Autonomous Navigation, Guidance and Control of Small Electric Helicopter

In this study, we design an autonomous navigation, guidance and control system for a small electric helicopter. Only small, light-weight, and inaccurate sensors can be used for the control of small helicopters because of the payload limitation. To overcome the problem of inaccurate sensors, a composite navigation system is designed. The designed navigation system enables us to precisely obtain the position and velocity of the helicopter. A guidance and control system is designed for stabilizing the helicopter at an arbitrary point in three-dimensional space. In particular, a novel and simple guidance system is designed using the combination of optimal control theory and quaternion kinematics. The designs of the study are validated experimentally, and the experimental results verify the efficiency of our navigation, guidance and control system for a small electric helicopter.ArticleINTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS. 10:54 (2013)journal articl

Control Design and Performance Analysis for Autonomous Formation Flight Experimentss

Autonomous Formation Flight is a key approach for reducing greenhouse gas emissions and managing traffic in future high density airspace. Unmanned Aerial Vehicles (UAV\u27s) have made it possible for the physical demonstration and validation of autonomous formation flight concepts inexpensively and eliminates the flight risk to human pilots. This thesis discusses the design, implementation, and flight testing of three different formation flight control methods, Proportional Integral and Derivative (PID); Fuzzy Logic (FL); and NonLinear Dynamic Inversion (NLDI), and their respective performance behavior. Experimental results show achievable autonomous formation flight and performance quality with a pair of low-cost unmanned research fixed wing aircraft and also with a solo vertical takeoff and landing (VTOL) quadrotor

Control Design and Performance Analysis for Autonomous Formation Flight Experiments

Autonomous Formation Flight is a key approach for reducing greenhouse gas emissions and managing traffic in future high density airspace. Unmanned Aerial Vehicles (UAV’s) have made it possible for the physical demonstration and validation of autonomous formation flight concepts inexpensively and eliminates the flight risk to human pilots. This thesis discusses the design, implementation, and flight testing of three different formation flight control methods, Proportional Integral and Derivative (PID); Fuzzy Logic (FL); and NonLinear Dynamic Inversion (NLDI), and their respective performance behavior. Experimental results show achievable autonomous formation flight and performance quality with a pair of low-cost unmanned research fixed wing aircraft and also with a solo vertical takeoff and landing (VTOL) quadrotor

Adaptive trajectory tracking control for quadrotors with disturbances by using generalized regression neural networks

In this document, the development and experimental validation of a nonlinear controller with an adaptive disturbance compensation system applied on a quadrotor are presented. The introduced scheme relies on a generalized regression neural network (GRNN). The proposed scheme has a structure consisting of an inner control loop inaccessible to the user (i.e., an embedded controller) and an outer control loop which generates commands for the inner control loop. The adaptive GRNN is applied in the outer control loop. The proposed approach lies in the aptitude of the GRNN to estimate the disturbances and unmodeled dynamic effects without requiring accurate knowledge of the quadrotor parameters. The adaptation laws are deduced from a rigorous convergence analysis ensuring asymptotic trajectory tracking. The proposed control scheme is implemented on the QBall 2 quadrotor. Comparisons with respect to a PD-based control, an adaptive model regressor-based scheme, and an adaptive neural-network controller are carried out. The experimental results validate the functionality of the novel control scheme and show a performance improvement since smaller tracking error values are produced.Fil: Lopez Sanchez, Ivan. INSTITUTO POLITÉCNICO NACIONAL (IPN);Fil: Rossomando, Francisco Guido. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; ArgentinaFil: Pérez Alcocer, Ricardo. INSTITUTO POLITÉCNICO NACIONAL (IPN);Fil: Soria, Carlos Miguel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; ArgentinaFil: Carelli, Ricardo. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; ArgentinaFil: Moreno Valenzuela, Javier. INSTITUTO POLITÉCNICO NACIONAL (IPN)

Advanced UAV Trajectory Generation: Planning and Guidance

As technology and legislation move forward (JAA & Eurocontrol, 2004) remotely controlled, semi-autonomous or autonomous Unmanned Aerial Systems (UAS) will play a significant role in providing services and enhancing safety and security of the military and civilian community at large (e.g. surveillance and monitoring) (Coifman et al., 2004). The potential market for UAVs is, however, much bigger than just surveillance. UAVs are ideal for risk assessment and neutralization in dangerous areas such as war zones and regions stricken by disaster, including volcanic eruptions, wildfires, floods, and even terrorist acts. As they become more autonomous, UAVs will take on additional roles, such as air-to-air combat and even planetary science exploration (Held et al., 2005). As the operational capabilities of UAVs are developed there is a perceived need for a significant increase in their level of autonomy, performance, reliability and integration with a controlled airspace full of manned vehicles (military and civilian). As a consequence researchers working with advanced UAVs have moved their focus from system modeling and low-level control to mission planning, supervision and collision avoidance, going from vehicle constraints to mission constraints (Barrientos et al., 2006). This mission-based approach is most useful for commercial applications where the vehicle must accomplish tasks with a high level of performance and maneuverability. These tasks require flexible and powerful trajectory-generation and guidance capabilities, features lacking in many of the current commercial UAS. For this reason, the purpose of this work is to extend the capabilities of commercially available autopilots for UAVs. Civil systems typically use basic trajectory-generation algorithms, capable only of linear waypoint navigation (Rysdyk, 2003), with a minimum or non-existent control over the trajectory. These systems are highly constrained when maneuverability is a mission requirement. On the other hand, military researchers have developed algorithms for high-performance 3D path planning and obstacle avoidance (Price, 2006), but these are highly proprietary technologies that operate with different mission constraints (target acquisition, threat avoidance and situational awareness) so they cannot be used in civil scenarios

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

Trajectory Generation and Control for Quadrotors

This thesis presents contributions to the state-of-the-art in quadrotor control, payload transportation with single and multiple quadrotors, and trajectory generation for single and multiple quadrotors. In Ch. 2 we describe a controller capable of handling large roll and pitch angles that enables a quadrotor to follow trajectories requiring large accelerations and also recover from extreme initial conditions. In Ch. 3 we describe a method that allows teams of quadrotors to work together to carry payloads that they could not carry individually. In Ch. 4 we discuss an online parameter estimation method for quadrotors transporting payloads which enables a quadrotor to use its dynamics in order to learn about the payload it is carrying and also adapt its control law in order to improve tracking performance. In Ch. 5 we present a trajectory generation method that enables quadrotors to fly through narrow gaps at various orientations and perch on inclined surfaces. Chapter 6 discusses a method for generating dynamically optimal trajectories through a series of predefined waypoints and safe corridors and Ch. 7 extends that method to enable heterogeneous quadrotor teams to quickly rearrange formations and avoid a small number of obstacles

Aerial Vehicles

This book contains 35 chapters written by experts in developing techniques for making aerial vehicles more intelligent, more reliable, more flexible in use, and safer in operation.It will also serve as an inspiration for further improvement of the design and application of aeral vehicles. The advanced techniques and research described here may also be applicable to other high-tech areas such as robotics, avionics, vetronics, and space

Autonomous Close Formation Flight of Small UAVs Using Vision-Based Localization

As Unmanned Aerial Vehicles (UAVs) are integrated into the national airspace to comply with the 2012 Federal Aviation Administration Reauthorization Act, new civilian uses for robotic aircraft will come about in addition to the more obvious military applications. One particular area of interest for UAV development is the autonomous cooperative control of multiple UAVs. In this thesis, a decentralized leader-follower control strategy is designed, implemented, and tested from the follower’s perspective using vision-based localization. The tasks of localization and control were carried out with separate processing hardware dedicated to each task. First, software was written to estimate the relative state of a lead UAV in real-time from video captured by a camera on-board the following UAV. The software, written using OpenCV computer vision libraries and executed on an embedded single-board computer, uses the Efficient Perspective-n-Point algorithm to compute the 3-D pose from a set of 2-D image points. High-intensity, red, light emitting diodes (LEDs) were affixed to specific locations on the lead aircraft’s airframe to simplify the task if extracting the 2-D image points from video. Next, the following vehicle was controlled by modifying a commercially available, open source, waypoint-guided autopilot to navigate using the relative state vector provided by the vision software. A custom Hardware-In-Loop (HIL) simulation station was set up and used to derive the required localization update rate for various flight patterns and levels of atmospheric turbulence. HIL simulation showed that it should be possible to maintain formation, with a vehicle separation of 50 ± 6 feet and localization estimates updated at 10 Hz, for a range of flight conditions. Finally, the system was implemented into low-cost remote controlled aircraft and flight tested to demonstrate formation convergence to 65.5 ± 15 feet of separation