Precision landing capability is a necessary development that must take place before unmanned aircraft systems (UAS) can realize their full potential in today's modern society. Current multirotor UAS are heavily reliant on GPS data to provide positioning information for landing. While generally accurate to within several meters, much higher levels of accuracy are needed to ensure safe and trouble-free operations in several UAS applications that are currently being pursued. Examples of these applications include package delivery, automatic docking and recharging, and landing on moving vehicles. The specific problem we consider is that of precision landing of a multirotor unmanned aircraft on a small barge at sea---which presents several significant challenges. Not only must we land on a moving vehicle, but the vessel also experiences random rotational and translational motion as a result of waves and wind. Because maritime operations often span long periods of time, it is also desirable that precision landing can occur at any time---day or night.In this work we present a complete approach for precision shipboard landing and address each of the aforementioned challenges. Our method is enabled by leveraging an on-board camera and a specialized landing target which can be detected in light or dark conditions. Features belonging to the target are extracted from camera imagery and are used to compute image-based visual servoing velocity commands that lead to precise alignment between the multirotor and landing target. To enable the multirotor to match the horizontal velocities of the barge, an extended Kalman filter is used to generate feed-forward velocity reference commands. The complete landing procedure is guided by a state machine architecture that incorporates corrections to account for wind, and is also capable of quickly reacquiring the landing target in a loss event. Our approach is thoroughly validated through full-scale outdoor flight tests and is shown to be reliable, timely, and accurate to within 4 to 10 centimeters.

Submitted by Automa??o e Estat?stica (sst@bczm.ufrn.br) on 2016-12-15T16:27:02Z No. of bitstreams: 1 ManoelPedroDeMedeirosNeto_DISSERT.pdf: 3664081 bytes, checksum: f1856f73174bde3b90b40604d7d1ae0e (MD5) / Approved for entry into archive by Arlan Eloi Leite Silva (eloihistoriador@yahoo.com.br) on 2016-12-15T16:36:27Z (GMT) No. of bitstreams: 1 ManoelPedroDeMedeirosNeto_DISSERT.pdf: 3664081 bytes, checksum: f1856f73174bde3b90b40604d7d1ae0e (MD5) / Made available in DSpace on 2016-12-15T16:36:27Z (GMT). No. of bitstreams: 1 ManoelPedroDeMedeirosNeto_DISSERT.pdf: 3664081 bytes, checksum: f1856f73174bde3b90b40604d7d1ae0e (MD5) Previous issue date: 2016-02-29 / Ve?culos n?o tripulados est?o cada vez mais presentes no cotidiano das empresas e das pessoas, pois esse tipo de ve?culo est? de forma crescente desempenhando atividades que anteriormente eram apenas executadas por seres humanos. No entanto, para se compreender melhor o potencial de ve?culos n?o tripulados, ? importante conhecer seus tipos, caracter?sticas, aplica??es, limita??es e desafios, pois somente com esse conhecimento pode-se entender as potencialidades do uso de ve?culos dessa natureza em aplica??es variadas. Nesse contexto, na primeira parte desta pesquisa foram estudados os diferentes tipos de ve?culos n?o tripulados, i.e. terrestres, aqu?ticos, a?reos e h?bridos. Durante a segunda fase da pesquisa, foi realizado um aprofundamento tendo como foco as interfaces de usu?rio para controle dos ve?culos a?reos n?o tripulados. Esses dois levantamentos iniciais do dom?nio, permitiram a identifica??o de desafios e oportunidades para o desenvolvimento de novas aplica??es para esse contexto. Com base no conhecimento adquirido com esses estudos, ent?o, foi desenvolvido um sistema de entrega automatizada de objetos para o campus de Universidades, denominado de PostDrone University, e desenvolvido um ve?culo a?reo n?o tripulado para realizar as entregas, denominado de PostDrone University UAV K-263. O sistema possui uma interface de usu?rio de f?cil uso, que n?o requer conhecimentos de dom?nios espec?ficos como avia??o ou controle de aeronaves para sua opera??o. Por fim, diversos testes foram realizados com o intuito de validar e identificar as limita??es da solu??o desenvolvida nesta pesquisa. / Unmanned vehicles are increasingly present in the daily of companies and people, because this kind of vehicle is performing ever more tasks that were previously only executed by human beings. However, to better understand the potential of unmanned vehicles, it is important to know their types, features, applications, limitations and challenges, thus with this knowledge it is possible to comprehend the possibilities of use of these vehicles in several applications. In this context, the first step of the present research consists in studying the different kinds of unmanned vehicles, i.e., ground, surface and underwater, aerial, and hybrid. During the second step of the research, a deepening study was accomplished, with focus on user interfaces of unmanned aerial vehicles. These two initial reviews of the domain allowed the identification of challenges and opportunities to the development of new applications for this context. Based on the acquired knowledge from these studies, then, an automated goods delivery system was developed for universities? campuses, called PostDrone University, and an unmanned vehicle to make the deliveries, called PostDrone University UAV K-263, was also developed. The system has an easy use UI, which does not require the user to have knowledge about specific domains, as aviation or aircraft control, for the operation of the system. Lastly, several test were accomplished aiming to validate the solution proposed in the present research and identify its limitations / 2018-03-31

With the increased performance and reduced cost of cameras, the robotics community has taken great interest in estimation and control algorithms that fuse camera data with other sensor data.In response to this interest, this dissertation investigates the algorithms needed for robust guidance, navigation, and control of fixed-wing and multirotor aircraft applied to target estimation and circumnavigation.This work begins with the development of a method to estimate target position relative to static landmarks, deriving and using a state-of-the-art EKF that estimates static landmarks in its state.Following this estimator, improvements are made to a nonlinear observer solving part of the SLAM problem.These improvements include a moving origin process to keep the coordinate origin within the camera field of view and a sliding window iteration algorithm to drastically improve convergence speed of the observer.Next, observers to directly estimate relative target position are created with a circumnavigation guidance law for a multirotor aircraft.Taking a look at fixed-wing aircraft, a state-dependent LQR controller with inputs based on vector fields is developed, in addition to an EKF derived from error state and Lie group theory to estimate aircraft state and inertial wind velocity.The robustness of this controller/estimator combination is demonstrated through Monte Carlo simulations.Next, the accuracy, robustness, and consistency of a state-of-the-art EKF are improved for multirotors by augmenting the filter with a drag coefficient, partial updates, and keyframe resets.Monte Carlo simulations demonstrate the improved accuracy and consistency of the augmented filter.Lastly, a visual-inertial EKF using image coordinates is derived, as well as an offline calibration tool to estimate the transforms needed for accurate, visual-inertial estimation algorithms.The imaged-based EKF and calibrator are also shown to be robust under various conditions through numerical simulation.

This work deals with physical laws affecting behavior of a quadcopter as a mobile robot. It describes methods of controlling movements and stability. The result of the theoretical analysis is creation of simulation model. Moreover it depicts practical software developement of a real machine controller unit with its own conclusion, comparison between simulation and practical experiments and the workflow of the physical system construction.

Optimal control for multicopters is difficult in part due to the low processing power available, and the instability inherent to multicopters. Deep imitation learning is a method for approximating an expert control policy with a neural network, and has the potential of improving control for multicopters. We investigate the performance and reliability of deep imitation learning with trajectory optimization as the expert policy by first defining a dynamics model for multicopters and applying a trajectory optimization algorithm to it. Our investigation shows that network architecture plays an important role in the characteristics of both the learning process and the resulting control policy, and that in particular trajectory optimization can be leveraged to improve convergence times for imitation learning. Finally, we identify some limitations and future areas of study and development for the technology. / Optimal kontroll för multikoptrar är ett svårt problem delvis på grund av den vanligtvis låga processorkraft som styrdatorn har, samt att multikoptrar är synnerligen instabila system. Djup imitationsinlärning är en metod där en beräkningstung expert approximeras med ett neuralt nätverk, och gör det därigenom möjligt att köra dessa tunga experter som realtidskontroll för multikoptrar. I detta arbete undersöks prestandan och pålitligheten hos djup imitationsinlärning med banoptimering som expert genom att först definiera en dynamisk modell för multikoptrar, sedan applicera en välkänd banoptimeringsmetod på denna modell, och till sist approximera denna expert med imitationsinlärning. Vår undersökning visar att nätverksarkitekturen spelar en avgörande roll för karakteristiken hos både inlärningsprocessens konvergenstid, såväl som den resulterande kontrollpolicyn, och att särskilt banoptimering kan nyttjas för att förbättra konvergenstiden hos imitationsinlärningen. Till sist påpekar vi några begränsningar hos metoden och identifierar särskilt intressanta områden för framtida studier.

The omnicopter is a small unmanned aerial vehicle capable of executing decoupled translational and rotational motion (six degree of freedom, 6DOF, motion). The development of controllers for various 6DOF controllable multirotors has been much more limited than development for quadrotors, which makes selecting a controller for a 6DOF multirotor difficult. The omnicopter is subject to various uncertainties and disturbances from hardware changes, structural dynamics, and airflow, making adaptive controllers particularly interesting to investigate. The goal of this research is to design and evaluate the performance of various position and attitude controller combinations for the omnicopter, specifically focusing on adaptive controllers. Simulations are first used to compare combinations of three position controllers, PID, model reference adaptive control, augmented model reference adaptive control (aMRAC), and four attitude controllers, PI/feedback linearization (PIFL), augmented model reference adaptive control, backstepping, and adaptive backstepping (aBack). For the simulations, the omnicopter is commanded to point at and track a stationary aim point as it travels along a $C^0$ continuous trajectory and a trajectory that is $C^1$ continuous. The controllers are stressed by random disturbances and the addition of an unaccounted for suspended mass. The augmented model reference adaptive controller for position control paired with the adaptive backstepping controller for attitude control is shown to be the best controller combination for tracking various trajectories while subject to disturbances. Based on the simulation results, the PID/PIFL and aMRAC/aBack controllers are selected to be compared during three different flight tests. The first flight test is on a $C^1$ continuous trajectory while the omnicopter is commanded to point at and track a stationary aim point. The second flight test is a hover with an unmodeled added weight, and the third is a circular trajectory with a broken blade. As with the simulation results, the adaptive controller is shown to yield better performance than the nonadaptive controller for all scenarios, particularly for position tracking. With an added weight or a broken propeller, the adaptive attitude controller struggles to return to level flight, but is capable of maintaining steady flight when the nonadaptive controller tends to fail. Finally, while model reference adaptive controllers are shown to be effective, their nonlinearity can make them difficult to tune and certify via standard certification methods, such as gain and phase margin. A method for using time delay margin estimates, a potential certification metric, to tune the adaptive parameter tuning gain matrix is shown to be useful when applied to an augmented MRAC controller for a quadrotor. / Doctor of Philosophy / The omnicopter is a small unmanned aerial vehicle capable of executing decoupled translational and rotational motion. The development of controllers for these types of vehicles has been limited, making controller selection difficult. The omnicopter is subject to variations in hardware and airflow, making adaptive controllers particularly interesting to investigate. The goal of this research is to design and compare the performance of various position and attitude controller combinations for the omnicopter, specifically focusing on adaptive controllers. Simulations are first used to compare combinations of several position and attitude controllers on various trajectories and disturbances. Simulation results showed that a fully adaptive controller combination produced the best trajectory tracking while subject to disturbances. As with the simulation results, flight tests showed the adaptive controller yields better performance than the nonadaptive controller for all scenarios, particularly for position tracking. Finally, while the adaptive position controller was shown to be effective, it is difficult to tune and certify for widespread use. A method for using time delay margin estimates, a potential certification metric, to tune the adaptive controller is shown to be useful when applied to an adaptive controller for a quadrotor.

Most micro air vehicles rely heavily on reliable GPS measurements for proper estimation and control, and therefore struggle in GPS-degraded environments. When GPS is not available, the global position and heading of the vehicle is unobservable. This dissertation establishes the theoretical and practical advantages of a relative navigation framework for MAV navigation in GPS-degraded environments. This dissertation explores how the consistency, accuracy, and stability of current navigation approaches degrade during prolonged GPS dropout and in the presence of heading uncertainty. Relative navigation (RN) is presented as an alternative approach that maintains observability by working with respect to a local coordinate frame. RN is compared with several current estimation approaches in a simulation environment and in hardware experiments. While still subject to global drift, RN is shown to produce consistent state estimates and stable control. Estimating relative states requires unique modifications to current estimation approaches. This dissertation further provides a tutorial exposition of the relative multiplicative extended Kalman filter, presenting how to properly ensure observable state estimation while maintaining consistency. The filter is derived using both inertial and body-fixed state definitions and dynamics. Finally, this dissertation presents a series of prolonged flight tests, demonstrating the effectiveness of the relative navigation approach for autonomous GPS-degraded MAV navigation in varied, unknown environments. The system is shown to utilize a variety of vision sensors, work indoors and outdoors, run in real-time with onboard processing, and not require special tuning for particular sensors or environments. Despite leveraging off-the-shelf sensors and algorithms, the flight tests demonstrate stable front-end performance with low drift. The flight tests also demonstrate the onboard generation of a globally consistent, metric, and localized map by identifying and incorporating loop-closure constraints and intermittent GPS measurements. With this map, mission objectives are shown to be autonomously completed.

Micro air vehicles have recently gained popularity due to their potential as autonomous systems. Their future impact, however, will depend in part on how well they can navigate in GPS-denied and GPS-degraded environments. In response to this need, this dissertation investigates a potential solution for GPS-denied operations called relative navigation. The method utilizes keyframe-to-keyframe odometry estimates and their covariances in a global back end that represents the global state as a pose graph. The back end is able to effectively represent nonlinear uncertainties and incorporate opportunistic global constraints. The GPS-denied research community has, for the most part, neglected to consider fixed-wing aircraft. This dissertation enables fixed-wing aircraft to utilize relative navigation by accounting for their sensing requirements. The development of an odometry-like, front-end, EKF-based estimator that utilizes only a monocular camera and an inertial measurement unit is presented. The filter uses the measurement model of the multi-state-constraint Kalman filter and regularly performs relative resets in coordination with keyframe declarations. In addition to the front-end development, a method is provided to account for front-end velocity bias in the back-end optimization. Finally a method is presented for enabling multiple vehicles to improve navigational accuracy by cooperatively sharing information. Modifications to the relative navigation architecture are presented that enable decentralized, cooperative operations amidst temporary communication dropouts. The proposed framework also includes the ability to incorporate inter-vehicle measurements and utilizes a new concept called the coordinated reset, which is necessary for optimizing the cooperative odometry and improving localization. Each contribution is demonstrated through simulation and/or hardware flight testing. Simulation and Monte-Carlo testing is used to show the expected quality of the results. Hardware flight-test results show the front-end estimator performance, several back-end optimization examples, and cooperative GPS-denied operations.

Micro aerial vehicles and other autonomous systems have the potential to truly transform life as we know it, however much of the potential of autonomous systems remains unrealized because reliable navigation is still an unsolved problem with significant challenges. This dissertation presents solutions to many aspects of autonomous navigation. First, it presents ROSflight, a software and hardware architure that allows for rapid prototyping and experimentation of autonomy algorithms on MAVs with lightweight, efficient flight control. Next, this dissertation presents improvments to the state-of-the-art in optimal control of quadrotors by utilizing the error-state formulation frequently utilized in state estimation. It is shown that performing optimal control directly over the error-state results in a vastly more computationally efficient system than competing methods while also dealing with the non-vector rotation components of the state in a principled way. In addition, real-time robust flight planning is considered with a method to navigate cluttered, potentially unknown scenarios with real-time obstacle avoidance. Robust state estimation is a critical component to reliable operation, and this dissertation focuses on improving the robustness of visual-inertial state estimation in a filtering framework by extending the state-of-the-art to include better modeling and sensor fusion. Further, this dissertation takes concepts from the visual-inertial estimation community and applies it to tightly-coupled GNSS, visual-inertial state estimation. This method is shown to demonstrate significantly more reliable state estimation than visual-inertial or GNSS-inertial state estimation alone in a hardware experiment through a GNSS-GNSS denied transition flying under a building and back out into open sky. Finally, this dissertation explores a novel method to combine measurements from multiple agents into a coherent map. Traditional approaches to this problem attempt to solve for the position of multiple agents at specific times in their trajectories. This dissertation instead attempts to solve this problem in a relative context, resulting in a much more robust approach that is able to handle much greater intial error than traditional approaches.