moontlikheid dat ’n fout die herkonfigurasie van die beheer, bestuur of navigasiesisteme van die vaartuig tot gevolg sal hê, deur die verskil te verminder tussen die verlangde en bereikbare werkverrigtingsraamwerk van die vaartuig. Dit word bereik deur die optimisering van die toekenning van beheerpoging aangevoer deur die virtuele aktueerders na die fisiese aktueerders teenwoordig op die vaartuig. ’n Eenvoudige algemene ses grade van vryheid lugvaartuig model word voorgestel wat al die relevante terme bevat wat benodig word om die onewewigtigheid verstelling van die vaartuig se aktueerders te vind en die werksverrigting van die virtuele aktueerders te evalueer. Hierdie model is gebruik om ’n beheer toekenning formulering te ontwikkel wat die werkverrigting van die virtuele aktueerders van die vaartuig optimiseer terwyl nadelige gevolge verminder word asook aktueerder versadiging vermy word. Die gevolglike probleem formulering is omskryf as ’n multi-doel optimiserings probleem wat opgelos is deur gebruik van die sekwensiële kwadratiese programmerings metode. Die beheertoekenning sisteem is prakties geïmplementeer en getoets. ’n Aantal fout kategorieë van verskillende grade van erns is gedefinieer en twee vaartuie met verskillende vlakke van aktueerder oortolligheid is gebruik om die sisteem te toets. Die beheer toekenning algoritme is geëvalueer vir elke fout kategorie, vaartuig toetsgeval, asook vir ’n aantal verskillende beheertoekenning sisteem konfigurasies. ’n Aantal verbeterings is aangebring aan die beheertoekenning sisteem, naamlik die toevoeging van frekwensie gebaseerde toekenning en wysiging van die algoritme vir ’n onkonvensionele onbemande geleide waaier lugvaartuig. Die beheertoekenning sisteem is van toepassing op ’n aantal verskillende konvensionele vaartuig konfigurasies met geen verstellings asook van toepassing op onkonvensionele vaartuie met geringe verstellings. Die beheertoekenning sisteem kan beide enkel- en veelvoudige aktueerder tekortkominge hanteer en die belangrikheid van aktueerder oortolligheid is beklemtoon as ’n faktor wat die effektiwiteit van beheertoekenning beïnvloed. Die beheertoekenning sisteem kan effektief geïmplementeer word as deel van ’n fout verdraagsame beheersisteem of as ’n werktuig om beheertoekenning en vaartuig oortolligheid te ondersoek.