With the recent development of reliable autonomous technologies for small unmanned air vehicles (UAVs), the algorithms utilizing teams of these vehicles are becoming an increasingly important research area. Unfortunately, there is no unified framework into which all (or even most) cooperative control problems fall. Five factors that affect the development of cooperative control algorithms are objective coupling, communication, completeness, robustness, and efficiency. We classify cooperative control algorithms by these factors and then present three algorithms with application to target and perimeter surveillance and a method for decentralized algorithm design. The primary contributions of this research are the development and analysis of decentralized algorithms for perimeter and target surveillance. We pose the cooperative perimeter surveillance problem and offer a decentralized solution that accounts for perimeter growth (expanding or contracting) and insertion/deletion of team members. By identifying and sharing the critical coordination information and by exploiting the known communication topology, only a small communication range is required for accurate performance. Convergence of the algorithm to the optimal configuration is proven to occur in finite-time. Simulation and hardware results are presented that demonstrate the applicability of the solution. For single target surveillance, a team of UAVs angularly spaced (i.e. in the splay state configuration) provides the best coverage of the target in a wide variety of circumstances. We propose a decentralized algorithm to achieve the splay state configuration for a team of UAVs tracking a moving target and derive the allowable bounds on target velocity to generate a feasible solution as well as show that, near equilibrium, the overall system is exponentially stable. Monte Carlo simulations indicate that the surveillance algorithm is asymptotically stable for arbitrary initial conditions. We conclude with high fidelity simulation and actual flight tests to show the applicability of the splay state controller to unmanned air systems.

The design, analysis, construction and flight testing of a miniature Vertical Take-Off and Landing (VTOL) tail-sitter Unmanned Aerial Vehicle (UAV) prototype is presented in detail. Classic aircraft design methods were combined with numerical analysis to estimate the aircraft performance and flight characteristics. The numerical analysis employed a propeller blade-element theory coupled with momentum equations to predict the influence of a propeller slipstream on the freestream flow field, then the aircraft was analyzed using 3-D vortex lifting-line theory to model finite wings immersed in the flow field. Four prototypes were designed, built, and tested and the evolution of these prototypes is presented. The final prototype design is discussed in detail. A method for sizing control surfaces for a tail-sitter was defined. The final prototype successfully demonstrated controllability both in horizontal flight and vertical flight. Significant contributions included the development of a control system that was effective in hover as well as descending vertical flight, and the development of a strong but light weight airframe. The aircraft had a payload weight fraction of 14.5% and a maximum dimension of one meter, making it the smallest tail-sitter UAV to carry a useful payload. This project is expected to provide a knowledge base for the future design of small electric VTOL tail-sitter aircraft and to provide an airframe for future use in tail-sitter research.

Recent developments in autopilot technology have increased the potential use of micro air vehicles (MAVs). As the number of applications increase, the demand on MAVs to operate autonomously in any scenario increases. Currently, MAVs cannot reliably fly in cluttered environments because of the difficulty to detect and avoid obstacles. The main contribution of this research is to offer obstacle detection and avoidance strategies using laser rangers and cameras coupled with computer vision processing. In addition, we explore methods of visual target tracking and task allocation. Utilizing a laser ranger, we develop a dynamic geometric guidance strategy to generate paths around detected obstacles. The strategy overrides a waypoint planner in the presence of pop-up-obstacles. We develop a second guidance strategy that oscillates the MAV around the waypoint path and guarantees obstacle detection and avoidance. Both rely on a laser ranger for obstacles detection and are demonstrated in simulation and in flight tests. Utilizing EO/IR cameras, we develop two guidance strategies based on movement of obstacles in the camera field-of-view to maneuver the MAV around pop-up obstacles. Vision processing available on a ground station provides range and bearing to nearby obstacles. The first guidance law is derived for single obstacle avoidance and pushes the obstacle to the edge of the camera field-of-view causing the vehicle to avoid a collision. The second guidance law is derived for two obstacles and balances the obstacles on opposite edges of the camera field-of-view, maneuvering between the obstacles. The guidance strategies are demonstrated in simulation and flight tests. This research also addresses the problem of tracking a ground based target with a fixed camera pointing out the wing of a MAV that is subjected to constant wind. Rather than planning explicit trajectories for the vehicle, a visual feedback guidance strategy is developed that maintains the target in the field-of-view of the camera. We show that under ideal conditions, the resulting flight paths are optimal elliptical trajectories if the target is forced to the center of the image plane. Using simulation and flight tests, the resulting algorithm is shown to be robust with respect to gusts and vehicle modeling errors. Lastly, we develop a method of a priori collision avoidance in assigning multiple tasks to cooperative unmanned air vehicles (UAV). The problem is posed as a combinatorial optimization problem. A branch and bound tree search algorithm is implemented to find a feasible solution using a cost function integrating distance traveled and proximity to other UAVs. The results demonstrate that the resulting path is near optimal with respect to distance traveled and includes a significant increase in expected proximity distance to other UAVs. The algorithm runs in less than a tenth of a second allowing on-the-fly replanning.

Air corridors are an integral part of the advanced air mobility infrastructure. They are the virtual highways in the sky for transportation of people and cargo in the controlled airspace at an altitude of around 1000 ft. to 2000 ft. above the ground level. This paper presents fundamental insights into the design of air corridors with high operational efficiency as well as zero collisions. It begins with the definitions of air cube, skylane or track, intersection, vertiport, gate, and air corridor. Then, a multi-layered air corridor model is proposed. Traffic at intersections is analyzed in detail with examples of vehicles turning in different directions. The concept of capacity of an air corridor is introduced along with the nature of distribution of locations of vehicles in the air corridor and collision probability inside the corridor are discussed. Finally, the results of simulations of traffic flows are presented.

In this thesis, an unmanned air vehicle (UAV) is used to develop a prototype base test platform for flight testing of new control algorithms and avionics for advanced UAV system development applications. A control system that holds the UAV at a fixed altitude above the ground is designed and flight tested. Only the longitudinal motion of the UAV is considered during the controller design, hence its lateral motions are controlled manually by a remote control unit from the ground. UAV&amp / #8217 / s altitude with respect to the mean sea level and position are obtained by an onboard global positioning system (GPS) and this information is transmitted to the ground computer via radio frequency (RF) communication modules. The altitude of the UAV above the ground is calculated by using the digital terrain elevation data (DTED). A controller is designed and its gains are tuned to maintain this flight altitude at a desired value by using the mathematical model developed to represent the longitudinal dynamics of the UAV. Input signals generated by the controller for elevator deflections are transmitted back to the UAV via RF communication modules to drive onboard servomotors to generate desired elevator deflections. All controller computations and RF communications are handled by a MATLAB&reg / based platform on a ground computer. UAV flight tests are carried out at two different autopilot modes / namely, mean sea level (MSL) altitude hold mode and above ground level (AGL) altitude hold mode. The developed platform worked properly during flight tests and proved to be reliable in almost every condition. Moreover, the designed controller system is demonstrated to be effective and it fulfills the requirements.

Spacecloth design and development is vital and crucial in Radar Absorbing Materials (RAM) for achieving Low Observability in an Aircraft or an Unmanned Air Vehicle(UAV). The RAM design translates into the spacecloth design. The spacecloths form the constituent layers in a broadband Jaumann absorber in which case they have to be designed for various values of surface resistivity. The design specifications of spacecloth(s) in RAMS is well understood and documented in literature. But the design of spacecloth hitherto, has been the domain of materials' scientists wherein the specified properties of the spacecloth are achieved by an iterative, trial and error process, by mixing various constituents in different proportions to get the design specified surface resistivity in the final end-product. In an effort to bridge this gap, a novel spacecloth for RAM applications is proposed in the thesis. It is proposed that a repetitive geometrical grid network of chip resistors simulates spacecloth. The sheet resistivity of the spacecloth is derived by analyzing various geometries like square, rectangle, triangle and hexagonal grids. The transmission and reflection loss for the chip resistor spacecloth is derived. The design of chip resistor spacecloths for operation at S and C bands is given followed by experimental verification using waveguide simulator experiments. Numerical study of multilayer RAM has been carried out with exponential taper variation of surface resistivities for constituent spacecloth layers and design curves are given for multilayer RAM both for normal and oblique incidence for TE and TM polarizations.

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Coalitions are collections of agents that join together to solve a common problem that either cannot be solved individually or can be solved more efficiently as a group. Each individual agent has capabilities that can benefit the group when working together as a coalition. Typically, individual capabilities are joined together in an additive way when forming a coalition. This work will introduce a new operator that is used when combining capabilities, and suggest that the behavior of the operator is contextual, depending on the nature of the capability itself. This work considers six different capabilities of Unmanned Air Vehicles (UAV) and determines the nature of the new operator in the context of each capability as coalitions (squadrons) of UAVs are formed. Coalitions are formed using three different search algorithms, both with and without heuristics: Depth-First, Depth-First Iterative Deepening, and Genetic Algorithm (GA). The effectiveness of each algorithm is evaluated. Multi agent-based UAV simulation software was developed and used to test the ideas presented. In addition to coalition formation, the software aims to address additional multi-agent issues such as agent identity, mutability, and communication as applied to UAV systems, in a realistic simulated environment. Social potential fields provide a means of modeling a clustering attractive force at the same time as a collision-avoiding repulsive force, and are used by the simulation to maintain aircraft position relative to other UAVs.

Video acquired using a camera mounted on a mini Unmanned Air Vehicle (mUAV) may be very helpful in Wilderness Search and Rescue and many other applications but is commonly plagued with limited spatial and temporal field of views, distractive jittery motions, disorienting rotations, and noisy and distorted images. These problems collectively make it very difficult for human viewers to identify objects of interest as well as infer correct orientations throughout the video. In order to expand the temporal and spatial field of view, stabilize, and better orient users of noisy and distorted mUAV video, a method is proposed of estimating in software and in real time the relative motions of each frame to the next by tracking a small subset of features within each frame to the next. Using these relative motions, a local Euclidean mosaic of the video can be created and a curve can be fit to the video's accumulative motion path to stabilize the presentations of both the video and the local Euclidean mosaic. The increase in users' abilities to perform common search-and-rescue tasks of identifying objects of interest throughout the stabilized and locally mosaiced mUAV video is then evaluated. Finally, a discussion of remaining limitations is presented along with some possibilities for future work.

Unmanned vehicle systems, specifically unmanned air vehicles (UAVs) and unmanned ground vehicles (UGVs), have become a popular research topic. This thesis discusses the potential of a UAV-UGV system used to track a human moving through complex urban terrain. This research focuses on path planning problems for both a UAV and a UGV, and presents effective solutions for both problems. In the UAV path planning problem, we desire to plan a path for a miniature fixed-wing UAV to fly through known urban terrain without colliding with any buildings. We present the Waypoint RRT (WRRT) algorithm, which accounts for UAV dynamics while planning a flyable, collision-free waypoint path for a UAV in urban terrain. Results show that this method is fast and robust, and is able to plan paths in difficult urban environments and other terrain maps as well. Simulation and hardware tests demonstrate that these paths are indeed flyable by a UAV. The UGV path planning problem focuses on planning a path to capture a moving target in an urban grid. We discuss using a target motion model based on Markov chains to predict future target locations. We then introduce the Capture and Propagate algorithm, which uses this target motion model to determine the probabilities of capturing the target in various numbers of steps and with various initial UGV moves. By applying some different cost functions, the result of this algorithm is used to choose an optimal first step for the UGV. Results demonstrate that this algorithm is at least as effective as planning a path directly to the current location of the target, and that in many cases, this algorithm performs better. We discuss these cases and verify them with simulation results.