Optimal Multi-UAV Trajectory Planning for Filming Applications

Teams of multiple Unmanned Aerial Vehicles (UAVs) can be used to record large-scale outdoor scenarios and complementary views of several action points as a promising system for cinematic video recording. Generating the trajectories of the UAVs plays a key role, as it should be ensured that they comply with requirements for system dynamics, smoothness, and safety. The rise of numerical methods for nonlinear optimization is finding a ourishing field in optimization-based approaches to multi- UAV trajectory planning. In particular, these methods are rather promising for video recording applications, as they enable multiple constraints and objectives to be formulated, such as trajectory smoothness, compliance with UAV and camera dynamics, avoidance of obstacles and inter-UAV con icts, and mutual UAV visibility. The main objective of this thesis is to plan online trajectories for multi-UAV teams in video applications, formulating novel optimization problems and solving them in real time. The thesis begins by presenting a framework for carrying out autonomous cinematography missions with a team of UAVs. This framework enables media directors to design missions involving different types of shots with one or multiple cameras, running sequentially or concurrently. Second, the thesis proposes a novel non-linear formulation for the challenging problem of computing optimal multi-UAV trajectories for cinematography, integrating UAV dynamics and collision avoidance constraints, together with cinematographic aspects such as smoothness, gimbal mechanical limits, and mutual camera visibility. Lastly, the thesis describes a method for autonomous aerial recording with distributed lighting by a team of UAVs. The multi-UAV trajectory optimization problem is decoupled into two steps in order to tackle non-linear cinematographic aspects and obstacle avoidance at separate stages. This allows the trajectory planner to perform in real time and to react online to changes in dynamic environments. It is important to note that all the methods in the thesis have been validated by means of extensive simulations and field experiments. Moreover, all the software components have been developed as open source.Los equipos de vehículos aéreos no tripulados (UAV) son sistemas prometedores para grabar eventos cinematográficos, en escenarios exteriores de grandes dimensiones difíciles de cubrir o para tomar vistas complementarias de diferentes puntos de acción. La generación de trayectorias para este tipo de vehículos desempeña un papel fundamental, ya que debe garantizarse que se cumplan requisitos dinámicos, de suavidad y de seguridad. Los enfoques basados en la optimización para la planificación de trayectorias de múltiples UAVs se pueden ver beneficiados por el auge de los métodos numéricos para la resolución de problemas de optimización no lineales. En particular, estos métodos son bastante prometedores para las aplicaciones de grabación de vídeo, ya que permiten formular múltiples restricciones y objetivos, como la suavidad de la trayectoria, el cumplimiento de la dinámica del UAV y de la cámara, la evitación de obstáculos y de conflictos entre UAVs, y la visibilidad mutua. El objetivo principal de esta tesis es planificar trayectorias para equipos multi-UAV en aplicaciones de vídeo, formulando novedosos problemas de optimización y resolviéndolos en tiempo real. La tesis comienza presentando un marco de trabajo para la realización de misiones cinematográficas autónomas con un equipo de UAVs. Este marco permite a los directores de medios de comunicación diseñar misiones que incluyan diferentes tipos de tomas con una o varias cámaras, ejecutadas de forma secuencial o concurrente. En segundo lugar, la tesis propone una novedosa formulación no lineal para el difícil problema de calcular las trayectorias óptimas de los vehículos aéreos no tripulados en cinematografía, integrando en el problema la dinámica de los UAVs y las restricciones para evitar colisiones, junto con aspectos cinematográficos como la suavidad, los límites mecánicos del cardán y la visibilidad mutua de las cámaras. Por último, la tesis describe un método de grabación aérea autónoma con iluminación distribuida por un equipo de UAVs. El problema de optimización de trayectorias se desacopla en dos pasos para abordar los aspectos cinematográficos no lineales y la evitación de obstáculos en etapas separadas. Esto permite al planificador de trayectorias actuar en tiempo real y reaccionar en línea a los cambios en los entornos dinámicos. Es importante señalar que todos los métodos de la tesis han sido validados mediante extensas simulaciones y experimentos de campo. Además, todos los componentes del software se han desarrollado como código abierto

Mixed-reality for unmanned aerial vehicle operations in near earth environments

Future applications will bring unmanned aerial vehicles (UAVs) to near Earth environments such as urban areas, causing a change in the way UAVs are currently operated. Of concern is that UAV accidents still occur at a much higher rate than the accident rate for commercial airliners. A number of these accidents can be attributed to a UAV pilot's low situation awareness (SA) due to the limitations of UAV operating interfaces. The main limitation is the physical separation between the vehicle and the pilot. This eliminates any motion and exteroceptive sensory feedback to the pilot. These limitation on top of a small eld of view from the onboard camera results in low SA, making near Earth operations di cult and dangerous. Autonomy has been proposed as a solution for near Earth tasks but state of the art arti cial intelligence still requires very structured and well de ned goals to allow safe autonomous operations. Therefore, there is a need to better train pilots to operate UAVs in near Earth environments and to augment their performance for increased safety and minimization of accidents.In this work, simulation software, motion platform technology, and UAV sensor suites were integrated to produce mixed-reality systems that address current limitations of UAV piloting interfaces. The mixed reality de nition is extended in this work to encompass not only the visual aspects but to also include a motion aspect. A training and evaluation system for UAV operations in near Earth environments was developed. Modi cations were made to ight simulator software to recreate current UAV operating modalities (internal and external). The training and evaluation system has been combined with Drexel's Sensor Integrated Systems Test Rig (SISTR) to allow simulated missions while incorporating real world environmental e ects andUAV sensor hardware.To address the lack of motion feedback to a UAV pilot, a system was developed that integrates a motion simulator into UAV operations. The system is designed such that during ight, the angular rate of a UAV is captured by an onboard inertial measurement unit (IMU) and is relayed to a pilot controlling the vehicle from inside the motion simulator.Efforts to further increase pilot SA led to the development of a mixed reality chase view piloting interface. Chase view is similar to a view of being towed behind the aircraft. It combines real world onboard camera images with a virtual representation of the vehicle and the surrounding operating environment. A series of UAV piloting experiments were performed using the training and evaluation systems described earlier. Subjects' behavioral performance while using the onboard camera view and the mixed reality chase view interface during missions was analyzed. Subjects' cognitive workload during missions was also assessed using subjective measures such as NASA task load index and non-subjective brain activity measurements using a functional Infrared Spectroscopy (fNIR) system. Behavioral analysis showed that the chase view interface improved pilot performance in near Earth ights and increased their situational awareness. fNIR analysis showed that a subjects cognitive workload was signi cantly less while using the chase view interface. Real world ight tests were conducted in a near Earth environment with buildings and obstacles to evaluate the chase view interface with real world data. The interface performed very well with real world, real time data in close range scenarios.The mixed reality approaches presented follow studies on human factors performance and cognitive loading. The resulting designs serve as test beds for studying UAV pilot performance, creating training programs, and developing tools to augment UAV operations and minimize UAV accidents during operations in near Earth environments.Ph.D., Mechanical Engineering -- Drexel University, 201

Effects of Visual Interaction Methods on Simulated Unmanned Aircraft Operator Situational Awareness

The limited field of view of static egocentric visual displays employed in unmanned aircraft controls introduces the soda straw effect on operators, which significantly affects their ability to capture and maintain situational awareness by not depicting peripheral visual data. The problem with insufficient operator situational awareness is the resulting increased potential for error and oversight during operation of unmanned aircraft, leading to accidents and mishaps costing United States taxpayers between $4 million to$54 million per year. The purpose of this quantitative experimental completely randomized design study was to examine and compare use of dynamic eyepoint to static visual interaction in a simulated stationary egocentric environment to determine which, if any, resulted in higher situational awareness. The theoretical framework for the study established the premise that the amount of visual information available could affect the situational awareness of an operator and that increasing visual information through dynamic eyepoint manipulation may result in higher situational awareness than static visualization. Four experimental dynamic visual interaction methods were examined (analog joystick, head tracker, uninterrupted hat/point of view switch, and incremental hat/point of view switch) and compared to a single static method (the control treatment). The five methods were used in experimental testing with 150 participants to determine if the use of a dynamic eyepoint significantly increased the situational awareness of a user within a stationary egocentric environment, indicating that employing dynamic control would reduce the occurrence or consequences of the soda straw effect. The primary difference between the four dynamic visual interaction methods was their unique manipulation approaches to control the pitch and yaw of the simulated eyepoint. The identification of dynamic visual interaction increasing user SA may lead to the further refinement of human-machine-interface (HMI), teleoperation, and unmanned aircraft control principles, with the pursuit and performance of related research

The Underpinnings of Workload in Unmanned Vehicle Systems

This paper identifies and characterizes factors that contribute to operator workload in unmanned vehicle systems. Our objective is to provide a basis for developing models of workload for use in design and operation of complex human-machine systems. In 1986, Hart developed a foundational conceptual model of workload, which formed the basis for arguably the most widely used workload measurement techniquethe NASA Task Load Index. Since that time, however, there have been many advances in models and factor identification as well as workload control measures. Additionally, there is a need to further inventory and describe factors that contribute to human workload in light of technological advances, including automation and autonomy. Thus, we propose a conceptual framework for the workload construct and present a taxonomy of factors that can contribute to operator workload. These factors, referred to as workload drivers, are associated with a variety of system elements including the environment, task, equipment and operator. In addition, we discuss how workload moderators, such as automation and interface design, can be manipulated in order to influence operator workload. We contend that workload drivers, workload moderators, and the interactions among drivers and moderators all need to be accounted for when building complex, human-machine systems

Tracking and Grasping of Moving Objects Using Aerial Robotic Manipulators: A Brief Survey

Unmanned Aerial Vehicles (UAV) has evolved in recent years, their features have changed to be more useful to the society, although some years ago the drones had been thought to be teleoperated by humans and to take some pictures from above, which is useful; nevertheless, nowadays the drones are capable of developing autonomous tasks like tracking a dynamic target or even grasping different kind of objects. Some task like transporting heavy loads or manipulating complex shapes are more challenging for a single UAV, but for a fleet of them might be easier. This brief survey presents a compilation of relevant works related to tracking and grasping with aerial robotic manipulators, as well as cooperation among them. Moreover, challenges and limitations are presented in order to contribute with new areas of research. Finally, some trends in aerial manipulation are foreseeing for different sectors and relevant features for these kind of systems are standing out

System Architectures for Cooperative Teams of Unmanned Aerial Vehicles Interacting Physically with the Environment

Unmanned Aerial Vehicles (UAVs) have become quite a useful tool for a wide range of applications, from inspection & maintenance to search & rescue, among others. The capabilities of a single UAV can be extended or complemented by the deployment of more UAVs, so multi-UAV cooperative teams are becoming a trend. In that case, as di erent autopilots, heterogeneous platforms, and application-dependent software components have to be integrated, multi-UAV system architectures that are fexible and can adapt to the team's needs are required. In this thesis, we develop system architectures for cooperative teams of UAVs, paying special attention to applications that require physical interaction with the environment, which is typically unstructured. First, we implement some layers to abstract the high-level components from the hardware speci cs. Then we propose increasingly advanced architectures, from a single-UAV hierarchical navigation architecture to an architecture for a cooperative team of heterogeneous UAVs. All this work has been thoroughly tested in both simulation and eld experiments in di erent challenging scenarios through research projects and robotics competitions. Most of the applications required physical interaction with the environment, mainly in unstructured outdoors scenarios. All the know-how and lessons learned throughout the process are shared in this thesis, and all relevant code is publicly available.Los vehículos aéreos no tripulados (UAVs, del inglés Unmanned Aerial Vehicles) se han convertido en herramientas muy valiosas para un amplio espectro de aplicaciones, como inspección y mantenimiento, u operaciones de rescate, entre otras. Las capacidades de un único UAV pueden verse extendidas o complementadas al utilizar varios de estos vehículos simultáneamente, por lo que la tendencia actual es el uso de equipos cooperativos con múltiples UAVs. Para ello, es fundamental la integración de diferentes autopilotos, plataformas heterogéneas, y componentes software -que dependen de la aplicación-, por lo que se requieren arquitecturas multi-UAV que sean flexibles y adaptables a las necesidades del equipo. En esta tesis, se desarrollan arquitecturas para equipos cooperativos de UAVs, prestando una especial atención a aplicaciones que requieran de interacción física con el entorno, cuya naturaleza es típicamente no estructurada. Primero se proponen capas para abstraer a los componentes de alto nivel de las particularidades del hardware. Luego se desarrollan arquitecturas cada vez más avanzadas, desde una arquitectura de navegación para un único UAV, hasta una para un equipo cooperativo de UAVs heterogéneos. Todo el trabajo ha sido minuciosamente probado, tanto en simulación como en experimentos reales, en diferentes y complejos escenarios motivados por proyectos de investigación y competiciones de robótica. En la mayoría de las aplicaciones se requería de interacción física con el entorno, que es normalmente un escenario en exteriores no estructurado. A lo largo de la tesis, se comparten todo el conocimiento adquirido y las lecciones aprendidas en el proceso, y el código relevante está publicado como open-source

Design, Deployment, Navigation, and Control of Mobile Robots for Perception and Sensor Data Collection

Aerial robots, including rotary-wing and fixed-wing unmanned aerial vehicles or UAVs, have shown great capabilities in surveying as well as search and rescue from above. However, either rotary-wing or fixed-wing UAVs have nearly insoluble flaws. In order to overcome the under-actuating nature of multi-rotor UAVs, Chapter 2 proposes modeling methods and control schemes for fully-actuated hexacopters. Additionally, rotary-wing robots suffer from limited battery life as well as lack of fail-safe mechanism upon losing motors, while fixed-wing robots lacks the ability to take off and land vertically. Therefore, Chapter 4 proposes a bio-inpired hybrid aerial robot to extend mutli-rotor flight time and fail-safe capability and provide fixed-wing glider with vertical take-off and landing or VTOL capability. Moreover, to extend the flight time and optimize the energy consumption of multi-rotor UAVs, Chapter 3 proposes a multi-disciplinary design optimization based flight trajectory optimizer involving linear rotor inflow models to reduce flight time or energy consumption of specific missions.In terms of unmanned ground vehicles or UGVs used for perception and mapping, there has been a research gap to provide a low-cost, highly agile over-actuated chassis design. Chapter 5 proposes a 3D-printable double Ackermann steering chassis design with 2-wheel standing and balancing capability to fill in this gap. Chapter 6, on the other hand, proposes the system design of a UGV capable of performing perception and mapping in a limited lighting, unstructured, and GPS-denied environment based on a nevertheless nonholonomic chassis, where primary concern becomes the reliability in performing real-time mapping and preservation of solely static environment.The last but not least topic discussed in this dissertation is to promote the role of UAV imagery in earthquake response. In Chapter 7 we combine the traditional UAV plan view perspective with north and east elevation view video data to provide motion estimation in all 6 degrees of freedom, as well as proposing Video Transformer for motion tracking.All in all, with attempts to expand and promote the designs, deployment and control schemes of both aerial and ground mobile robots, this dissertation strives to provide case study results and state-of-the-art methods for future robotics studies

Sensitivity Study for UAV GPS-Denied Navigation in Uncertain Landmark Fields

This document provides two 2D simulation sensitivity analyses regarding a drone’s flight characteristic (state) errors within a GPS-denied region. The research focuses on a development and investigation of utilizing a camera to simultaneously determine a drone’s state while locating landmarks, where there is uncertainty in the landmarks’ exact positions prior to the mission (SLAM). This SLAM method is performed in regions with limited access to GPS. Furthermore, there is development and investigation of controlling the drone in conjunction with SLAM using potential error-reducing control parameters. Objectives are to quantitatively understand the UAV’s sensitivity of position errors to sensor grade and landmark characteristics as well as sensitivity of position errors to tuned control parameters

Design and Simulation of a Neuroevolutionary Controller for a Quadcopter Drone

The problem addressed in the present paper is the design of a controller based on an evolutionary neural network for autonomous flight in quadrotor systems. The controller's objective is to govern the quadcopter in such a way that it reaches a specific position, bearing on attitude limitations during flight and upon reaching a target. Given the complex nature of quadcopters, an appropriate neural network architecture and a training algorithm were designed to guide a quadcopter toward a target. The designed controller was implemented as a single multi-layer perceptron. On the basis of the quadcopter's current state, the developed neurocontroller produces the correct rotor speed values, optimized in terms of both attitude-limitation compliance and speed. The neural network training was completed using a custom evolutionary algorithm whose design put particular emphasis on the cost function's definition. The developed neurocontroller was tested in simulation to drive a quadcopter to autonomously follow a complex path. The obtained simulated results show that the neurocontroller manages to effortlessly follow several types of paths with adequate precision while maintaining low travel times