Wide-Area Surveillance System using a UAV Helicopter Interceptor and Sensor Placement Planning Techniques

This project proposes and describes the implementation of a wide-area surveillance system comprised of a sensor/interceptor placement planning and an interceptor unmanned aerial vehicle (UAV) helicopter. Given the 2-D layout of an area, the planning system optimally places perimeter cameras based on maximum coverage and minimal cost. Part of this planning system includes the MATLAB implementation of Erdem and Sclaroff’s Radial Sweep algorithm for visibility polygon generation. Additionally, 2-D camera modeling is proposed for both fixed and PTZ cases. Finally, the interceptor is also placed to minimize shortest-path flight time to any point on the perimeter during a detection event. Secondly, a basic flight control system for the UAV helicopter is designed and implemented. The flight control system’s primary goal is to hover the helicopter in place when a human operator holds an automatic-flight switch. This system represents the first step in a complete waypoint-navigation flight control system. The flight control system is based on an inertial measurement unit (IMU) and a proportional-integral-derivative (PID) controller. This system is implemented using a general-purpose personal computer (GPPC) running Windows XP and other commercial off-the-shelf (COTS) hardware. This setup differs from other helicopter control systems which typically use custom embedded solutions or micro-controllers. Experiments demonstrate the sensor placement planning achieving \u3e90% coverage at optimized-cost for several typical areas given multiple camera types and parameters. Furthermore, the helicopter flight control system experiments achieve hovering success over short flight periods. However, the final conclusion is that the COTS IMU is insufficient for high-speed, high-frequency applications such as a helicopter control system

Integration of an Autopilot for a Micro Air Vehicle

Two autopilots providing autonomous flight capabilities are presented herein. The first is the Pico-Pilot, demonstrated for the 12-inch size class of micro air vehicles. The second is the MicroPilot MP2028(sup g), where its integration into a 36-inch Zagi airframe (tailless, elevons only configuration) is investigated and is the main focus of the report. Analytical methods, which include the use of the Advanced Aircraft Analysis software from DARCorp, were used to determine the stability and control derivatives, which were then validated through wind tunnel experiments. From the aerodynamic data, the linear, perturbed equations of motion from steady-state flight conditions may be cast in terms of these derivatives. Using these linear equations, transfer functions for the control and navigation systems were developed and feedback control laws based on Proportional, Integral, and Derivative (PID) control design were developed to control the aircraft. The PID gains may then be programmed into the autopilot software and uploaded to the microprocessor of the autopilot. The Pico-Pilot system was flight tested and shown to be successful in navigating a 12-inch MAV through a course defined by a number of waypoints with a high degree of accuracy, and in 20 mph winds. The system, though, showed problems with control authority in the roll and pitch motion of the aircraft: causing oscillations in these directions, but the aircraft maintained its heading while following the prescribed course. Flight tests were performed in remote control mode to evaluate handling, adjust trim, and test data logging for the Zagi with integrated MP2028(sup g). Ground testing was performed to test GPS acquisition, data logging, and control response in autonomous mode. Technical difficulties and integration limitations with the autopilot prevented fully autonomous flight from taking place, but the integration methodologies developed for this autopilot are, in general, applicable for unmanned air vehicles within the 36-inch size class or larger that use a PID control based autopilot

Experimental versus computational determination of the dynamical model of a glider

In this paper we present and compare two air- craft model identification techniques that are easy to implement and suitable for various air- plane models, gliders comprised. One of them relies on flight data, while the second one uses a virtual model of the plane. To obtain the flight data, we propose a flight protocol that is simple to follow. Our analysis show that the methods find resembling results for similar airspeeds

Instrumentation and control of a target fixed-wing drone for launch and capture

This work was developed within the scope of the CAPTURE project, in which a collaborative network was intended to be built in which a quadcopter drone would help a fixed-wing drone perform landing and takeoff maneuvers. The study of small fixed-wing unmanned aerial vehicles (UAVs) were presented, as well as their attitude control, instrumentation, and trajectory tracking. One of the goals of this dissertation was to model a real vehicle, specifically the Easy Glider 4. All the work was developed based on this vehicle, for which it was necessary to use the XFLR software to obtain its aerodynamic response and thus obtain a more accurate model and, consequently, its control. The main challenges of this dissertation were related to obtaining the full dynamic model (with the aerodynamic coefficients included), the control techniques that would be used to deal with their nonlinearities, and their integration with a path following algorithm. Two types of attitude controllers were developed: a linear controller based on PI and a nonlinear controller based on the backstepping technique. An external loop was then added to make the UAV follow a specific path. Two different techniques were implemented: a path following algorithm that would make the vehicle follow a vector field around the intended trajectory and an adaptive algorithm capable of dealing with uncertainties in the environment, such as wind with unknown direction and intensity.Este trabalho é desenvolvido no âmbito do projecto CAPTURE , em que se pretende construir uma rede colaborativa em que um drone quadricóptero ajude um drone de asa fixa a realizar manobras de aterragem e descolagem. Será apresentado o estudo e modelação de pequenos veículos não tripulados de asa fixa (UAV), bem como o seu controlo de atitude, instrumentação e seguimento de trajetória. Um dos objectivos desta dissertação é a modelação de um veículo real, mais especificamente o Easy glider 4. Todo o trabalho será desenvolvido com base neste veículo, para isso, é necessário utilizar o software XFLR para obter sua resposta aerodinâmica e assim obter uma modelação mais precisa e, consequentemente, o seu controlo. Devido à complexidade da dinâmica do UAV, os principais desafios desta dissertação estão relacionados com a obtenção do modelo dinâmico, às técnicas de controlo que serão utilizadas para lidar com suas não linearidades e a sua integração com um algoritmo de path following. Serão desenvolvidos dois tipos de controladores de atitude: Um controlador linear baseado no PID e um controlador não linear baseado na técnica de backstepping. Um loop externo é então adicionado para que o UAV siga um determinado caminho. Serão implementadas duas ténicas diferentes: Um algoritmo de path following que fará o veículo seguir um campo vectorial em volta da trajetória pretendida e um algoritmo adaptativo capaz de lidar com incertezas do meio ambiente, tais como vento com direção e amplitude desconhecidas

Fault Tolerance Analysis of L1 Adaptive Control System for Unmanned Aerial Vehicles

Trajectory tracking is a critical element for the better functionality of autonomous vehicles. The main objective of this research study was to implement and analyze L1 adaptive control laws for autonomous flight under normal and upset flight conditions. The West Virginia University (WVU) Unmanned Aerial Vehicle flight simulation environment was used for this purpose. A comparison study between the L1 adaptive controller and a baseline conventional controller, which relies on position, proportional, and integral compensation, has been performed for a reduced size jet aircraft, the WVU YF-22. Special attention was given to the performance of the proposed control laws in the presence of abnormal conditions. The abnormal conditions considered are locked actuators (stabilator, aileron, and rudder) and excessive turbulence. Several levels of abnormal condition severity have been considered. The performance of the control laws was assessed over different-shape commanded trajectories. A set of comprehensive evaluation metrics was defined and used to analyze the performance of autonomous flight control laws in terms of control activity and trajectory tracking errors. The developed L1 adaptive control laws are supported by theoretical stability guarantees. The simulation results show that L1 adaptive output feedback controller achieves better trajectory tracking with lower level of control actuation as compared to the baseline linear controller under nominal and abnormal conditions

A Minimalist Control Strategy for Small UAVs

Most autopilots of existing Miniature Unmanned Air Vehicles (MUAVs) rely on control architectures that typically use a large number of sensors (gyros, accelerometers, magnetometers, GPS) and a computationally demanding estimation of flight states. As a consequence, they tend to be complex, require a significant amount of processing power and are usually expensive. Many research projects that aim at experiments with one, or even several, MUAVs would benefit from a simpler, potentially smaller, lighter and less expensive autopilot for their flying platforms. In this paper, we present a minimalist control strategy for fixed-wing MUAVs that provides the three basic functionalities of airspeed, altitude and heading turnrate control while only using two pressure sensors and a single- axis rate gyro. To achieve this, we use reactive control loops, which rely on direct feedback from the sensors instead of full state information. In order to characterize the control strategy, it was implemented on a custom-made autopilot. With data recorded during flight experiments, we carried out a statistical analysis of step responses to altitude and turnrate commands as well as responses to artificial perturbations

Autonomous Flight, Fault, and Energy Management of the Flying Fish Solar-Powered Seaplane.

The Flying Fish autonomous unmanned seaplane is designed and built for persistent ocean surveillance. Solar energy harvesting and always-on autonomous control and guidance are required to achieve unattended long-term operation. This thesis describes the Flying Fish avionics and software systems that enable the system to plan, self-initiate, and autonomously execute drift-flight cycles necessary to maintain a designated watch circle subject to environmentally influenced drift. We first present the avionics and flight software architecture developed for the unique challenges of an autonomous energy-harvesting seaplane requiring the system to be: waterproof, robust over a variety of sea states, and lightweight for flight. Seaplane kinematics and dynamics are developed based on conventional aircraft and watercraft and upon empirical flight test data. These models serve as the basis for development of flight control and guidance strategies which take the form of a cyclic multi-mode guidance protocol that smoothly transitions between nested gain-scheduled proportional-derivative feedback control laws tuned for the trim conditions of each flight mode. A fault-tolerant airspeed sensing system is developed in response to elevated failure rates arising from pitot probe water ingestion in the test environment. The fault-tolerance strategy utilizes sensor characteristics and signal energy to combine redundant sensor measurements in a weighted voting strategy, handling repeated failures, sensor recovery, non-homogenous sensors, and periods of complete sensing failure. Finally, a graph-based mission planner combines models of global solar energy, local ocean-currents, and wind with flight-verified/derived aircraft models to provide an energy-aware flight planning tool. An NP-hard asymmetric multi-visit traveling salesman planning problem is posed that integrates vehicle performance and environment models using energy as the primary cost metric. A novel A* search heuristic is presented to improve search efficiency relative to uniform cost search. A series of cases studies are conducted with surface and airborne goals for various times of day and for multi-day scenarios. Energy-optimal solutions are identified except in cases where energy harvesting produces multiple comparable-cost plans via negative-cost cycles. The always-on cyclic guidance/control system, airspeed sensor fault management algorithm, and the nested-TSP heuristic for A* are all critical innovation required to solve the posed research challenges.Ph.D.Aerospace EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/91453/1/eubankrd_1.pd

An Efficient Navigation-Control System for Small Unmanned Aircraft

Unmanned Aerial Vehicles have been research in the past decade for a broad range of tasks and application domains such as search and rescue, reconnaissance, traffic control, pipe line inspections, surveillance, border patrol, and communication bridging. This work describes the design and implementation of a lightweight Commercial-Off-The-Shelf (COTS) semi-autonomous Fixed-Wing Unmanned Aerial Vehicle (UAV). Presented here is a methodology for System Identification utilizing the Box-Jenkins model estimator on recorded flight data to characterize the system and develop a mathematical model of the aircraft. Additionally, a novel microprocessor, the XMOS, is utilized to navigate and maneuver the aircraft utilizing a PD control system. In this thesis is a description of the aircraft and the sensor suite utilized, as well as the flight data and supporting videos for the benefit of the UAV research community