Cooperation and Autonomy for UAV Swarms

In the last few years, the level of autonomy of mini- and micro-Unmanned Aerial Vehicles (UAVs) has increased thanks to the miniaturization of flight control systems and payloads, and the availability of computationally affordable algorithms for autonomous Guidance Navigation and Control (GNC). However, despite the technological evolution, operations conducted by a single micro-UAV still present limits in terms of performance, coverage and reliability. The scope of this thesis is to overcome single-UAV limits by developing new distributed GNC architectures and technologies where the cooperative nature of a UAV formation is exploited to obtain navigation information. Moreover, this thesis aims at increasing UAVs autonomy by developing a take-off and landing technique which permits to complete fully autonomous operations, also taking into account regulations and the required level of safety. Indeed, in addition to the typical performance limitations of micro-UAVs, this thesis takes into account also those applications where a multi-vehicle architecture can improve coverage and reliability, and allow real time data fusion. Furthermore, considering the low cost of micro-UAV systems with consumer grade avionics, having several UAVs can be more cost effective than equipping a single vehicle with high performance equipment. Among several research challenges to be addressed in order to design and operate a distributed system of vehicles working together for real time applications, this thesis focuses on the following topics regarding cooperation and autonomy: Improvement of UAV navigation performance: This research topic aims at improving the navigation performance of an UAV flying cooperatively with one or more UAVs, considering that the only integration of low cost inertial measurement units (IMUs), Global Navigation Satellite Systems (GNSS) and magnetometers allows real time stabilization and flight control but may not be suitable for applications requiring fine sensor pointing. The focus is set on outdoor environments and it is assumed that all vehicles of the formation are flying under nominal Global Positioning System (GPS) coverage, hence, the main navigation improvement is in terms of attitude estimation. In particular, the key concept is to exploit Differential GPS (DGPS) among vehicles and vision-based tracking to build a virtual additional navigation sensor whose information is then integrated within a sensor fusion algorithm based on an Extended Kalman Filter (EKF). Both numerical simulations and flight results show the potential of sub-degree angular accuracy. In particular, proper formation geometries, and even relatively small baselines, allow achieving a heading uncertainty that can approach 0.1°, which represents a very important result taking into account typical performance levels of IMUs onboard small UAVs. UAV navigation in GPS challenging environments: This research topic aims at developing algorithms for improving navigation performance of UAVs flying in GPS-challenging environments (e.g. natural or urban canyons, or mixed outdoor-indoor settings), where GPS measurements can be unavailable and/or unreliable. These algorithms exploit aiding measurements from one or more cooperative UAVs flying under nominal GPS coverage and are based on the concepts of relative sensing and information sharing. The developed sensor fusion architecture is based on a tightly coupled EKF that integrates measurements from onboard inertial sensors and magnetometers, the available GPS pseudoranges, position information from cooperative UAVs, and line-of-sight information derived by visual sensors. In addition, if available, measurements coming from a monocular pose estimation algorithm can be integrated within the developed EKF in order to counteract the position error drift. Results show that aiding measurements from a single cooperative UAV do not allow eliminating position error drift. However, combining this approach with a standalone visual-SLAM, integrating valid pseudoranges in the tightly coupled filtering structure, or exploiting ad hoc commanded motion of the cooperative vehicle under GPS coverage drastically reduces the position error drift keeping meter-level positioning accuracy also in absence of reliable GPS observables. Autonomous take-off and landing: This research activity, conducted during a 6 month Academic Guest period at ETH Zürich, focuses on increasing reliability, versatility and flight time of UAVs, by developing an autonomous take-off and landing technique. Often, the landing phase is the most critical as it involves performing delicate maneuvers; e.g., landing on a station for recharging or on a ground carrier for transportation. These procedures are subject to constraints on time and space and must be robust to changes in environmental conditions. These problems are addressed in this thesis, where a guidance approach, based on the intrinsic Tau guidance theory, is integrated within the end-to-end software developed at ETH Zürich. This method has been validated both in simulations and through real platform experiments by using rotary-wing UAVs to land on static platforms. Results show that this method achieves smooth landings within 10 cm accuracy, with easily adjustable trajectory parameters

Autonomous Drone Landings on an Unmanned Marine Vehicle using Deep Reinforcement Learning

This thesis describes with the integration of an Unmanned Surface Vehicle (USV) and an Unmanned Aerial Vehicle (UAV, also commonly known as drone) in a single Multi-Agent System (MAS). In marine robotics, the advantage offered by a MAS consists of exploiting the key features of a single robot to compensate for the shortcomings in the other. In this way, a USV can serve as the landing platform to alleviate the need for a UAV to be airborne for long periods time, whilst the latter can increase the overall environmental awareness thanks to the possibility to cover large portions of the prevailing environment with a camera (or more than one) mounted on it. There are numerous potential applications in which this system can be used, such as deployment in search and rescue missions, water and coastal monitoring, and reconnaissance and force protection, to name but a few. The theory developed is of a general nature. The landing manoeuvre has been accomplished mainly identifying, through artificial vision techniques, a fiducial marker placed on a flat surface serving as a landing platform. The raison d'etre for the thesis was to propose a new solution for autonomous landing that relies solely on onboard sensors and with minimum or no communications between the vehicles. To this end, initial work solved the problem while using only data from the cameras mounted on the in-flight drone. In the situation in which the tracking of the marker is interrupted, the current position of the USV is estimated and integrated into the control commands. The limitations of classic control theory used in this approached suggested the need for a new solution that empowered the flexibility of intelligent methods, such as fuzzy logic or artificial neural networks. The recent achievements obtained by deep reinforcement learning (DRL) techniques in end-to-end control in playing the Atari video-games suite represented a fascinating while challenging new way to see and address the landing problem. Therefore, novel architectures were designed for approximating the action-value function of a Q-learning algorithm and used to map raw input observation to high-level navigation actions. In this way, the UAV learnt how to land from high latitude without any human supervision, using only low-resolution grey-scale images and with a level of accuracy and robustness. Both the approaches have been implemented on a simulated test-bed based on Gazebo simulator and the model of the Parrot AR-Drone. The solution based on DRL was further verified experimentally using the Parrot Bebop 2 in a series of trials. The outcomes demonstrate that both these innovative methods are both feasible and practicable, not only in an outdoor marine scenario but also in indoor ones as well

Visual guidance of unmanned aerial manipulators

The ability to fly has greatly expanded the possibilities for robots to perform surveillance, inspection or map generation tasks. Yet it was only in recent years that research in aerial robotics was mature enough to allow active interactions with the environment. The robots responsible for these interactions are called aerial manipulators and usually combine a multirotor platform and one or more robotic arms. The main objective of this thesis is to formalize the concept of aerial manipulator and present guidance methods, using visual information, to provide them with autonomous functionalities. A key competence to control an aerial manipulator is the ability to localize it in the environment. Traditionally, this localization has required external infrastructure of sensors (e.g., GPS or IR cameras), restricting the real applications. Furthermore, localization methods with on-board sensors, exported from other robotics fields such as simultaneous localization and mapping (SLAM), require large computational units becoming a handicap in vehicles where size, load, and power consumption are important restrictions. In this regard, this thesis proposes a method to estimate the state of the vehicle (i.e., position, orientation, velocity and acceleration) by means of on-board, low-cost, light-weight and high-rate sensors. With the physical complexity of these robots, it is required to use advanced control techniques during navigation. Thanks to their redundancy on degrees-of-freedom, they offer the possibility to accomplish not only with mobility requirements but with other tasks simultaneously and hierarchically, prioritizing them depending on their impact to the overall mission success. In this work we present such control laws and define a number of these tasks to drive the vehicle using visual information, guarantee the robot integrity during flight, and improve the platform stability or increase arm operability. The main contributions of this research work are threefold: (1) Present a localization technique to allow autonomous navigation, this method is specifically designed for aerial platforms with size, load and computational burden restrictions. (2) Obtain control commands to drive the vehicle using visual information (visual servo). (3) Integrate the visual servo commands into a hierarchical control law by exploiting the redundancy of the robot to accomplish secondary tasks during flight. These tasks are specific for aerial manipulators and they are also provided. All the techniques presented in this document have been validated throughout extensive experimentation with real robotic platforms.La capacitat de volar ha incrementat molt les possibilitats dels robots per a realitzar tasques de vigilància, inspecció o generació de mapes. Tot i això, no és fins fa pocs anys que la recerca en robòtica aèria ha estat prou madura com per començar a permetre interaccions amb l’entorn d’una manera activa. Els robots per a fer-ho s’anomenen manipuladors aeris i habitualment combinen una plataforma multirotor i un braç robòtic. L’objectiu d’aquesta tesi és formalitzar el concepte de manipulador aeri i presentar mètodes de guiatge, utilitzant informació visual, per dotar d’autonomia aquest tipus de vehicles. Una competència clau per controlar un manipulador aeri és la capacitat de localitzar-se en l’entorn. Tradicionalment aquesta localització ha requerit d’infraestructura sensorial externa (GPS, càmeres IR, etc.), limitant així les aplicacions reals. Pel contrari, sistemes de localització exportats d’altres camps de la robòtica basats en sensors a bord, com per exemple mètodes de localització i mapejat simultànis (SLAM), requereixen de gran capacitat de còmput, característica que penalitza molt en vehicles on la mida, pes i consum elèctric son grans restriccions. En aquest sentit, aquesta tesi proposa un mètode d’estimació d’estat del robot (posició, velocitat, orientació i acceleració) a partir de sensors instal·lats a bord, de baix cost, baix consum computacional i que proporcionen mesures a alta freqüència. Degut a la complexitat física d’aquests robots, és necessari l’ús de tècniques de control avançades. Gràcies a la seva redundància de graus de llibertat, aquests robots ens ofereixen la possibilitat de complir amb els requeriments de mobilitat i, simultàniament, realitzar tasques de manera jeràrquica, ordenant-les segons l’impacte en l’acompliment de la missió. En aquest treball es presenten aquestes lleis de control, juntament amb la descripció de tasques per tal de guiar visualment el vehicle, garantir la integritat del robot durant el vol, millorar de l’estabilitat del vehicle o augmentar la manipulabilitat del braç. Aquesta tesi es centra en tres aspectes fonamentals: (1) Presentar una tècnica de localització per dotar d’autonomia el robot. Aquest mètode està especialment dissenyat per a plataformes amb restriccions de capacitat computacional, mida i pes. (2) Obtenir les comandes de control necessàries per guiar el vehicle a partir d’informació visual. (3) Integrar aquestes accions dins una estructura de control jeràrquica utilitzant la redundància del robot per complir altres tasques durant el vol. Aquestes tasques son específiques per a manipuladors aeris i també es defineixen en aquest document. Totes les tècniques presentades en aquesta tesi han estat avaluades de manera experimental amb plataformes robòtiques real

Modeling the Human Visuo-Motor System for Remote-Control Operation

University of Minnesota Ph.D. dissertation. 2018. Major: Computer Science. Advisors: Nikolaos Papanikolopoulos, Berenice Mettler. 1 computer file (PDF); 172 pages.Successful operation of a teleoperated miniature rotorcraft relies on capabilities including guidance, trajectory following, feedback control, and environmental perception. For many operating scenarios fragile automation systems are unable to provide adequate performance. In contrast, human-in-the-loop systems demonstrate an ability to adapt to changing and complex environments, stability in control response, high level goal selection and planning, and the ability to perceive and process large amounts of information. Modeling the perceptual processes of the human operator provides the foundation necessary for a systems based approach to the design of control and display systems used by remotely operated vehicles. In this work we consider flight tasks for remotely controlled miniature rotorcraft operating in indoor environments. Operation of agile robotic systems in three dimensional spaces requires a detailed understanding of the perceptual aspects of the problem as well as knowledge of the task and models of the operator response. When modeling the human-in-the-loop the dynamics of the vehicle, environment, and human perception-action are tightly coupled in space and time. The dynamic response of the overall system emerges from the interplay of perception and action. The main questions to be answered in this work are: i) what approach does the human operator implement when generating a control and guidance response? ii) how is information about the vehicle and environment extracted by the human? iii) can the gaze patterns of the pilot be decoded to provide information for estimation and control? In relation to existing research this work differs by focusing on fast acting dynamic systems in multiple dimensions and investigating how the gaze can be exploited to provide action-relevant information. To study human-in-the-loop systems the development and integration of the experimental infrastructure is described. Utilizing the infrastructure, a theoretical framework for computational modeling of the human pilot’s perception-action is proposed and verified experimentally. The benefits of the human visuo-motor model are demonstrated through application examples where the perceptual and control functions of a teleoperation system are augmented to reduce workload and provide a more natural human-machine interface

Automatic control of a multirotor

Objective of this thesis is to describe the design and realisation phases of a multirotor to be used for low risk and cost aerial observation. Starting point of this activity was a wide literature study related to the technological evolution of multirotors design and to the state of the art. Firstly the most common multirotor configurations were defined and, according to a size and performance based evaluation, the most suitable one was chosen. A detailed computer aided design model was drawn as basis for the realisation of two prototypes. The realised multirotors were “X-shaped” octorotors with eight coaxially coupled motors. The mathematical model of the multirotor dynamics was studied. “Proportional Integral Derivative” and “Linear Quadratic” algorithms were chosen as techniques to regulate the attitude dynamics of the multirotor. These methods were tested with a nonlinear model simulation developed in the Matlab Simulink environment. In the meanwhile the Arduino board was selected as the best compromise between costs and performance and the above mentioned algorithms were implemented using this platform thanks to its main characteristic of being completely “open source”. Indeed the multirotor was conceived to be a serviceable tool for the public utility and, at the same time, to be an accessible device for research and studies. The behaviour of the physical multirotor was evaluated with a test bench designed to isolate the rotation about one single body axis at a time. The data of the experimental tests were gathered in real time using a custom Matlab code and several indoor tests allowed the “fine tuning” of the controllers gains. Afterwards a portable “ground station” was conceived and realised in adherence with the real scenarios users needs. Several outdoor experimental flights were executed with successful results and the data gathered during the outdoor tests were used to evaluate some key performance indicators as the endurance and the maximum allowable payload mass. Then the fault tolerance of the control system was evaluated simulating and experimenting the loss of one motor; even in this critical condition the system exhibited an acceptable behaviour. The reached project readiness allowed to meet some potential users as the “Turin Fire Department” and to cooperate with them in a simulated emergency. During this event the multirotor was used to gather and transmit real time aerial images for an improved “situation awareness”. Finally the study was extended to more innovative control techniques like the neural networks based ones. Simulations results demonstrated their effectiveness; nevertheless the inherent complexity and the unreliability outside the training ranges could have a catastrophic impact on the airworthiness. This is a factor that cannot be neglected especially in the applications related to flying platforms. Summarising, this research work was addressed mainly to the operating procedures for implementing automatic control algorithms to real platforms. All the design aspects, from the preliminary multirotor configuration choice to the tests in possible real scenarios, were covered obtaining performances comparable with other commercial of-the-shelf platforms

Air Force Institute of Technology Research Report 2006

This report summarizes the research activities of the Air Force Institute of Technology’s Graduate School of Engineering and Management. It describes research interests and faculty expertise; lists student theses/dissertations; identifies research sponsors and contributions; and outlines the procedures for contacting the school. Included in the report are: faculty publications, conference presentations, consultations, and funded research projects. Research was conducted in the areas of Aeronautical and Astronautical Engineering, Electrical Engineering and Electro-Optics, Computer Engineering and Computer Science, Systems and Engineering Management, Operational Sciences, Mathematics, Statistics and Engineering Physics

Aerodynamic force interactions and measurements for micro quadrotors

Unmanned Aerial Vehicles (UAVs) have become mainstream through the success of several large commercial drone manufacturers. Quadrotors have been widely adopted due to their mechanical simplicity, ability to take off from a small area and hover at a fixed location. As these aircraft are increasingly being used in urban environments and indoors their ability to maintain stable flight in the presence of disturbances and nearby obstacles is of growing importance.Understanding the aerodynamics acting in these environments is the first step to improving quadrotor behaviour. This presents a challenge, as to characterise and verify models of the aerodynamic phenomena it is essential to collect numerous consistent experimental data points. On a typical quadrotor the motor response changes as the battery discharges, leading to variation in flight performance. Typically, this is addressed through the use high gain feedback control regulating attitude and position. To overcome this a unique voltage regulator for quadrotor power was developed to maintain constant supply voltage over the quadrotors flight. This enables the quadrotor to produce consistent and repeatable behaviour as the battery discharges.One way to improve the performance of quadrotors flying in constrained environments with limited sensing is to exploit aerodynamic effects for passive control and stability. Ground effect and rotor inflow damping are two effects of interest: ground effect provides a quadratic increase in thrust as a rotor moves closer to the ground; rotor inflow damping acts to resist axial motion by causing a change thrust opposing the movement. By canting the rotors of a quadrotor these effects were brought from the vertical axis into the lateral axis as well. A canted quadrotor flying over a v-shaped channel was modeled and found to exhibit passive stability in position. A demonstrator aircraft and v-shaped channel were tested in a number of configurations and shown to be stable for a channel slope of 10, 15 or 20 degrees with a rotor cant of 15 or 20 degrees.In order to observe more subtle aerodynamic effects, such as wall effect, it is necessary to have a method to measure rotor forces directly during quadrotor flight. Existing force torque sensors are too bulky, heavy, expensive or insensitive. To overcome these limitations a novel force torque sensor was developed that costs less than $50, weighs 3g and is capable of measuring sub mN forces. These sensors utilise an array of micro-electro-mechanical system (MEMS) barometers encapsulated in rubber to measure the strain field imparted by forces acting on the attached load plate. Mounting force torque sensors under the motors of a quadrotor allows the lateral rotor forces to be transmitted through the motor body and measured as torques at the base.Closely related to this, one of the key limitations faced by quadrotors is their inability to directly measure the airspeed of the aircraft. Providing an oncoming wind speed measurement will allow them to compensate for disturbances improving trajectory tracking and gust rejection. Blade flapping and induced drag are aerodynamic phenomena which relate lateral motion to a force acting in opposition to the rotors motion. By measuring this force using a rotor force sensor the airspeed of the aircraft is computed directly using induced drag and rotor blade flapping models. It was found that lateral velocity could be measured for the velocities tested, up to 1.5m/s, and showed a strong linear relationship to ground truth measurements.The work of this thesis has led to the development of: a quadrotor platform for consistent flight behaviour; a passive position-keeping quadrotor; and a novel rotor force sensor for direct measurement of quadrotor airspeed. These technologies open up avenues to improve the flight performance of quadrotors and better understand subtle aerodynamic interactions in flight