Cooperative localization for autonomous underwater vehicles

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution February 2009Self-localization of an underwater vehicle is particularly challenging due to the absence of Global Positioning System (GPS) reception or features at known positions that could otherwise have been used for position computation. Thus Autonomous Underwater Vehicle (AUV) applications typically require the pre-deployment of a set of beacons. This thesis examines the scenario in which the members of a group of AUVs exchange navigation information with one another so as to improve their individual position estimates. We describe how the underwater environment poses unique challenges to vehicle navigation not encountered in other environments in which robots operate and how cooperation can improve the performance of self-localization. As intra-vehicle communication is crucial to cooperation, we also address the constraints of the communication channel and the effect that these constraints have on the design of cooperation strategies. The classical approaches to underwater self-localization of a single vehicle, as well as more recently developed techniques are presented. We then examine how methods used for cooperating land-vehicles can be transferred to the underwater domain. An algorithm for distributed self-localization, which is designed to take the specific characteristics of the environment into account, is proposed. We also address how correlated position estimates of cooperating vehicles can lead to overconfidence in individual position estimates. Finally, key to any successful cooperative navigation strategy is the incorporation of the relative positioning between vehicles. The performance of localization algorithms with different geometries is analyzed and a distributed algorithm for the dynamic positioning of vehicles, which serve as dedicated navigation beacons for a fleet of AUVs, is proposed.This work was funded by Office of Naval Research grants N00014-97-1-0202, N00014-05-1-0255, N00014-02-C-0210, N00014-07-1-1102 and the ASAP MURI program led by Naomi Leonard of Princeton University

Emotion recognition and verbal and non-verbal memory changes among older adults: Is decline generalised or modular?

Declines in cognitive abilities among ageing adults are observed phenomena. But are these declines ‘across the board’ or are they modular? The answer affects theory and practice, including potential treatments that may reduce the declines. Deficits in emotion recognition may provide a window into what is occurring in the ageing brain. We investigated whether changes in recognition of emotion could be attributed to a decline in memory processes. Sixty-two participants recruited from South-Eastern Queensland divided into young (19-49), middle old (49-64) and old (65 and above) cohorts performed computer administered tasks assessing emotion recognition, verbal and non-verbal memory. Older adults evidenced decline in recognition of anger, surprised and fearful faces. In addition, age related decline was evident in verbal memory performance. However, there was no corresponding decline in non-verbal memory performance. The dissociation of non-verbal memory performance from emotion recognition performance provides support for a modular decline model of age-related decline. The detection of decline in both verbal memory performance and emotion recognition suggests a common underlying process may be associated with both. Performance on the emotion recognition task may be verbally mediated. This study provides valuable insight into the ageing process and suggests decline may occur asynchronously- that is, is modular

Dynamic positioning of beacon vehicles for cooperative underwater navigation

Autonomous Underwater Vehicles (AUVs) are used for an ever increasing range of applications due to the maturing of the technology. Due to the absence of the GPS signal underwater, the correct estimation of its position is a challenge for submerged vehicles. One promising strategy to mitigate this problem is to use a group of AUVs where one or more assume the role of a beacon vehicle which has a very accurate position estimate due to an expensive navigation suite or frequent surfacings. These beacon vehicles broadcast their position and the remaining survey vehicles can use this position information and intra-vehicle ranges to update their position estimate. The effectiveness of this approach strongly depends on the geometry between the beacon vehicles and the survey vehicles. The trajectories of the beacon vehicles should thus be planned with the goal to minimize the position uncertainty of the survey vehicles. We propose a distributed algorithm which dynamically computes the locally optimal position for a beacon vehicle using only information obtained from broadcast communication of the survey vehicles. It does not need prior information about the survey vehicles' trajectory and can be used for any group size of beacon and survey vehicles.United States. Office of Naval Research (Grant N00014-97-1-0202)United States. Office of Naval Research (Grant N00014-05-1-0255)United States. Office of Naval Research (Grant N00014-02-C- 0210)United States. Office of Naval Research (Grant N00014-07-1-1102

Multivariate Untersuchungen in Gasphasenprozessen und Aerosolen mittels Raman-Spektroskopie

Für Entwurf, Modellierung sowie Überwachung von Gasphasenprozessen sind fun-dierte Kenntnisse über elementare Zustandsgrößen wie Temperatur oder Spezieskon-zentration unerlässlich. Obwohl bereits heute eine breite Palette an optischen, nicht-invasiven Online-Messtechniken zu Verfügung steht, ist deren Einsatz noch immer auf wenige Anwendungsfelder beschränkt. Die Gründe dafür liegen im oft hohen ex-perimentellen Aufwand oder in anderen Nachteilen wie der Notwendigkeit zum Einsatz von Tracern oder der Kalibrierung über zusätzliche Referenzen. Um diese Nachteile zu umgehen, wurde im Rahmen dieser Arbeit ein mobiles, faserbasiertes Sensorsystem, basierend auf der spontanen Raman-Spektroskopie entwickelt. Die Technik verwendet durchstimmbare NIR-Dauerstrich-Laser-Anregung, Signalerfassung in rückstreuender Geometrie (Punktmessung) und erfordert weder Probennahme, noch Tracer innerhalb der Strömung oder Kalibrierschritte am zu untersuchenden Prozess. Die Methode ermöglicht die simultane Bestimmung von Gastemperaturen und Spezieskonzentrationen sowie im Falle von Aerosolen die Bestimmung der Partikelspezies und der Anteile ihrer polymorphen Kristallstrukturen. Die Datenauswertung basiert auf der Rekonstruktion der gemessenen Spektren anhand simulierter Modellspektren durch Least-Square-Algorithmen. Herkömmliche Ansätze liefern lediglich Parameter, die das Residuum zwischen Simulation und Messsignal minimieren. Unsicherheiten der Messgrößen sind daraus nicht ermittelbar und werden deshalb konventionell durch Wiederholung der Messung bestimmt. Mit Hilfe der hier eingesetzten Bayes'schen Statistik lassen sich die entsprechenden Unsicherheiten direkt bestimmen. Darüber hinaus ermöglicht der Ansatz das Einbeziehen von Vorwissen zur Verbesserung der Robustheit und Genauigkeit der Auswertung. Die Performance des Sensorsystems wurde durch Einsätze an verschiedenen Gasphasenprozessen getestet und evaluiert. Dazu gehören Test-Aerosole, ein TiO2-Nanopartikelsyntheseprozess sowie eine laminare, rußarme Flamme. Ein leicht modifiziertes Sensorsystem (VIS-Anregung) wurde an einem Vergasungsreaktor eingesetzt. Generell konnte eine hohe Qualität der ermittelten Messgrößen festgestellt werden. So sind deren Unsicherheiten mit denen deutlich komplexerer Messtechniken vergleichbar, stellenweise sogar geringer. Die mittlere Unsicherheit der Gastemperaturen innerhalb der Flamme betrug nur 1,6 %. Somit ermöglicht der vorgestellte Sensor bei geringem experimentellen Aufwand die Bestimmung wertvoller Prozessdaten und stellt so potentiell die Basis für eine breitere Anwendung optischer Prozessmesstechnik dar.For the design, modelling and monitoring of gas-phase processes a profound knowledge of elementary state variables such as temperature or species concentration is essential. Although a wide range of optical, non-invasive online measurement techniques is already available today, their use is still limited to a few fields of application. The reasons for this are the regularly high experimental effort or other disadvantages such as the necessity to use tracers or to execute calibration via additional references. In order to avoid these disadvantages, a mobile, fiber-based sensor system based on spontaneous Raman spectroscopy was developed within the scope of this work. The technique uses tunable NIR continuous-wave laser excitation, signal acquisition in backscattering geometry (point measurement) and requires neither sampling, tracers within the flow nor calibration steps at the process under investigation. The method allows the simultaneous determination of gas temperatures and species concentrations and, in the case of aerosols, the determination of the particle species and their polymorphic crystal structures. The data evaluation is based on the reconstruction of the measured spectra using simulated model spectra through least square algorithms. Conventional approaches only provide parameters that minimize the residual between simulation and measurement signal. Uncertainties of the measured variables cannot be determined from these parameters and are, therefore, determined conventionally by repeating the measurement. With the help of the Bayesian statistics used here, the corresponding uncertainties can be determined directly. Furthermore, the approach allows the inclusion of prior knowledge to improve the robustness and accuracy of the evaluation. The performance of the sensor system was tested and evaluated by using it in different gas phase processes. These include test aerosols, a TiO2 nanoparticle synthesis process and a laminar weakly sooting flame. A slightly modified system (VIS excitation) was used with a similar operation strategy at a gasification reactor. In general, a high quality of the measured variables could be determined. Their uncertainties are comparable with those of much more complex measuring techniques, in some cases even lower. The mean uncertainty of the gas temperatures within the flame was only 1.6 %. Thus, the presented sensor enables the determination of valuable process data with low experimental effort and can potentially be the basis for a broader application of optical process measurement technology

Low-Cost Collaborative Localization for Large-Scale Multi-Robot Systems

Large numbers of collaborating robots are advantageous for solving distributed problems. In order to efficiently solve the task at hand, the robots often need accurate localization. In this work, we address the localization problem by developing a solution that has low computational and sensing requirements, and that is easily deployed on large robot teams composed of cheap robots. We build upon a real-time, particle-filter based localization algorithm that is completely decentralized and scalable, and accommodates realistic robot assumptions including noisy sensors, and asynchronous and lossy communication. In order to further reduce this algorithm's overall complexity, we propose a low-cost particle clustering method, which is particularly well suited to the collaborative localization problem. Our approach is experimentally validated on a team of ten real robots

Vertex: A New Distributed Underwater Robotic Platform for Environmental Monitoring

We present a new Autonomous Underwater Vehicle (AUV) system for cooperative environmental sensing. The AUV was specifically developed as a platform for distributed, cooperative sensing in lakes and coastal areas. In this paper we describe the prerequisite subsystems for a submersible multi-robot system and their interactions. In particular, we incorporate a distributed acoustic localisation system and distributed time-sliced communication systems into an agile, 5-DOF submersible robot that is small, easy to deploy and retrieve, with a modular environmental sensor payload for relevant scientific measurements. We also developed a distributed Hardware-In-the-Loop (HIL) simulation framework to facilitate early testing of algorithms in simulation while running final binary code on the actual robot hardware. To avoid communication overhead and real-time issues, the simulation of the vehicle dynamics and all proprioceptive sensors is performed on-board. Exteroceptive sensors are simulated by vehicle-to-vehicle communication where possible, supported by a central simulation supervisor where required. Finally, we present some preliminary experimental results of the system

A Flexible In Situ Power Monitoring Unit for Environmental Sensor Networks

Wireless radios are a great consumer of energy in sensor networks. Retrieving data from a remote deployment in an energy-efficient fashion is a difficult problem, and while solutions have been proposed in literature, real-world systems typically implement robust though inefficient methods. In an effort to bring efficient monitoring techniques to real-world environmental sensor networks, we seek to now quantify the performance brought by these algorithms in practical terms, i.e., by their resulting reduction in overall station energy consumption. To this end, we have developed a power monitoring extension board that integrates seamlessly with a commercial environmental sensor network platform. The board is capable of measuring all incoming and outgoing power for a station, and can disconnect subsystems, such as the solar panel or sensor bus, as necessary. The board is currently deployed on an outdoor network and is undergoing extensive testing