Invariant manifolds, discrete mechanics, and trajectory design for a mission to Titan

With an environment comparable to that of primordial Earth, a surface strewn with liquid hydrocarbon lakes, and an atmosphere denser than that of any other moon in the solar system, Saturn's largest moon Titan is a treasure trove of potential scientific discovery and is the target of a proposed NASA mission scheduled for launch in roughly one decade. A chief consideration associated with the design of any such mission is the constraint imposed by fuel limitations that accompany the spacecraft's journey between celestial bodies. In this study, we explore the use of patched three-body models in conjunction with a discrete mechanical optimization algorithm for the design of a fuel-efficient Saturnian moon tour focusing on Titan. In contrast to the use of traditional models for trajectory design such as the patched conic approximation, we exploit subtleties of the three-body problem, a classic problem from celestial mechanics that asks for the motion of three masses in space under mutual gravitational interaction, in order to slash fuel costs. In the process, we demonstrate the aptitude of the DMOC (Discrete Mechanics and Optimal Control) optimization algorithm in handling celestial mechanical trajectory optimization problems

Hyperspectral unmixing: a theoretical aspect and applications to CRISM data processing

Hyperspectral imaging has been deployed in earth and planetary remote sensing, and has contributed the development of new methods for monitoring the earth environment and new discoveries in planetary science. It has given scientists and engineers a new way to observe the surface of earth and planetary bodies by measuring the spectroscopic spectrum at a pixel scale. Hyperspectal images require complex processing before practical use. One of the important goals of hyperspectral imaging is to obtain the images of reflectance spectrum. A raw image obtained by hyperspectral remote sensing usually undergoes conversion to a physical quantity representing the intensity of light energy, called radiance. In order to obtain the reflectance spectrum of surface, the contribution of atmosphere needs to be addressed and then divided by a spectrum of ``white reference.\u27\u27 Furthermore, the obtained reflectance spectra of image pixels are likely to be the mixtures of multiple species due to limited spatial resolution from orbits around planets. Hyperspectral unmixing is an attempt to unmix those pixels - to identify substantial components and estimate their fractional abundances. Hyperspectral unmixing has been widely explored in the literature, but there are still many aspects yet to be studied. The majority of research focuses on the development of methods to retrieve correct substantial components and accurate fractional abundances. Their theoretical aspects are rarely investigated. Chapter 2 will pursue a theoretical aspect of sparse unmixing, one of the hyperspectral unmixing problems and derive its theoretical conditions that guarantee the correct identification of substantial components. Hyperspectral unmixing can also be used for other stages of hyperspectral data processing. Chapter 3 explores the application of hyperspectral unmixing to the processing of hyperspectral image acquired by the Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) onboard the Mars Reconnaissance Orbiter (MRO). In particular, new atmospheric correction and de-noising methods for the CRISM data that use a hyperspectral unmixing to model surface spectra, are introduced. The new methods remove most of the problematic systematic artifacts present in CRISM images and significantly improve signal quality. Chapter 4 investigates how hyperspectral images acquired from orbits can be combined with ground exploration. In the recent rush of the launch of many Martian ground rover missions, it is important to effectively integrate knowledge obtained by hyperspectral remote sensing from orbits into ground exploration for facilitating Martian exploration. In specific, this dissertation solves the problem of matching hyperspectral image pixels obtained by the CRISM with ground mega-pixel images acquired by the Mast Camera (Mastcam) installed on the Curiosity rover on Mars. A new systematic methodology to map the CRISM and Mastcam images onto high resolution surface topography is developed

Integrated recovery of elevation and photometric reflectance properties from hyperspectral data

The analysis of optical measurements, i.e. images, may be subdivided into methods with respect to the spatial reflectance distribution, e.g. bundle adjustment and shape from shading, and methods with respect to the spectral reflectance distribution, e.g. determination of object properties based on its colour. Current research considers these problems separately. Hyperspectral imagery, however, simultaneously provides knowledge on the local surface topography, i.e. shading, and the spectral reflectance. One problem that requires treatment in all methods is the dependence of the object's appearance on its shape. The goal of this thesis is to bridge the gap between spatial and spectral analysis of reflectance data, i.e. to extract and combine the spatial and the spectral information from hyperspectral images. This is achieved by an integrated framework for the recovery of local surface topography and the normalisation of spectral data. Photometric shape recovery methods derive the surface orientation, i.e. its gradient field, from the image and retrieve the shape by integrating the estimated gradient field, which is prone to the accumulation of systematic errors originating from the gradient estimation. To suppress these systematic errors, the photometric shape recovery is re-stricted by soft constraints derived from topographic models of lower lateral resolution. These soft constraints are applied to both the gradient field estimation and the gradient field integration. The earth's moon has been of scientific interest for a long time and thus a wealth of measurements exists and is publicly available. The available measurements include high resolution topography models derived from stereo image analysis and laser altimeter measurements, hyperspectral reflec-tance measurements and elemental abundances measured by gamma ray spectrometers. This wealth of data is rarely met in industrial applications and thus the lunar surface is an ideal object for the method development. The developed methods include the refinement, i.e. increase of lateral resolution, of stereo based topographic model and the estimation of the surface's temperature and the parameters of the reflectance model. The computed values allow for a normalisation of the spec-tral data and compensation of the thermal component. The developed techniques are applied to derive a near-global Moon Mineralogy Mapper mosaic. Based on this mosaic, a regression method is applied to map parameters of the spectral absorption bands onto elemental abundances measured by the Lunar Prospector Gamma-Ray Spectrometer. To obtain co-registered images, which are required for an analysis of the spectral data, an illumination independent image registration method is developed based on the recovered elevation models, which, by definition, are co-registered to the original image. Finally, the photometric surface refine-ment methods are applied to Lunar Orbiter Narrow Angle Camera images to derive to elevation models of the highest possible resolution. The results show that the influence of the local topography is nearly eliminated from the normalised reflectance maps. A qualitative analysis of the obtained parameters of the reflectance model, e.g. the single-scattering albedo, is in good agreement with known bright and dark areas, e.g. bright volcanic domes or ash deposits. An analysis of the temperature estimation shows, that accurate estimates of temperatures above 300 K are possible. Comparing the results of the refined topographic models to single high accuracy laser altimeter measurements show that the depth error is comparable to stereo analysis while the lateral resolution is greatly increased. The presented image registration technique based on the topography models achieves sub-pixel accuracy.Die Analyse von optischen Messungen, d. h. Bildern, kann in zwei Gruppen von Methoden eingeteilt werden: Die Analyse der räumlichen Reflektanzverteilung, z. B. Bündelausgleich, und die Analyse der spektralen Reflektanz, z. B. die Bestimmung von Objekteigenschaften auf Basis der Objektfarbe. Übli-cherweise werden beide Methoden getrennt entwickelt. Allerdings ist allen Methoden die Grund-problematik der Abhängigkeit der gemessenen Reflektanz von der Objektform gemein. Ziel dieser Arbeit ist es beide Gruppen zu vereinen und die für die Normalisierung der spektralen Reflektanz geschätzten Reflektanzfunktion für die photometrische Bestimmung der Objektform, z. B. mittels Shape-from-Shading, zu nutzen. Photometrische Methoden nutzen die Abhängigkeit der Objekt-reflektanz von seiner Form um die Orientierung, d. h. das Gradientenfeld, der Objektoberfläche zu bestimmen. In einem zweiten Schritt wird die Form durch Integration des Gradientenfeldes ermittelt. Daher sind photometrische Methoden anfällig für die Akkumulation kleiner, systematischer Fehler bei der Bestimmung des Gradientenfeldes. Um diese Effekte zu beseitigen wird die photometrische Bestimmung der Oberfläche durch Oberflächendaten mit geringerer lateraler Auflösung einge-schränkt. Dies geschieht durch das Einführen sogenannter Soft-Constraints in beiden Stufen der pho-tometrischen Rekonstruktion. Der Mond ist schon lange Forschungsgegenstand und so ist ein gewaltiger Fundus an Daten der Öf-fentlichkeit zugänglich. Diese Messdaten sind z. B. Topographiemodelle aus Stereobildanalyse und Laser Altimetrie, Hyperspektraldaten sowie mittels Gammastrahlenspektroskopie ermittelte Ele-menthäufigkeiten. Daher bildet der Mond ein ideales Testobjekt für die Entwicklung von Methoden. Diese beinhalten die Erhöhung der lateralen Auflösung von topographischen Modellen durch photo-metrische Methoden sowie die Ermittlung der Oberflächentemperatur und der Reflektanzmodell-parameter. Die ermittelten Größen ermöglichen eine Normalisierung der spektralen Reflektanz und eine Kompensation der von der Oberfläche abgestrahlten thermischen Komponente. Die Entwickelten Methoden werden genutzt um ein nahezu globales Moon Mineralogy Mapper Mo-saik zu normalisieren. Ausgehend von dem normalisierten Mosaik wird eine Regressionsmethode genutzt um die spektralen Absorptionen, welche durch bestimmte Elemente in den Mineralen er-zeugt werden, auf die Elementhäufigkeitskarten des Lunar Prospector Gamma-Ray Spectrometers abzubilden. Weil für die Analyse pixelsynchrone Bilder erforderlich sind wird eine robuste und be-leuchtungsunabhängige Bildregistrierungsmethode aus den entwickelten Methoden abgeleitet. Au-ßerdem werden die entwickelten photometrischen Methoden genutzt um aus Lunar Orbiter Narrow Angle Camera Bildern topographische Modelle mit der höchstmöglichen Auflösung zu erzeugen. Die Ergebnisse zeigen, dass der Einfluss der lokalen Topographie auf die Spektraldaten nahezu besei-tigt wird. Eine qualitative Analyse der ermittelten Reflektanzmodellparameter zeigt gute Überein-stimmung mit den bekannten geologischen Verteilungen von hellen und dunklen Gebieten, z. B. helle Vulkankuppen und dunkle Ascheablagerungen. Eine Analyse der Oberflächentemperaturschätzung zeigt, dass Temperaturen über 300 K geschätzt werden können. Ein Vergleich der erzeugten topogra-phischen Modelle mit hochgenauen Laser-Messungen zeigt, dass die vertikale Genauigkeit erhalten bleibt, während der visuelle Eindruck eindeutig eine Verbesserung der lateralen Auflösung gegen-über der Stereobasierten Modelle zeigt. Die abgeleitete Bildregistrierungsroutine auf Basis der topo-graphischen Modelle erreicht Genauigkeiten von unter einem Pixel

Feature-rich distance-based terrain synthesis

This thesis describes a novel terrain synthesis method based on distances in a weighted graph. The method begins with a regular lattice with arbitrary edge weights; heights are determined by path cost from a set of generator nodes. The shapes of individual terrain features, such as mountains, hills, and craters, are specified by a monotonically decreasing profile describing the cross-sectional shape of a feature, while the locations of features in the terrain are specified by placing the generators. Pathing places ridges whose initial location have a dendritic shape. The method is robust and easy to control, making it possible to create pareidolia effects. It can produce a wide range of realistic synthetic terrains such as mountain ranges, craters, faults, cinder cones, and hills. The algorithm incorporates random graph edge weights, permits the inclusion of multiple topography profiles, and allows precise control over placement of terrain features and their heights. These properties all allow the artist to create highly heterogeneous terrains that compare quite favorably to existing methods

Third International Symposium on Artificial Intelligence, Robotics, and Automation for Space 1994

The Third International Symposium on Artificial Intelligence, Robotics, and Automation for Space (i-SAIRAS 94), held October 18-20, 1994, in Pasadena, California, was jointly sponsored by NASA, ESA, and Japan's National Space Development Agency, and was hosted by the Jet Propulsion Laboratory (JPL) of the California Institute of Technology. i-SAIRAS 94 featured presentations covering a variety of technical and programmatic topics, ranging from underlying basic technology to specific applications of artificial intelligence and robotics to space missions. i-SAIRAS 94 featured a special workshop on planning and scheduling and provided scientists, engineers, and managers with the opportunity to exchange theoretical ideas, practical results, and program plans in such areas as space mission control, space vehicle processing, data analysis, autonomous spacecraft, space robots and rovers, satellite servicing, and intelligent instruments

Autonomous Vehicles

This edited volume, Autonomous Vehicles, is a collection of reviewed and relevant research chapters, offering a comprehensive overview of recent developments in the field of vehicle autonomy. The book comprises nine chapters authored by various researchers and edited by an expert active in the field of study. All chapters are complete in itself but united under a common research study topic. This publication aims to provide a thorough overview of the latest research efforts by international authors, open new possible research paths for further novel developments, and to inspire the younger generations into pursuing relevant academic studies and professional careers within the autonomous vehicle field

Collaborative Localization and Mapping for Autonomous Planetary Exploration : Distributed Stereo Vision-Based 6D SLAM in GNSS-Denied Environments

Mobile robots are a crucial element of present and future scientific missions to explore the surfaces of foreign celestial bodies such as Moon and Mars. The deployment of teams of robots allows to improve efficiency and robustness in such challenging environments. As long communication round-trip times to Earth render the teleoperation of robotic systems inefficient to impossible, on-board autonomy is a key to success. The robots operate in Global Navigation Satellite System (GNSS)-denied environments and thus have to rely on space-suitable on-board sensors such as stereo camera systems. They need to be able to localize themselves online, to model their surroundings, as well as to share information about the environment and their position therein. These capabilities constitute the basis for the local autonomy of each system as well as for any coordinated joint action within the team, such as collaborative autonomous exploration. In this thesis, we present a novel approach for stereo vision-based on-board and online Simultaneous Localization and Mapping (SLAM) for multi-robot teams given the challenges imposed by planetary exploration missions. We combine distributed local and decentralized global estimation methods to get the best of both worlds: A local reference filter on each robot provides real-time local state estimates required for robot control and fast reactive behaviors. We designed a novel graph topology to incorporate these state estimates into an online incremental graph optimization to compute global pose and map estimates that serve as input to higher-level autonomy functions. In order to model the 3D geometry of the environment, we generate dense 3D point cloud and probabilistic voxel-grid maps from noisy stereo data. We distribute the computational load and reduce the required communication bandwidth between robots by locally aggregating high-bandwidth vision data into partial maps that are then exchanged between robots and composed into global models of the environment. We developed methods for intra- and inter-robot map matching to recognize previously visited locations in semi- and unstructured environments based on their estimated local geometry, which is mostly invariant to light conditions as well as different sensors and viewpoints in heterogeneous multi-robot teams. A decoupling of observable and unobservable states in the local filter allows us to introduce a novel optimization: Enforcing all submaps to be gravity-aligned, we can reduce the dimensionality of the map matching from 6D to 4D. In addition to map matches, the robots use visual fiducial markers to detect each other. In this context, we present a novel method for modeling the errors of the loop closure transformations that are estimated from these detections. We demonstrate the robustness of our methods by integrating them on a total of five different ground-based and aerial mobile robots that were deployed in a total of 31 real-world experiments for quantitative evaluations in semi- and unstructured indoor and outdoor settings. In addition, we validated our SLAM framework through several different demonstrations at four public events in Moon and Mars-like environments. These include, among others, autonomous multi-robot exploration tests at a Moon-analogue site on top of the volcano Mt. Etna, Italy, as well as the collaborative mapping of a Mars-like environment with a heterogeneous robotic team of flying and driving robots in more than 35 public demonstration runs

NASA Tech Briefs, December 2005

Topics covered include: Video Mosaicking for Inspection of Gas Pipelines; Shuttle-Data-Tape XML Translator; Highly Reliable, High-Speed, Unidirectional Serial Data Links; Data-Analysis System for Entry, Descent, and Landing; Hybrid UV Imager Containing Face-Up AlGaN/GaN Photodiodes; Multiple Embedded Processors for Fault-Tolerant Computing; Hybrid Power Management; Magnetometer Based on Optoelectronic Microwave Oscillator; Program Predicts Time Courses of Human/ Computer Interactions; Chimera Grid Tools; Astronomer's Proposal Tool; Conservative Patch Algorithm and Mesh Sequencing for PAB3D; Fitting Nonlinear Curves by Use of Optimization Techniques; Tool for Viewing Faults Under Terrain; Automated Synthesis of Long Communication Delays for Testing; Solving Nonlinear Euler Equations With Arbitrary Accuracy; Self-Organizing-Map Program for Analyzing Multivariate Data; Tool for Sizing Analysis of the Advanced Life Support System; Control Software for a High-Performance Telerobot; Java Radar Analysis Tool; Architecture for Verifiable Software; Tool for Ranking Research Options; Enhanced, Partially Redundant Emergency Notification System; Close-Call Action Log Form; Task Description Language; Improved Small-Particle Powders for Plasma Spraying; Bonding-Compatible Corrosion Inhibitor for Rinsing Metals; Wipes, Coatings, and Patches for Detecting Hydrazines; Rotating Vessels for Growing Protein Crystals; Oscillating-Linear-Drive Vacuum Compressor for CO2; Mechanically Biased, Hinged Pairs of Piezoelectric Benders; Apparatus for Precise Indium-Bump Bonding of Microchips; Radiation Dosimetry via Automated Fluorescence Microscopy; Multistage Magnetic Separator of Cells and Proteins; Elastic-Tether Suits for Artificial Gravity and Exercise; Multichannel Brain-Signal-Amplifying and Digitizing System; Ester-Based Electrolytes for Low-Temperature Li-Ion Cells; Hygrometer for Detecting Water in Partially Enclosed Volumes; Radio-Frequency Plasma Cleaning of a Penning Malmberg Trap; Reduction of Flap Side Edge Noise - the Blowing Flap; and Preventing Accidental Ignition of Upper-Stage Rocket Motors

Clearing the Clouds: Extracting 3D information from amongst the noise

Advancements permitting the rapid extraction of 3D point clouds from a variety of imaging modalities across the global landscape have provided a vast collection of high fidelity digital surface models. This has created a situation with unprecedented overabundance of 3D observations which greatly outstrips our current capacity to manage and infer actionable information. While years of research have removed some of the manual analysis burden for many tasks, human analysis is still a cornerstone of 3D scene exploitation. This is especially true for complex tasks which necessitate comprehension of scale, texture and contextual learning. In order to ameliorate the interpretation burden and enable scientific discovery from this volume of data, new processing paradigms are necessary to keep pace. With this context, this dissertation advances fundamental and applied research in 3D point cloud data pre-processing and deep learning from a variety of platforms. We show that the representation of 3D point data is often not ideal and sacrifices fidelity, context or scalability. First ground scanning terrestrial LIght Detection And Ranging (LiDAR) models are shown to have an inherent statistical bias, and present a state of the art method for correcting this, while preserving data fidelity and maintaining semantic structure. This technique is assessed in the dense canopy of Micronesia, with our technique being the best at retaining high levels of detail under extreme down-sampling (\u3c 1%). Airborne systems are then explored with a method which is presented to pre-process data to preserve a global contrast and semantic content in deep learners. This approach is validated with a building footprint detection task from airborne imagery captured in Eastern TN from the 3D Elevation Program (3DEP), our approach was found to achieve significant accuracy improvements over traditional techniques. Finally, topography data spanning the globe is used to assess past and previous global land cover change. Utilizing Shuttle Radar Topography Mission (SRTM) and Moderate Resolution Imaging Spectroradiometer (MODIS) data, paired with the airborne preprocessing technique described previously, a model for predicting land-cover change from topography observations is described. The culmination of these efforts have the potential to enhance the capabilities of automated 3D geospatial processing, substantially lightening the burden of analysts, with implications improving our responses to global security, disaster response, climate change, structural design and extraplanetary exploration