Thesis (MTech (Mechanical Engineering))--Cape Peninsula University of Technology, 2015. / Currently non-destructive testing techniques for composite aircraft structures are disadvantaged when compared to online Structural Health Monitoring (SHM) systems that monitor the structure while in-service and give real time data. The present research work looks at developing a protocol for online structural health monitoring of a UAV wing structure using PVDF film sensors, especially including the monitoring of structural changes caused by defects. Different types of SHM techniques were studied in relation to carbon fibre composites. Laminate composite make-up and manufacturing process was investigated and vacuum infusion process was used to manufacture the samples that resemble the Guardian II wing structure, then the three-point bending test was used to determine the material properties. Digital Shearography was employed as a stationery non-destructive technique to determine the sensor to structure attachment, type and position of defects that affect the state of performance. Finite Element Analysis (FEA) was done using ANSYS Workbench which served as a modelling tool using a drawing imported from Solid-works. Experimental investigation was done using PVDF sensor embedded on the surface of the sample in a cantilever setup and a vertical Vernier scale to measure the deflection due to impact and vibration loading. A Fluke-View oscilloscope was used as a data logger when the measurement of the output voltage and the natural frequency were recorded. The techniques of using FEA and experimental investigation were then compared. The findings of this study showed that the PVDF sensor is suitable for condition monitoring of a UAV wing structure.

The Boeing Joined-Wing SenorCraft is a novel aircraft design that has many potential benefits, especially for surveillance missions. However, computational studies have shown the potential for nonlinear structural responses in the joined-wing configuration due to aerodynamic loading that could result in aft wing buckling. The design, construction, and flight testing of a 1/9th scale, aeroelastically tuned model of the Joined-Wing SensorCraft has been the subject of an ongoing international collaboration aimed at experimentally demonstrating the nonlinear aeroelastic response in flight. To accurately measure and capture the configuration\'s potential for structural nonlinearity, the test article must exhibit equivalent structural flexibility and be designed to meet airworthiness standards. Previous work has demonstrated airworthiness through the successful flight of a Geometrically Scaled Remotely Piloted Vehicle. The work presented in this thesis involves evaluation of an aeroelastically tuned design through ground-based experimentation. The result of these experimental investigations has led to the conclusion that a full redesign of the forward and aft wings must be completed to demonstrate sufficient geometric nonlinearity for the follow-on Aeorelastically Tuned Remotely Piloted Vehicle. This thesis also presents flight test plans for the aeroelastically tuned RPV. / Master of Science

Thesis: M.S., Massachusetts Institute of Technology, Department of Mechanical Engineering, 1979 / Bibliography: leaves 38-39. / by Michio Takahashi. / M.S. / M.S. Massachusetts Institute of Technology, Department of Mechanical Engineering

Thesis (Ph. D.)--Joint Program in Applied Ocean Science and Engineering (Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science; and the Woods Hole Oceanographic Institution), 2012. / Cataloged from PDF version of thesis. / Includes bibliographical references (p. 118-131). / As analysis of imagery and environmental data plays a greater role in mission construction and execution, there is an increasing need for autonomous marine vehicles to transmit this data to the surface. Without access to the data acquired by a vehicle, surface operators cannot fully understand the state of the mission. Communicating imagery and high-resolution sensor readings to surface observers remains a significant challenge - as a result, current telemetry from free-roaming autonomous marine vehicles remains limited to 'heartbeat' status messages, with minimal scientific data available until after recovery. Increasing the challenge, long-distance communication may require relaying data across multiple acoustic hops between vehicles, yet fixed infrastructure is not always appropriate or possible. In this thesis I present an analysis of the unique considerations facing telemetry systems for free-roaming Autonomous Underwater Vehicles (AUVs) used in exploration. These considerations include high-cost vehicle nodes with persistent storage and significant computation capabilities, combined with human surface operators monitoring each node. I then propose mechanisms for interactive, progressive communication of data across multiple acoustic hops. These mechanisms include wavelet-based embedded coding methods, and a novel image compression scheme based on texture classification and synthesis. The specific characteristics of underwater communication channels, including high latency, intermittent communication, the lack of instantaneous end-to-end connectivity, and a broadcast medium, inform these proposals. Human feedback is incorporated by allowing operators to identify segments of data that warrant higher quality refinement, ensuring efficient use of limited throughput. I then analyze the performance of these mechanisms relative to current practices. Finally, I present CAPTURE, a telemetry architecture that builds on this analysis. CAPTURE draws on advances in compression and delay tolerant networking to enable progressive transmission of scientific data, including imagery, across multiple acoustic hops. In concert with a physical layer, CAPTURE provides an end-to- end networking solution for communicating science data from autonomous marine vehicles. Automatically selected imagery, sonar, and time-series sensor data are progressively transmitted across multiple hops to surface operators. Human operators can request arbitrarily high-quality refinement of any resource, up to an error-free reconstruction. The components of this system are then demonstrated through three field trials in diverse environments on SeaBED, OceanServer and Bluefin AUVs, each in different software architectures. / by Christopher Alden Murphy. / Ph.D.

The development and implementation of a flight test program for an unmanned aircraft is a multidisciplinary challenge. This thesis presents the development and implementation of a rigorous test program for the flight test of a Geometrically Scaled Joined Wing SensorCraft Remotely Piloted Vehicle from concept through successful flight test. The design methodology utilized in the development of the test program is presented, along with the extensive formal review process required for the approval of the test plan by the Air Force Research Laboratory. The design, development and calibration of a custom instrumentation package is also presented along with the setup, procedure and results from all testing. Results are presented for a wind tunnel test for air data boom calibration, propulsion system static thrust testing, a bifilar pendulum test for experimental calculation of mass moments of inertia, a static structural loading test for structural design validation, a full taxi test and a successful first flight. / Master of Science

Thesis (S.M.)--Joint Program in Oceanography/Applied Ocean Science and Engineering (Massachusetts Institute of Technology, Dept. of Mechanical Engineering; and the Woods Hole Oceanographic Institution), 2009. / Includes bibliographical references (p. 88-91). / The work presented in this thesis addresses the problem of the detection, classification and localization of seabed objects in shallow water environments using a time reversal approach in a bistatic configuration. The waveguide is insonified at low frequency ('kHz) with an omnidirectional source and the resulting scattered field is sampled by a receiving array towed behind an Autonomous Underwater Vehicle (AUV). The recorded signals are then processed to simulate onboard the AUV, the time reversed transmissions which serve to localize the origin of the scattered field on the seabed and estimate the position of the targets present. The clutter rejection based upon the analysis of the singular values of the Time Reversal operator is investigated with simulated data and field measurements collected off the coast of Palmaria (Italy) in January 2008. / by Alexis J. Dumortier. / S.M.

Thesis (S.M.)--Joint Program in Oceanography/Applied Ocean Science and Engineering (Massachusetts Institute of Technology, Dept. of Mechanical Engineering; and the Woods Hole Oceanographic Institution), 2008. / Includes bibliographical references (p. 159-170). / This thesis formulates an estimation framework for Simultaneous Localization and Mapping (SLAM) that addresses the problem of scalability in large environments. We describe an estimation-theoretic algorithm that achieves significant gains in computational efficiency while maintaining consistent estimates for the vehicle pose and the map of the environment.We specifically address the feature-based SLAM problem in which the robot represents the environment as a collection of landmarks. The thesis takes a Bayesian approach whereby we maintain a joint posterior over the vehicle pose and feature states, conditioned upon measurement data. We model the distribution as Gaussian and parametrize the posterior in the canonical form, in terms of the information (inverse covariance) matrix. When sparse, this representation is amenable to computationally efficient Bayesian SLAM filtering. However, while a large majority of the elements within the normalized information matrix are very small in magnitude, it is fully populated nonetheless. Recent feature-based SLAM filters achieve the scalability benefits of a sparse parametrization by explicitly pruning these weak links in an effort to enforce sparsity. We analyze one such algorithm, the Sparse Extended Information Filter (SEIF), which has laid much of the groundwork concerning the computational benefits of the sparse canonical form. The thesis performs a detailed analysis of the process by which the SEIF approximates the sparsity of the information matrix and reveals key insights into the consequences of different sparsification strategies. We demonstrate that the SEIF yields a sparse approximation to the posterior that is inconsistent, suffering from exaggerated confidence estimates. / (cont) This overconfidence has detrimental effects on important aspects of the SLAM process and affects the higher level goal of producing accurate maps for subsequent localization and path planning. This thesis proposes an alternative scalable filter that maintains sparsity while preserving the consistency of the distribution. We leverage insights into the natural structure of the feature-based canonical parametrization and derive a method that actively maintains an exactly sparse posterior. Our algorithm exploits the structure of the parametrization to achieve gains in efficiency, with a computational cost that scales linearly with the size of the map. Unlike similar techniques that sacrifice consistency for improved scalability, our algorithm performs inference over a posterior that is conservative relative to the nominal Gaussian distribution. Consequently, we preserve the consistency of the pose and map estimates and avoid the effects of an overconfident posterior. We demonstrate our filter alongside the SEIF and the standard EKEF both in simulation as well as on two real-world datasets. While we maintain the computational advantages of an exactly sparse representation, the results show convincingly that our method yields conservative estimates for the robot pose and map that are nearly identical to those of the original Gaussian distribution as produced by the EKF, but at much less computational expense. The thesis concludes with an extension of our SLAM filter to a complex underwater environment. We describe a systems-level framework for localization and mapping relative to a ship hull with an Autonomous Underwater Vehicle (AUV) equipped with a forward-looking sonar. The approach utilizes our filter to fuse measurements of vehicle attitude and motion from onboard sensors with data from sonar images of the hull. We employ the system to perform three-dimensional, 6-DOF SLAM on a ship hull. / by Matthew R. Walter. / S.M.

Le projet européen BACCHUS (impact of Biogenic versus Anthropogenic emissions on Clouds and Climate: towards a Holistic UnderStanding) porte sur les interactions aérosol-nuage. Les vitesses du vent vertical à proximité de la base des nuages et les spectres des noyaux de condensation des nuages (CCN) sont deux paramètres d'entrée importants pour les modèles de parcelle aérosol-nuage dans la détermination des propriétés microphysiques et optiques des nuages. Par conséquent, la présente étude se concentre sur le développement et la mise en oeuvre de mesures de vent atmosphérique afin d’améliorer les études de fermeture aérosolnuage. Les systèmes d'aéronefs pilotés à distance (RPAS) ont démontré leur potentiel en tant qu'outils pour la recherche atmosphérique dans l’étude de la couche limite, des aérosols et des nuages. Cependant, en tant qu'outil récent en recherche atmosphérique, le RPAS nécessite un développement instrumental pour répondre aux besoins d'observation actuels. Une sonde à 5 voies est développée pour une plateforme d'aéronef piloté à distance (RPA), assistée par un système de navigation inertiel (INS) pour obtenir les trois vecteurs du vent atmosphérique. La sonde à 5 voies est d'abord calibrée dans une soufflerie (à Météo-France, Toulouse, France), et une analyse d'erreur est effectuée sur la mesure du vent vertical. Les vecteurs de vent obtenus à partir de vols de RPA sont comparés à des vecteurs de vent déterminés à partir d'anémomètres soniques situés à différents niveaux d’un mât météorologique de 60 m (Centre de Recherches Atmosphériques, Lannemezan, France). Une bonne concordance entre les fonctions de densité de probabilité de la vitesse verticale du vent est obtenue. La densité spectrale de puissance des trois composantes du vent suit la ligne -5/3 en régime de turbulence établie (loi de Kolmogorov). Les valeurs d’énergie cinétique turbulente (TKE), calculées à partir du RPA, sont légèrement supérieures à celles de l'anémomètre sonique. Cependant, les résultats concordent avec ceux rapportés dans d'autres expériences comparant les plateformes RPAs à des anémomètres soniques (Lampert et al. (2016), Båserud et al. (2016)). Comme le RPA équipé d'une sonde à 5 voies (définie comme le ``wind-RPA'') est développé pour les observations aérosol-nuage, les vitesses verticales (updraft) près de la base des nuages sont comparées avec les données d’un radar de nuage au cours d'une campagne de mesures BACCHUS (Mace Head Research Station, Irlande). Trois études de cas illustrent la similitude des vitesses verticales dans les nuages mesurées par le wind-RPA et le radar de nuage. Une bonne concordance entre les vitesses verticales des deux instruments à travers différentes conditions météorologiques est établie. Les mesures de vitesse verticale du wind-RPA sont implémentées dans le modèle de parcelle aérosol nuage pour mener une étude de fermeture (campagne de mesures BACCHUS à Chypre). Les distributions de taille des aérosols et les CCN mesurés par un site au sol servent de paramètres d’entrée au modèle avec les vitesses verticales mesurées par le RPA. Le modèle de parcelle aérosol-nuage montre que l'entraînement dans les nuages a un impact plus important sur les propriétés optiques des nuages que la variabilité de la vitesse verticale et que la concentration en aérosols. Les résultats du cas d’étude de Chypre sont cohérents avec les résultats des études de fermeture similaires de la campagne de mesures à Mace Head (Sanchez et al., 2017) et renforcent l'importance d'inclure les processus d'entraînement dans les modèles de nuages pour réduire les incertitudes liées aux interactions aérosol-nuage. / The European project BACCHUS (impact of Biogenic versus Anthropogenic emissions on Clouds and Climate: towards a Holistic UnderStanding) focuses on aerosol-cloud interactions. Vertical wind velocities near cloud base, and cloud condensation nuclei (CCN) spectra, are the two most important input parameters for aerosol-cloud parcel models in determining cloud microphysical and optical properties. Therefore, the present study focuses on the instrumental development for vertical wind measurements to improve aerosol-cloud closure studies. Enhancements in Remotely Piloted Aircraft Systems (RPAS) have demonstrated their potential as tools in atmospheric research to study the boundary layer dynamics, aerosols and clouds. However, as a relatively new tool for atmospheric research, RPA require instrumental development and validation to address current observational needs. A 5-hole probe is implemented on a remotely piloted aircraft (RPA) platform, with an inertial navigation system (INS) to obtain atmospheric wind vectors. The 5- hole probe is first calibrated in a wind tunnel (at Météo-France, Toulouse, France), and an error analysis is conducted on the vertical wind measurement. Atmospheric wind vectors obtained from RPA flights are compared with wind vectors determined from sonic anemometers located at different levels on a 60 m meteorological mast (Centre de Recherches Atmosphériques, Lannemezan, France). Good agreements between vertical wind velocity probability density functions are obtained. The power spectral density of the three wind components follow the -5/3 line for the established regime of turbulence (Kolmogorov law). Turbulent kinetic energy (TKE) values calculated from the RPA are somewhat higher than TKE compared to the sonic anemometer; however, the results agree with those reported in other experiments that compare RPA platforms and sonic anemometers (Lampert et al. (2016), Båserud et al. (2016)). As the RPA equipped with a 5-hole probe (defined as the ``wind-RPA'') is developed for aerosol-cloud observations, updraft velocities near cloud base are compared with cloud radar data during a BACCHUS field campaign (Mace Head Research Station, Ireland). Three case studies illustrate the similarity of in-cloud updrafts measured between the wind-RPA and the cloud radar. A good agreement between vertical velocities of both instruments over a range of different meteorological conditions is found. Updraft velocity measurements from the wind-RPA are implemented in the aerosol-cloud parcel model to conduct a closure study for stratocumulus case with convection sampled during a BACCHUS field campaign in Cyprus. Aerosol size distributions and CCN were measured at a ground-site, which served as input to the aerosol-cloud parcel model along with the updraft velocities at cloud base measured by the RPA. In addition, the RPA conducted a vertical profile through the cloud layer and measured the shortwave transmission of solar irradiance during the ascent. The aerosol-cloud parcel model also shows that entrainment has a greater impact on cloud optical properties than variability in updraft velocity and aerosol particle concentration. Results of the case study for the Cyprus field experiment are consistent with results for similar closure studies conducted during the Mace Head field campaign (Sanchez et al., 2017), and reinforce the significance of including entrainment processes in cloud models to reduce uncertainties in aerosol-cloud interactions.

The objective of this thesis is to develop a general obstacle segmentation algorithm for use on board a ground based unmanned vehicle (GUV). The algorithm processes video data captured by a single monocular camera mounted on the GUV. We make the assumption that the GUV moves on a locally planar surface, representing the ground plane. We start by deriving the equations of the expected motion field (observed by the camera) induced by the motion of the robot on the ground plane. Given an initial view of a presumably static scene, this motion field is used to generate a predicted view of the same scene after a known camera displacement. This predicted image is compared to the actual image taken at the new camera location by means of an optical flow calculation. Because the planar assumption is used to generate the predicted image, portions of the image which mismatch the prediction correspond to salient feature points on objects which lie above or below the ground plane, we consider these objects obstacles for the GUV. We assume that these salient feature points (called seed pixels ) capture the color statistics of the obstacle and use them to initialize a Bayesian region growing routine to generate a full obstacle segmentation. Alignment of the seed pixels with the obstacle is not guaranteed due to the aperture problem, however successful segmentations were obtained for natural scenes. The algorithm was tested off line using video captured by a camera mounted on a GUV.

Thesis (Ph. D.)--Joint Program in Applied Ocean Science and Engineering (Massachusetts Institute of Technology, Dept. of Mechanical Engineering; and the Woods Hole Oceanographic Institution), 2009. / Includes bibliographical references (p. 275-284). / This research presents a parallel hypothesis method for autonomous underwater vehicle navigation that enables a vehicle to expand the operating envelope of existing long baseline acoustic navigation systems by incorporating information that is not normally used. The parallel hypothesis method allows the in-situ identification of acoustic multipath time-of-flight measurements between a vehicle and an external transponder and uses them in real-time to augment the navigation algorithm during periods when direct-path time-of-flight measurements are not available. A proof of concept was conducted using real-world data obtained by the Woods Hole Oceanographic Institution Deep Submergence Lab's Autonomous Benthic Explorer (ABE) and Sentry autonomous underwater vehicles during operations on the Juan de Fuca Ridge. This algorithm uses a nested architecture to break the navigation solution down into basic building blocks for each type of available external information. The algorithm classifies external information as either line of position or gridded observations. For any line of position observation, the algorithm generates a multi-modal block of parallel position estimate hypotheses. The multimodal hypotheses are input into an arbiter which produces a single unimodal output. If a priori maps of gridded information are available, they are used within the arbiter structure to aid in the elimination of false hypotheses. / (cont.) For the proof of concept, this research uses ranges from a single external acoustic transponder in the hypothesis generation process and grids of low-resolution bathymetric data from a ship-based multibeam sonar in the arbitration process. The major contributions of this research include the in-situ identification of acoustic multipath time-of-flight measurements, the multiscale utilization of a priori low resolution bathymetric data in a high-resolution navigation algorithm, and the design of a navigation algorithm with a flexible architecture. This flexible architecture allows the incorporation of multimodal beliefs without requiring a complex mechanism for real-time hypothesis generation and culling, and it allows the real-time incorporation of multiple types of external information as they become available in situ into the overall navigation solution. / by Cara Elizabeth Grupe LaPointe. / Ph.D.