Recent years have seen ambitious robotic exploration missions to other planets and a renewed interest in sending humans beyond low Earth orbit. These activities give rise to a need for autonomous spacecraft operation. Of particular interest here is the ability of a spacecraft to navigate independent of contact with Earth-based resources. Optical navigation techniques are proposed as a solution to the problem of navigating in a planetary system without requiring navigation information from Earth. A detailed discussion of optical sensor hardware and error sources leads to new high fidelity math models for optical sensor performance that may be used in navigation simulations. Algorithms are developed that allow optical data to be used for the estimation of spacecraft position, velocity, and attitude. Sequential measurements are processed using traditional filtering techniques. Additionally, for the case of attitude estimation, a new attitude filter called Sequential Optimal Attitude Routine (SOAR) is presented. The models and techniques developed in this dissertation are demonstrated in two case studies: (1) navigation of a spacecraft performing a planetary fly-by using real images from the June 2007 MESSENGER fly-by of Venus and (2) navigation of a spacecraft in cislunar space on a return trajectory from the Moon. / text

Development of autonomous in-fight navigation capability for a CubeSat mission is a challenging task, often ignored in favour of relying on a Global Navigation Satellite System (GNSS). However, the potential value in solving this problem becomes great for deep-space missions where such networks have limited coverage. This thesis presents a proof of concept for how autonomous navigation can be achieved using the star tracker already included in the design of the Hugin satellite, a 3U-CubeSat under development by Beyond Atlas AB in Stockholm. A navigation algorithm presented in existing literature was selected, processing optically derived angular measurements of distant celestial bodies in an Unscented Kalman Filter (UKF). The algorithm was then integrated with a custom-built orbit simulator to test the navigation in a fight environment. Results from these simulations demonstrate that this algorithm can be used to allow a satellite in geocentric orbit to consistently track its position using only optical measurements, and key parameters for tuning the navigation UKF have been identifed. Additionally, Hugin’s star tracker was tested in order to verify its capabilities and measure the accuracy and precision of angular measurements. Software for generating images containing stars and celestial bodies was custom-built and used for the tests. Results from these tests were inconclusive, as the star tracker was unable to identify stars displayed in the test images. The most probable cause for this inability was judged to be an insuffciently true-to-reality test environment, with several possible improvements identifed to increase the fidelity of future tests. Based on the results from the simulations this report concludes that autonomous navigation is possible on Hugin using the presented algorithm. Despite this, much work remains to be done with the major topics needing further investigation outlined in this report. / Utveckling av autonom satellitnavigation för CubeSats är en stor utmaning, och en som ofta väljs bort för att istället förlita sig på ett Global Navigation Satellite System (GNSS). Men tillgängligheten till sådana system är begränsad i yttre rymden, vilket gör förmågan att navigera autonomt mer värdefull. Detta examensarbete presenterar ett koncepttest för hur autonom navigering kan möjliggöras med hjälp av den stjärnkikare som är en del av satelliten Hugins design, en 3U CubeSat som utvecklas av Beyond Atlas AB i Stockholm. En navigationsalgoritm som presenterats i en kontemporär forskningsrapport har valts ut, vilken bearbetar optiska vinkelmätningar av avlägsna himlakroppar i ett Unscented Kalman Filter (UKF). Algoritmen har integrerats med en specialbyggd orbitalsimulator för att testa navigationens prestation i en rymdmiljö. Resultat från dessa simuleringar demonstrerar att med hjälp av denna algoritm kan en sattelit i geocentrisk omloppsbana konsekvent uppskatta sin position endast med hjälp av optiska mätningar. Nyckelparametrar för att ställa in navigationsfiltret har även identiferats. Utöver detta testades Hugins stjärnkikare för att verifera dess funktionalitet och mäta dess noggrannhet och precision. Mjukvara för att generera bilder innehållande stjärnor och himlakroppar specialbyggdes och användes för testerna. Testresultaten var ofullständiga, eftersom stjärnkikaren inte kunde identiferade stjärnor som visades i testbilderna. Det mest sannolika skälet till denna oförmåga bedömdes vara en ej tillräckligt verklighetstrogen testmiljö, med ett antal möjliga åtgärder identiferade. Utifrån simuleringsresultaten dras slutsaten att det är möjligt att utföra autonom navigation på Hugin med den framförda algoritmen. Trots detta återstår mycket arbete inom flera utvecklingsområden, varav de främsta beskrivs i denna rapport.

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Observing the relative state of two space vehicles has been an active field of research since the earliest attempts at space rendezvous and docking during the 1960's. Several techniques have successfully been employed by several space agencies and the importance of these systems has been repeatedly demonstrated during the on-orbit assembly and continuous re-supply of the International Space Station. More recent efforts are focused on technologies that can enable fully automated navigation and control of space vehicles. Technologies which have previously been investigated or are actively researched include Video Guidance Systems (VGS), Light Detection and Ranging (LIDAR), RADAR, Differential GPS (DGPS) and Visual Navigation Systems. The proposed system leverages the theoretical foundation which has been advanced in the development of VisNav, invented at Texas A & M University, and the miniaturized commercially available Northstar sensor from Evolution Robotics. The dissertation first surveys contemporary technology, followed by an analytical investigation of the coarse sun sensor and errors associated with utilizing it in the near-field. Next, the commercial Northstar sensor is investigated, utilizing fundamentals to generate a theoretical model of its behavior, followed by the development of an experiment for the purpose of investigating and characterizing the sensor's performance. Experimental results are then presented and compared with a numerical simulation of a single-sensor system performance. A case study evaluating a two sensor implementation is presented evaluating the proposed system's performance in a multisensor configuration. The initial theoretical analysis relied on use of the cosine model, which proved inadequate in fully capturing the response of the coarse sun sensor. Fresenel effects were identified as a significant source of unmodeled sensor behavior and subsequently incorporated into the model. Additionally, near-field effects were studied and modeled. The near-field effects of significance include: unequal incidence angle, unequal incidence power, and non-uniform radiated power. It was found that the sensor displayed inherent instabilities in the 0.3 degree range. However, it was also shown that the sensor could be calibrated to this level. Methods for accomplishing calibration of the sensor in the near-field were introduced and feasibility of achieving better than 1 cm and 1 degree relative position and attitude accuracy in close proximity, even on a small satellite platform, was determined.

To perform close proximity operations under a low-gravity environment, relative and absolute positions are vital information to the maneuver. Hence navigation is inseparably integrated in space travel. Extreme Learning Machine (ELM) is presented as an optical navigation method around small celestial bodies. Optical Navigation uses visual observation instruments such as a camera to acquire useful data and determine spacecraft position. The required input data for operation is merely a single image strip and a nadir image. ELM is a machine learning Single Layer feed-Forward Network (SLFN), a type of neural network (NN). The algorithm is developed on the predicate that input weights and biases can be randomly assigned and does not require back-propagation. The learned model is the output layer weights which are used to calculate a prediction. Together, Extreme Learning Machine Optical Navigation (ELM OpNav) utilizes optical images and ELM algorithm to train the machine to navigate around a target body. In this thesis the asteroid, Vesta, is the designated celestial body. The trained ELMs estimate the position of the spacecraft during operation with a single data set. The results show the approach is promising and potentially suitable for on-board navigation.

Tradition spacecraft proximity operations require large and expensive on-board sensors and significant ground support. Relative angle measurements can be obtained from small, simple, and inexpensive on-board sensors, but have not traditionally been used for proximity operation because of difficulty generating rang information. In this thesis it is shown that useful relative range data can be generated provided that the spacecraft is experiencing a small continuous thrust such as would be provided by a low-thrust propulsion system.

Diese Dissertation soll einen Beitrag zur Weiterentwicklung der luftgestützten Verkehrslageerfassung leisten. Als Plattform dafür dient ein flugzeuggetragenes Kamerasystem, welches mit einem Inertialsystem gekoppelt ist. Vorgestellt werden hauptsächlich bildverarbeitende Algorithmen, welche an die Bildaufnahme anschließend bis hin zur Ermittlung der verkehrstechnischen Kenngrößen zum Einsatz kommen. Nach kurzer Skizzierung der verwendeten Hardware wird die Kalibrierung der Kameraeinbauwinkel durch Testflüge erläutert und auf ihre Genauigkeit hin untersucht. Es wird gezeigt, dass die Orientierungsdaten nicht die vom Hersteller angegebene Genauigkeit erreichen, was jedoch für die Verkehrslageerfassung nur von geringer Bedeutung ist. Anschließend an die Bildaufbereitung, welche die Orthobildgenerierung sowie die Eingrenzung der verkehrsaktiven Flächen beinhaltet, wird zur Ermittlung der Fahrzeugdichte ein zweistufiger Fahrzeugerkennungsalgorithmus entwickelt, welcher zunächst auf Kantenfilterbasis möglichst schnell Hypothesen erstellt. Diese werden in einer zweiten Phase durch eine Support Vector Machine überprüft, wobei ein Großteil der Fehlhypothesen verworfen wird. Die Erkennung erreicht bei guten Voraussetzungen Vollständigkeiten bis zu 90 Prozent bei sehr geringem Anteil von Fehldetektionen. Anschließend wird ein auf Singulärwertzerlegung basierender Tracking-Algorithmus verwendet, um Fahrzeughypothesen in benachbarten Bildern zu assoziieren und die mittleren Geschwindigkeiten zu ermitteln. Die erhaltenen Geschwindigkeiten unterscheiden sich um weniger als zehn km/h von den manuell erhobenen. Abschließend wird eine alternative Orientierungsmethode vorgestellt, welche auf Basis von GPS-Positionen und Bildinformationen automatisch die Fluglage ermittelt. Dies geschieht durch die Extraktion und das Matching von Straßensegmenten sowie zusätzliche Passpunktverfolgung. Die Ergebnisse weisen Genauigkeiten von etwa 0,1 bis 0,2 Grad auf. / This dissertation should make a contribution to the further development of airborne traffic detection. The used hardware is an airborne camera system combined with an inertial measurement unit for orientation determination. Mainly computer vision algorithms are presented, which are applied afterwards the image acquisition up to the determination of the most important traffic data. After a short presentation of the used hardware the calibration of the camera''s alignment angles during test flights is explained and its accuracy is analyzed. It is shown that the orientation data doesn''t reach the specified accuracy, which is fortunately less important for traffic detection. After the image preparation, which contains the ortho image generation as well as the clipping of traffic areas, a two-stage vehicle detection algorithm is implemented, which at first rapidly creates hypotheses based on edge filters. In the second stage those hypotheses are verified by a Support Vector Machine which rejects most of the False Posititves. At good conditions the detection reaches completeness rates of up to 90 percent with a low contingent of FP detections. Subsequently a tracking algorithm based on singular value decomposition is applied to associate vehicle hypotheses in adjacent images and determine the average speed. The achieved velocities differ less than ten kph from the manually obtained data. Concluding an orientation method is presented, that automatically determines the airplane''s attitude based on GPS and image information. This is realized by extraction and matching of street segments and additional tracking of ground control points. The results have accuracies of around 0.1 to 0.2 degrees.

No description available.