Two Dimensional Positioning and Heading Solution for Flying Vehicles Using a Line-Scanning Laser Radar (LADAR)

Emerging technology in small autonomous flying vehicles requires the systems to have a precise navigation solution in order to perform tasks. In many critical environments, such as indoors, GPS is unavailable necessitating the development of supplemental aiding sensors to determine precise position. This research investigates the use of a line scanning laser radar (LADAR) as a standalone two dimensional position and heading navigation solution and sets up the device for augmentation into existing navigation systems. A fast histogram correlation method is developed to operate in real-time on board the vehicle providing position and heading updates at a rate of 10 Hz. LADAR navigation methods are adapted to 3 dimensions with a simulation built to analyze performance loss due attitude changes during flight. These simulations are then compared to experimental results collected using SICK LD-OEM 1000 mounted a cart traversing. The histogram correlation algorithm applied in this work was shown to successfully navigate a realistic environment where a quadrotor in short flights of less than 5 min in larger rooms. Application in hallways show great promise providing a stable heading along with tracking movement perpendicular to the hallway

A Survey of Offline and Online Learning-Based Algorithms for Multirotor UAVs

Multirotor UAVs are used for a wide spectrum of civilian and public domain applications. Navigation controllers endowed with different attributes and onboard sensor suites enable multirotor autonomous or semi-autonomous, safe flight, operation, and functionality under nominal and detrimental conditions and external disturbances, even when flying in uncertain and dynamically changing environments. During the last decade, given the faster-than-exponential increase of available computational power, different learning-based algorithms have been derived, implemented, and tested to navigate and control, among other systems, multirotor UAVs. Learning algorithms have been, and are used to derive data-driven based models, to identify parameters, to track objects, to develop navigation controllers, and to learn the environment in which multirotors operate. Learning algorithms combined with model-based control techniques have been proven beneficial when applied to multirotors. This survey summarizes published research since 2015, dividing algorithms, techniques, and methodologies into offline and online learning categories, and then, further classifying them into machine learning, deep learning, and reinforcement learning sub-categories. An integral part and focus of this survey are on online learning algorithms as applied to multirotors with the aim to register the type of learning techniques that are either hard or almost hard real-time implementable, as well as to understand what information is learned, why, and how, and how fast. The outcome of the survey offers a clear understanding of the recent state-of-the-art and of the type and kind of learning-based algorithms that may be implemented, tested, and executed in real-time.Comment: 26 pages, 6 figures, 4 tables, Survey Pape

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

Real-time video stabilization without phantom movements for micro aerial vehicles

In recent times, micro aerial vehicles (MAVs) are becoming popular for several applications as rescue, surveillance, mapping, etc. Undesired motion between consecutive frames is a problem in a video recorded by MAVs. There are different approaches, applied in video post-processing, to solve this issue. However, there are only few algorithms able to be applied in real time. An additional and critical problem is the presence of false movements in the stabilized video. In this paper, we present a new approach of video stabilization which can be used in real time without generating false movements. Our proposal uses a combination of a low-pass filter and control action information to estimate the motion intention.Peer ReviewedPostprint (published version

Autonomous Navigation in Complex Indoor and Outdoor Environments with Micro Aerial Vehicles

Micro aerial vehicles (MAVs) are ideal platforms for surveillance and search and rescue in confined indoor and outdoor environments due to their small size, superior mobility, and hover capability. In such missions, it is essential that the MAV is capable of autonomous flight to minimize operator workload. Despite recent successes in commercialization of GPS-based autonomous MAVs, autonomous navigation in complex and possibly GPS-denied environments gives rise to challenging engineering problems that require an integrated approach to perception, estimation, planning, control, and high level situational awareness. Among these, state estimation is the first and most critical component for autonomous flight, especially because of the inherently fast dynamics of MAVs and the possibly unknown environmental conditions. In this thesis, we present methodologies and system designs, with a focus on state estimation, that enable a light-weight off-the-shelf quadrotor MAV to autonomously navigate complex unknown indoor and outdoor environments using only onboard sensing and computation. We start by developing laser and vision-based state estimation methodologies for indoor autonomous flight. We then investigate fusion from heterogeneous sensors to improve robustness and enable operations in complex indoor and outdoor environments. We further propose estimation algorithms for on-the-fly initialization and online failure recovery. Finally, we present planning, control, and environment coverage strategies for integrated high-level autonomy behaviors. Extensive online experimental results are presented throughout the thesis. We conclude by proposing future research opportunities

System Design Methodology and Implementation of Micro Aerial Vehicles

Ph.DDOCTOR OF PHILOSOPH

Motion Planning For Micro Aerial Vehicles

A Micro Aerial Vehicle (MAV) is capable of agile motion in 3D making it an ideal platform for developments of planning and control algorithms. For fully autonomous MAV systems, it is essential to plan motions that are both dynamically feasible and collision-free in cluttered environments. Recent work demonstrates precise control of MAVs using time-parameterized trajectories that satisfy feasibility and safety requirements. However, planning such trajectories is non-trivial, especially when considering constraints, such as optimality and completeness. For navigating in unknown environments, the capability for fast re-planning is also critical. Considering all of these requirements, motion planning for MAVs is a challenging problem. In this thesis, we examine trajectory planning algorithms for MAVs and present methodologies that solve a wide range of planning problems. We first introduce path planning and geometric control methods, which produce spatial paths that are inadequate for high speed flight, but can be used to guide trajectory optimization. We then describe optimization-based trajectory planning and demonstrate this method for solving navigation problems in complex 3D environments. When the initial state is not fixed, an optimization-based method is prone to generate sub-optimal trajectories. To address this challenge, we propose a search-based approach using motion primitives to plan resolution complete and sub-optimal trajectories. This algorithm can also be used to solve planning problems with constraints such as motion uncertainty, limited field-of-view and moving obstacles. The proposed methods can run in real time and are applicable for real-world autonomous navigation, even with limited on-board computational resources. This thesis includes a carefully analysis of the strengths and weaknesses of our planning paradigm and algorithms, and demonstration of their performance through simulation and experiments

Grasping, Perching, And Visual Servoing For Micro Aerial Vehicles

Micro Aerial Vehicles (MAVs) have seen a dramatic growth in the consumer market because of their ability to provide new vantage points for aerial photography and videography. However, there is little consideration for physical interaction with the environment surrounding them. Onboard manipulators are absent, and onboard perception, if existent, is used to avoid obstacles and maintain a minimum distance from them. There are many applications, however, which would benefit greatly from aerial manipulation or flight in close proximity to structures. This work is focused on facilitating these types of close interactions between quadrotors and surrounding objects. We first explore high-speed grasping, enabling a quadrotor to quickly grasp an object while moving at a high relative velocity. Next, we discuss planning and control strategies, empowering a quadrotor to perch on vertical surfaces using a downward-facing gripper. Then, we demonstrate that such interactions can be achieved using only onboard sensors by incorporating vision-based control and vision-based planning. In particular, we show how a quadrotor can use a single camera and an Inertial Measurement Unit (IMU) to perch on a cylinder. Finally, we generalize our approach to consider objects in motion, and we present relative pose estimation and planning, enabling tracking of a moving sphere using only an onboard camera and IMU

A hierarchical neuro-evolutionary approach to small quadrotor control

Quadrotors are unique among Micro Aerial Vehicles in providing excellent maneuverability (as opposed to winged flight),while maintaing a simple mechanical construction (as opposed to helicopters). This mechanical simplicity comes at a cost of increased controller complexity. Quadrotors are inherently unstable, in the sense that they are essentially unflyable by a human without stability assistance. Prior work has developed model-based controllers that successfully control quadrotors operating near hover conditions, but small quadrotor dynamics make such control more difficult. In this thesis,we present a hierarchical neuro-controller for small (0.5 kg) quadrotor control. The first stage of control aims to stabilize the craft and outputs rotor speeds based on a requested attitude (pitch, roll, yaw, and vertical velocity). This controller is developed in four parts around each of the variables, initially training them to achieve results similar to a PID controller. The four parts are then combined and the controller is trained further to increase robustness. The second stage of control aims to achieve a requested(x,y,z) position by providing the first stage with the appropriate attitude. The simulation results show that stable quadrotor control is achieved through this control architecture. In addition, the results show that the hierarchical control approach recovers from discrete angle disturbances over an order of magnitude faster than a basic PID controller, and can even recover from a disturbance that knocks the craft upside down. Finally, using the average volume used to maintain a stable hover, we show that the hierarchical controller provides stable flight in the presence of 5 times more sensor noise and 8 times more actuator noise as compared to the PID controller