Probablistic approaches for intelligent AUV localisation

This thesis studies the problem of intelligent localisation for an autonomous underwater vehicle (AUV). After an introduction about robot localisation and specific issues in the underwater domain, the thesis will focus on passive techniques for AUV localisation, highlighting experimental results and comparison among different techniques. Then, it will develop active techniques, which require intelligent decisions about the steps to undertake in order for the AUV to localise itself. The undertaken methodology consisted in three stages: theoretical analysis of the problem, tests with a simulation environment, integration in the robot architecture and field trials. The conclusions highlight applications and scenarios where the developed techniques have been successfully used or can be potentially used to enhance the results given by current techniques. The main contribution of this thesis is in the proposal of an active localisation module, which is able to determine the best set of action to be executed, in order to maximise the localisation results, in terms of time and efficiency

A Location Routing Protocol Based on Smart Antennas for Wireless Sensor Networks

RÉSUMÉ Les réseaux de capteurs sans fil sont une technologie émergente pour la surveillance de l’environnement. Un réseau de capteurs typique se compose d'un grand nombre de capteurs miniatures (noeuds) multifonctionnels, à faible coût et à faible consommation d’énergie, équipés d’un radio émetteur-récepteur et d’un ensemble de transducteurs pour récolter et transmettre des données environnementales d'une manière autonome. Une des contraintes les plus importantes de capteurs est la nécessitée d’économiser de l’énergie puisqu’ils utilisent des batteries de duré limitée, généralement irremplaçables. En outre, ils se caractérisent également par une faible vitesse de traitement, capacité de stockage et de bande passante, qui nécessite une gestion des ressources très attentive. En raison des limitations et caractéristiques inhérentes aux capteurs, le routage dans les réseaux de capteurs sans fil suppose un vrai défi. La tâche de trouver et de maintenir des routes n'est pas triviale étant donné les restrictions d'énergie et les changements soudains dans l'état des noeuds (exemple: mal-fonctionnement) qui entrainent des changements fréquents et imprévisibles dans la structure topologique. Ce travail présente LBRA, un nouveau protocole de routage géolocalisé qui utilise des antennes intelligentes pour estimer les positions des noeuds dans le réseau, et qui base les décisions de routage sur l’état de connexion des voisins et leur position relative. L'objectif principal de LBRA est d'éliminer le trafic de contrôle du réseau autant que possible. Pour atteindre cet objectif, l'algorithme emploie la position locale pour prendre des décisions de routage, met en oeuvre un nouveau mécanisme pour recueillir les informations de localisation et utilise seulement les noeuds impliqués dans la route pour faire la synchronisation des données de positionnement. De plus, le protocole considère le niveau de la batterie au moment de prendre des décisions de routage afin de balancer la dépense d’énergie du réseau. LBRA est une version améliorée du routage de ZigBee (norme actuelle pour les réseaux à faible coût et à faible consommation d’énergie) qui se base, lui aussi, sur AODV. Afin d'évaluer dans quelle mesure LBRA représente vraiment une amélioration par rapport au routage de ZigBee, une série de simulations a été effectué à l'aide du logiciel Network Simulator (ns). Les deux protocoles ont été implantés dans le simulateur. Les performances ont été comparées dans une variété de scenarios, dans des conditions différentes tels que les charges de trafic, les tailles de réseau et les conditions de mobilité. Les résultats des expériences ont montré que LBRA réussi à réduire le trafic de contrôle et la charge de routage, tout en améliorant le taux de livraison des paquets, à la fois pour les réseaux fixes et les réseaux mobiles. L'abaissement de l'alimentation du réseau est aussi plus équilibré, puisque les décisions de routage sont prises en fonction du niveau de la batterie des noeuds.----------ABSTRACT Wireless sensor networks are an emerging technology for environmental monitoring. A typical sensor network is composed of a large number of low-cost, low-power, multi-functional miniature sensor devices (nodes) equipped with a radio transceiver and a set of transducers utilized to acquire information about the surrounding environment. One of the most important constraints of sensor nodes is the low power consumption requirement since they carry limited, generally irreplaceable, batteries. In addition, they are also characterized by scarce processing speed, storage capacity and communication bandwidth, thus requiring careful resource management. Due to the inherent characteristics and restrictions of sensor nodes, routing in WSNs is very challenging. The task of finding and maintaining routes is nontrivial since energy restrictions and sudden changes in node status (e.g. failure) cause frequent and unpredictable topological changes. This work introduces a novel location routing protocol that uses smart antennas to estimate nodes positions into the network and to deliver information basing routing decisions on neighbour’s status connection and relative position, named LBRA. The main purpose of LBRA is to eliminate network control overhead as much as possible. To achieve this goal, the algorithm employs local position for route decision, implements a novel mechanism to collect the location information and involves only route participants in the synchronization of location information. In addition, the protocol uses node battery information to make power aware routing decisions. LBRA is an enhanced version of the ZigBee routing, which is the current standard for reliable, cost-effective and low power wireless networking, and like the latter is prototyped from AODV. In order to asses to what extent LBRA truly represents an improvement with respect to the ZigBee routing, a series of simulations were designed with the help of the Network Simulator (ns). Basically, both protocols were implemented in the simulator and its performance was compared in a variety of traffic load, network size and mobility conditions. The experiment results showed that LBRA succeed in reducing the control overhead and the routing load, improving the packet delivery rate for both static and mobile networks. Additionally, network power depletion is more balanced, since routing decisions are made depending on nodes’ battery level

Interval Kalman Filtering Techniques for Unmanned Surface Vehicle Navigation

In reference to IEEE copyrighted material which is used with permission in this thesis, the IEEE does not endorse any of Plymouth University's products or services. Internal or personal use of this material is permitted. If interested in reprinting/republishing IEEE copyrighted material for advertising or promotional purposes or for creating new collective works for resale or redistribution, please go to http://www.ieee.org/publications_standards/publications/rights/rights_link.html to learn how to obtain a License from RightsLink.This thesis is about a robust filtering method known as the interval Kalman filter (IKF), an extension of the Kalman filter (KF) to the domain of interval mathematics. The key limitation of the KF is that it requires precise knowledge of the system dynamics and associated stochastic processes. In many cases however, system models are at best, only approximately known. To overcome this limitation, the idea is to describe the uncertain model coefficients in terms of bounded intervals, and operate the filter within the framework of interval arithmetic. In trying to do so, practical difficulties arise, such as the large overestimation of the resulting set estimates owing to the over conservatism of interval arithmetic. This thesis proposes and demonstrates a novel and effective way to limit such overestimation for the IKF, making it feasible and practical to implement. The theory developed is of general application, but is applied in this work to the heading estimation of the Springer unmanned surface vehicle, which up to now relied solely on the estimates from a traditional KF. However, the IKF itself simply provides the range of possible vehicle headings. In practice, the autonomous steering system requires a single, point-valued estimate of the heading. In order to address this requirement, an innovative approach based on the use of machine learning methods to select an adequate point-valued estimate has been developed. In doing so, the so called weighted IKF (wIKF) estimate provides a single heading estimate that is robust to bounded model uncertainty. In addition, in order to exploit low-cost sensor redundancy, a multi-sensor data fusion algorithm compatible with the wIKF estimates and which additionally provides sensor fault tolerance has been developed. All these techniques have been implemented on the Springer platform and verified experimentally in a series of full-scale trials, presented in the last chapter of the thesis. The outcomes demonstrate that the methods are both feasible and practicable, and that they are far more effective in providing accurate estimates of the vehicle’s heading than the conventional KF when there is uncertainty in the system model and/or sensor failure occurs.EPSR

Mobile Robots Navigation

Mobile robots navigation includes different interrelated activities: (i) perception, as obtaining and interpreting sensory information; (ii) exploration, as the strategy that guides the robot to select the next direction to go; (iii) mapping, involving the construction of a spatial representation by using the sensory information perceived; (iv) localization, as the strategy to estimate the robot position within the spatial map; (v) path planning, as the strategy to find a path towards a goal location being optimal or not; and (vi) path execution, where motor actions are determined and adapted to environmental changes. The book addresses those activities by integrating results from the research work of several authors all over the world. Research cases are documented in 32 chapters organized within 7 categories next described

Effects of errorless learning on the acquisition of velopharyngeal movement control

Session 1pSC - Speech Communication: Cross-Linguistic Studies of Speech Sound Learning of the Languages of Hong Kong (Poster Session)The implicit motor learning literature suggests a benefit for learning if errors are minimized during practice. This study investigated whether the same principle holds for learning velopharyngeal movement control. Normal speaking participants learned to produce hypernasal speech in either an errorless learning condition (in which the possibility for errors was limited) or an errorful learning condition (in which the possibility for errors was not limited). Nasality level of the participants’ speech was measured by nasometer and reflected by nasalance scores (in %). Errorless learners practiced producing hypernasal speech with a threshold nasalance score of 10% at the beginning, which gradually increased to a threshold of 50% at the end. The same set of threshold targets were presented to errorful learners but in a reversed order. Errors were defined by the proportion of speech with a nasalance score below the threshold. The results showed that, relative to errorful learners, errorless learners displayed fewer errors (50.7% vs. 17.7%) and a higher mean nasalance score (31.3% vs. 46.7%) during the acquisition phase. Furthermore, errorless learners outperformed errorful learners in both retention and novel transfer tests. Acknowledgment: Supported by The University of Hong Kong Strategic Research Theme for Sciences of Learning © 2012 Acoustical Society of Americapublished_or_final_versio