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Planetary rovers are robots that need to perform autonomous navigation, because of the long delay communication and no human assistance. Furthermore, they need to perform the optimal estimation of its position in order to have a good performance on its navigation system. The need for good performance filters for estimating the actual position of mobile robots of this kind is needed, due to the fact that sensors are noisy and that information is of vital importance for a planetary rover’s mission. Besides, good accurate sensors for the matter, are not easy to find for space application. Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) were implemented to analyze a data set of a 4-wheel robot, and later used for comparison on accuracy in the estimation of its pose. The analysis will give the possibility to know the right combination of sensors, recognize some issues during the trajectory. Furthermore, this study has been made with aims to give the reader knowledge of state of the art in planetary rovers, their constraints and consideration while developing them. The robot used for the research has been developed for an international competition of field robot automation. The main goal is to navigate autonomously through flowerpots performing different tasks as flowerpot collection, distance traveled and robustness on localization and navigation algorithms. / <p>Validerat; 20120822 (anonymous)</p>

This thesis paper presents a simultaneous 3D mapping and geolocation of road surface technique that combines local road surface mapping and global camera localization. The local road surface is generated by structure from motion (SFM) with multiple views and optimized by Bundle Adjustment (BA). A system is developed for the global reconstruction of 3D road surface. Using the system, the proposed technique globally reconstructs 3D road surface by estimating the global camera pose using the Adaptive Extended Kalman Filter (AEKF) and integrates it with local road surface reconstruction techniques. The proposed AEKF-based technique uses image shift as prior. And the camera pose was corrected with the sparse low-accuracy Global Positioning System (GPS) data and digital elevation map (DEM). The AEKF adaptively updates the covariance of uncertainties such that the estimation works well in environment with varying uncertainties. The image capturing system is designed with the camera frame rate being dynamically controlled by vehicle speed read from on-board diagnostics (OBD) for capturing continuous data and helping to remove the effects of moving vehicle shadow from the images with a Random Sample and Consensus (RANSAC) algorithm. The proposed technique is tested in both simulation and field experiment, and compared with similar previous work. The results show that the proposed technique achieves better accuracy than conventional Extended Kalman Filter (EKF) method and achieves smaller translation error than other similar other works. / Master of Science / This thesis paper presents a simultaneous three dimensional (3D) mapping and geolocation of road surface technique that combines local road surface mapping and global camera localization. The local road surface is reconstructed by image processing technique with optimization. And the designed system globally reconstructs 3D road surface by estimating the global camera poses using the proposed Adaptive Extended Kalman Filter (AEKF)-based method and integrates with local road surface reconstructing technique. The camera pose uses image shift as prior, and is corrected with the sparse low-accuracy Global Positioning System (GPS) data and digital elevation map (DEM). The final 3D road surface map with geolocation is generated by combining both local road surface mapping and global localization results. The proposed technique is tested in both simulation and field experiment, and compared with similar previous work. The results show that the proposed technique achieves better accuracy than conventional Extended Kalman Filter (EKF) method and achieves smaller translation error than other similar other works.

<p dir="ltr">Object tracking in cislunar space has become an area of interest within many communities where cislunar space domain awareness (SDA) is critical to operations. Due to the influence of both the Earth and Moon on objects in this domain, the classical two body problem does not accurately describe the dynamics of the state. Legacy tracking capabilities fall short in providing accurate state estimates due to the large volume of space and the highly non-linear dynamics involved. In order to advance SDA in cislunar space, tracking capabilities must be updated for this domain. </p><p dir="ltr">Both the Extended Kalman Filter (EKF) and Gaussian Mixture Extended Kalman Filter (GM-EKF) are used for orbit determination in this thesis along side the Circular Restricted Three Body Problem (CR3BP) to model the non-linear dynamics. The filters are utilized to determine the best estimate of the state as well as its covariance. The two filter's performances are compared to highlight areas in which the assumptions surrounding the EKF are violated resulting in failed tracking, as well as to highlight the power of the GM-EKF for non-linear systems using splitting and merging techniques. </p><p dir="ltr">This thesis presents single and multiple object tracking of objects in a multitude of cislunar orbits using a Moon ground-based sensor. Multiple object tracking is accomplished using a novel Lyapunov-based scheduler in order to reduce the total system uncertainty. The environment is modeled to include exclusion zones which preclude measurements. These zones consist of conjunction from the Earth and Sun, brightness constraints, and camera field of regard (FOR). When measurements are unavailable the uncertainty in the state estimation rises significantly.</p><p dir="ltr">An investigation of varied sensor placements and Sun-Earth-Moon geometries provides results to inform locations and trends which are able to confidently track both single and multiple objects in cislunar orbits. </p>

As the mission field for autonomous vehicles expands into a larger variety of territories, the development of autonomous surface vehicles (ASVs) becomes increasingly important. ASVs have the potential to travel for long periods of time in areas that cannot be reached by aerial, ground, or underwater autonomous vehicles. ASVs are useful for a variety of missions, including bathymetric mapping, communication with other autonomous vehicles, military reconnaissance and surveillance, and environmental data collecting. Critical to an ASV's ability to maneuver without human intervention is its ability to detect obstacles, including the shoreline. Prior topological knowledge of the environment is not always available or, in dynamic environments, reliable. While many existing obstacle detection systems can only detect 3D obstacles at close range via a laser or radar signal, vision systems have the potential to detect obstacles both near and far, including "flat" obstacles such as the shoreline. The challenge lies in processing the images acquired by the vision system and extracting useful information. While this thesis does not address the issue of processing the images to locate the pixel positions of the obstacles, we assume that we have these processed images available. We present an algorithm that takes these processed images and, by incorporating the kinematic model of the ASV, maps the pixel locations of the obstacles into a global coordinate system. An Extended Kalman Filter is used to localize the ASV and the surrounding obstacles. / Master of Science

Yes / In this paper, a novel control algorithm is presented to enhance the performance of the tracking property for a class of nonlinear and dynamic stochastic systems subjected to non-Gaussian noises. Although the existing standard PI controller can be used to obtain the basic tracking of the systems, the desired tracking performance of the stochastic systems is difficult to achieve due to the random noises. To improve the tracking performance, an enhanced performance loop is constructed using the EKF-based state estimates without changing the existing closed loop with a PI controller. Meanwhile, the gain of the enhanced performance loop can be obtained based upon the entropy optimization of the tracking error. In addition, the stability of the closed loop system is analyzed in the mean-square sense. The simulation results are given to illustrate the effectiveness of the proposed control algorithm. / This work was supported in part by the PNNL Control of Complex Systems Initiative and in part by the National Natural Science Foundation of China under Grants 61621004,61573022 and 61333007.

High-accuracy localization is vital for many applications and is a fundamental prerequisite for enabling autonomous missions. Modern navigation systems often rely heavily on Global Navigation Satellite Systems (GNSS) for achieving high localization accuracy over extended periods of time, which has necessitated alternative localization methods that can be used in GNSS-disturbed environments. One popular alternative that has emerged is Collaborative Localization (CL), which is a method where agents of a swarm combine knowledge of their own state with relative measurements of other agents to achieve a localization accuracy that is better than what a single agent can achieve on its own. Performing this in a decentralized manner introduces the challenge of how to account for unknown inter-agent correlations, which typically leads to the need for using conservative fusion methods such as Covariance Intersection (CI) to preserve consistency. Many existing CL algorithms that utilize CI assume agents to have perception systems capable of identifying the relative position of other swarm members. These algorithms do therefore not work in systems where, e.g., agents are only capable of measuring range to each other. Other CI algorithms that support more generic measurement models can require large amounts of data to be exchanged when agents communicate, which could lead to issues in bandwidth-limited systems. This thesis develops a consistent decentralized collaborative localization algorithm based on CI that supports range-only measurements between agents and requires a communication effort that is constant in the number of agents in the swarm. The algorithm, referred to as the PSCI algorithm, was found to maintain satisfactory performance in various scenarios but exhibits slightly increased sensitivity to the measurement geometry compared to an already existing, more communication-heavy, CI-based algorithm. Moreover, the thesis highlights the impact of linearization errors in range-only CL systems and shows that performing CI-fusion before the range-observation measurement update, with a clever choice of CI cost function, can reduce linearization errors for the PSCI algorithm. A comparison between the PSCI algorithm and an already existing algorithm, referred to as the Cross-Covariance Approximation (CCA) algorithm, has further been conducted through a sensitivity analysis with respect to communication rate and the number of GNSS agents. The simulation results indicate that the PSCI algorithm exhibits diminishing improvement in Root Mean Square Error (RMSE) with increased communication rates, while the RMSE of the CCA algorithm reaches a local minimum, subsequently showing overconfidence with higher rates. Lastly, evaluation under a varying number of GNSS agents indicates that cooperative benefits for the PSCI filter are marginal when uncertainty levels are uniform across agents. However, the PSCI algorithm demonstrates superior performance improvements with an increased number of GNSS agents compared to the CCA algorithm, attributed to the overconfidence of the latter.

Two important physical aspects that determine the performance of a running train are the total running resistance that acts on the whole train moving forward, and the available adhesion (utilizable wheel-rail-friction) for propulsion and breaking. Using the measured and available signals, online identification of the current running resistance and available adhesion and also prediction of future values for a distance ahead of the train, is desired. With the aim to enhance the precision of those calculations, this thesis investigates the potential of online identification and prediction utilizing the Extended Kalman Filter. The conclusions are that problems with observability and sensitivity arise, which result in a need for sophisticated methods to numerically derive the acceleration from the velocity signal. The smoothing spline approximation is shown to provide the best results for this numerical differentiation. Sensitivity and its need for high accuracy, especially in the acceleration signal, results in a demand of higher sample frequency. A desire for other profound ways of collecting further information, or to enhance the models, arises with possibilities of future work in the field. / Två viktiga fysikaliska aspekter som bestämmer prestandan för ett tåg i drift är det totala gångmotståndet som verkar på hela tåget, samt den tillgängliga adhesionen (användbara hjul-räl-friktionen) för framdrivning och bromsning. Från de tillgängliga signalerna önskas identifiering, samt prediktering, av dessa två storheter, under drift. Med målet att förbättra precisionen av dessa skattningar undersöker detta examensarbete potentialen av skattning och prediktering av gångmotstånd och adhesion med hjälp av Extended KalmanFiltering. Slutsatsen är att problem med observerbarhet och känslighet uppstår, vilket resulterar i ett behov av sofistikerade metoder att numeriskt beräkna acceleration från en hastighetssignal. Metoden smoothing spline approximation visar sig ge de bästa resultaten för denna numeriska derivering. Känsligheten och dess medförda krav på hög precision, speciellt på accelerationssignalen, resulterar i ett behov av högre samplingsfrekvens. Ett behov av andra adekvata metoder att tillföra ytterligare information, eller att förbättra modellerna, ger upphov till möjliga framtida utredningar inom området.

Hydrofoiling in general has the potential to revolutionize watercraft in the future since it allows smoother and faster transport on water with less energy consumption than traditional planning hulls. Even if the concept of hydrofoiling has been around since the last century, development in control theory and material science together with increased computing power has led to a growing interest for the technology. Especially in water sports such as speed sailing and surfing due to its superiority in speed and comfort. Researchers and students at the Engineering Mechanics Department at KTH, Royal Institute of Technology, Stockholm are working on a new type of watercraft, utilizing only one single hydrofoil with the intention to minimize drag for faster and smoother rides in various wave and weather conditions. The difficulties lie in understanding the relationship between actuators and the mechanics. This thesis is a continuation work from a previous thesis which designed a control strategy based on a model with 4 degrees of freedom (DOF). Due to simplifications and linearizations, the 4 DOF model was not rich enough to meet the performance requirements. This thesis presents a 6 DOF model by deriving the mechanical equations for the spherical inverted pendulum and actuation from the hydrofoiling module. The inverted pendulum model is a well-known control problem that can be solved with different strategies. By showing that the hydrofoiling concept can be modelled as an inverted pendulum, it is also shown that it can be controlled as an inverted pendulum. The derived model is used together with an Extended Kalman Filter to create an observer. The observer is validated with a spherical inverted pendulum model in Matlab and the block diagram environment, Simulink. Simulation results show that the 6 DOF model is able to produce accurate state estimation of the watercraft even in the presence of stochastic measurement noise. It is also concluded that viscous forces, that arise from the watercraft being partly surrounded by water and partly by air, need further investigation. / Principen för bärplan är att generera lyftkraft från vattnet på samma sätt som flygplansvingar genererar lyftkraft från luften för att lyfta farkostens skrov ur vattnet. Detta minskar motståndet från friktionen mellan skrov och vatten vilket möjliggör snabbare och jämnare transport på vatten med en lägre energiförbrukning än traditionella planande skrov. På senare år har tekniken fått ett uppsving i och med framsteg inom strömningsmekanik, reglerteknik och materiallära. Detta i takt med datorers ökande beräkningskraft har lett till att bärplanskonstruktioner har kunnat uppvisa en överlägsenhet i vattensporter som kappsegling och surfing när det kommer till fart och komfort. Forskare och studenter på avdelningen för farkostteknik och solidmekanik vid Kungliga Tekniska Högskolan, Stockholm arbetar med att ta fram en ny typ av farkost med en minimal bärplansdesign, FoilCart. Dess utformning gör att det mekaniska beteendet kan liknas vid en inverterad pendel, vilket är ett välkänt, olinjärt reglerproblem som kan lösas på flera sätt. Denna avhandling är ett vidarearbete som bygger på en modell med fyra frihetsgrader från en tidigare avhandling kring FoilCart-projektet. Modellen med fyra frihetsgrader var, på grund av förenklingar och linjärisering av systemdynamiken, bristfällig och kunde inte garantera en robust balansering av farkosten förutom i linjäriseringspunkten. Modellen som presenteras i denna avhandling har sex frihetsgrader. Mekaniken och systemdynamiken härleds från den sfäriska inverterade pendeln tillsammans med styrningen från bärplansmodulen, utan förenklingar och linjärisering. Modellen används i ett Kalmanfilter för att konstruera en observatör för tillståndsrekonstruktion. Den framtagna modellen valideras med en FoilCart-modell i Simulink. Resultaten visar att observatören kan ge en noggrann tillståndsrekonstruktion även vid simulerat mätbrus i mätsignalen. Avhandlingen syftar till att visa hur den inverterade pendelmodellen kan användas vid framtida implementation av rekonstruerad tillståndsåterkoppling. I och med avgränsningar i avhandlingen finns det också en del strömningsmekaniska aspekter som inte tagits med vid framtagningen av denna modell. Eftersom farkosten delvis är omgiven av vatten och delvis av luft skulle det vara intressant att undersöka om noggrannheten i tillståndsrekonstruktionen kan förbättras genom att använda avancerad strömningsmekanik.

A filtragem, na sua forma mais geral, tem estado presente na vida do homem há muito tempo. Com o surgimento de novas tecnologias (surgimento da eletricidade e a sua evolução) e o desenvolvimento da computação, as técnicas de filtragem (separação) de sinais elétricos. Normalmente, os sistemas de comunicação (telefonia móvel e fixa, sinais recebidos de satélites e outros sistemas) contém sinais indesejáveis responsáveis pela degradação do sinal original. Dentro desse contexto, este projeto de pesquisa apresenta um estudo do algoritmo Filtro Duplo de Kalman Estendido, onde um filtro e Kalman e duas redes neurais são empregadas para a redução de ruídos em sinais de voz. O algoritmo estudado foi aplicado ao processamento de um sinal corrompido por dois tipos de ruídos diferentes: ruído branco e ruído gaussiano e ruído branco não estacionário, conseguindo-se bons resultados. Uma melhora sensível do sinal filtrado pode ser conseguida com técnicas de pré-filtragem do sinal. Neste trabalho foi utilizado o filtro de médias para a pré-filtragem, obtendo um sinal filtrado com ruído musical de baixa intensidade. / Filtering in it\'s most general kind has been present in men\'s life for a long time. With the appearance of new technologies (appearance of electricity and it\'s evolution) and the deyelopment of the computer science, the filtering techniques started to be widely used in engineering to the filtering (separation) of electric signals. Normally the communication systems (fixed and mobile telephony, signals sent from satellites and other systems) bring undesired results responsible for the degradation of the original signal. Within this context, this research project shows a study of the algorithm Dual Extended Kalman Filtering, in which a Kalman filter and two neural networks are used for the reduction of noise in speech signals. The algorithm studied was applied to the processing of a signal corrupted by two types of different noises: gaussian white noise and non stationary white noise obtaining good results. A significant improvement of the filtered noise can be obtained with techniques of pre-filtering of the signal. In this research the average filter for a pre-filtering was used, obtaining a filtered signal with musical noise oflow intensity.