Indoor Geo-location And Tracking Of Mobile Autonomous Robot

The field of robotics has always been one of fascination right from the day of Terminator. Even though we still do not have robots that can actually replicate human action and intelligence, progress is being made in the right direction. Robotic applications range from defense to civilian, in public safety and fire fighting. With the increase in urban-warfare robot tracking inside buildings and in cities form a very important application. The numerous applications range from munitions tracking to replacing soldiers for reconnaissance information. Fire fighters use robots for survey of the affected area. Tracking robots has been limited to the local area under consideration. Decision making is inhibited due to limited local knowledge and approximations have to be made. An effective decision making would involve tracking the robot in earth co-ordinates such as latitude and longitude. GPS signal provides us sufficient and reliable data for such decision making. The main drawback of using GPS is that it is unavailable indoors and also there is signal attenuation outdoors. Indoor geolocation forms the basis of tracking robots inside buildings and other places where GPS signals are unavailable. Indoor geolocation has traditionally been the field of wireless networks using techniques such as low frequency RF signals and ultra-wideband antennas. In this thesis we propose a novel method for achieving geolocation and enable tracking. Geolocation and tracking are achieved by a combination of Gyroscope and encoders together referred to as the Inertial Navigation System (INS). Gyroscopes have been widely used in aerospace applications for stabilizing aircrafts. In our case we use gyroscope as means of determining the heading of the robot. Further, commands can be sent to the robot when it is off balance or off-track. Sensors are inherently error prone; hence the process of geolocation is complicated and limited by the imperfect mathematical modeling of input noise. We make use of Kalman Filter for processing erroneous sensor data, as it provides us a robust and stable algorithm. The error characteristics of the sensors are input to the Kalman Filter and filtered data is obtained. We have performed a large set of experiments, both indoors and outdoors to test the reliability of the system. In outdoors we have used the GPS signal to aid the INS measurements. When indoors we utilize the last known position and extrapolate to obtain the GPS co-ordinates

An intelligent classification system for land use and land cover mapping using spaceborne remote sensing and GIS

The objectives of this study were to experiment with and extend current methods of Synthetic Aperture Rader (SAR) image classification, and to design and implement a prototype intelligent remote sensing image processing and classification system for land use and land cover mapping in wet season conditions in Bangladesh, which incorporates SAR images and other geodata. To meet these objectives, the problem of classifying the spaceborne SAR images, and integrating Geographic Information System (GIS) data and ground truth data was studied first. In this phase of the study, an extension to traditional techniques was made by applying a Self-Organizing feature Map (SOM) to include GIS data with the remote sensing data during image segmentation. The experimental results were compared with those of traditional statistical classifiers, such as Maximum Likelihood, Mahalanobis Distance, and Minimum Distance classifiers. The performances of the classifiers were evaluated in terms of the classification accuracy with respect to the collected real-time ground truth data. The SOM neural network provided the highest overall accuracy when a GIS layer of land type classification (with respect to the period of inundation by regular flooding) was used in the network. Using this method, the overall accuracy was around 15% higher than the previously mentioned traditional classifiers. It also achieved higher accuracies for more classes in comparison to the other classifiers. However, it was also observed that different classifiers produced better accuracy for different classes. Therefore, the investigation was extended to consider Multiple Classifier Combination (MCC) techniques, which is a recently emerging research area in pattern recognition. The study has tested some of these techniques to improve the classification accuracy by harnessing the goodness of the constituent classifiers. A Rule-based Contention Resolution method of combination was developed, which exhibited an improvement in the overall accuracy of about 2% in comparison to its best constituent (SOM) classifier. The next phase of the study involved the design of an architecture for an intelligent image processing and classification system (named ISRIPaC) that could integrate the extended methodologies mentioned above. Finally, the architecture was implemented in a prototype and its viability was evaluated using a set of real data. The originality of the ISRIPaC architecture lies in the realisation of the concept of a complete system that can intelligently cover all the steps of image processing classification and utilise standardised metadata in addition to a knowledge base in determining the appropriate methods and course of action for the given task. The implemented prototype of the ISRIPaC architecture is a federated system that integrates the CLIPS expert system shell, the IDRISI Kilimanjaro image processing and GIS software, and the domain experts' knowledge via a control agent written in Visual C++. It starts with data assessment and pre-processing and ends up with image classification and accuracy assessment. The system is designed to run automatically, where the user merely provides the initial information regarding the intended task and the source of available data. The system itself acquires necessary information about the data from metadata files in order to make decisions and perform tasks. The test and evaluation of the prototype demonstrates the viability of the proposed architecture and the possibility of extending the system to perform other image processing tasks and to use different sources of data. The system design presented in this study thus suggests some directions for the development of the next generation of remote sensing image processing and classification systems

Intelligent information retrieval and fault diagnosis for the asset management of power substations

This thesis mainly presents two intelligent approaches to the Asset Management (AM) of power substations, which include an Evidential Reasoning (ER)-based document ranking approach to an Ontology-based Document Search Engine (ODSE) for the Information Retrieval (IR) of power substations and an Association Rule Mining (ARM)-based Dissolved Gas Analysis (DGA) approach to the Fault Diagnosis (FD) of power transformers

Advances and Applications of Dezert-Smarandache Theory (DSmT) for Information Fusion (Collected Works), Vol. 4

The fourth volume on Advances and Applications of Dezert-Smarandache Theory (DSmT) for information fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics. The contributions (see List of Articles published in this book, at the end of the volume) have been published or presented after disseminating the third volume (2009, http://fs.unm.edu/DSmT-book3.pdf) in international conferences, seminars, workshops and journals. First Part of this book presents the theoretical advancement of DSmT, dealing with Belief functions, conditioning and deconditioning, Analytic Hierarchy Process, Decision Making, Multi-Criteria, evidence theory, combination rule, evidence distance, conflicting belief, sources of evidences with different importance and reliabilities, importance of sources, pignistic probability transformation, Qualitative reasoning under uncertainty, Imprecise belief structures, 2-Tuple linguistic label, Electre Tri Method, hierarchical proportional redistribution, basic belief assignment, subjective probability measure, Smarandache codification, neutrosophic logic, Evidence theory, outranking methods, Dempster-Shafer Theory, Bayes fusion rule, frequentist probability, mean square error, controlling factor, optimal assignment solution, data association, Transferable Belief Model, and others. More applications of DSmT have emerged in the past years since the apparition of the third book of DSmT 2009. Subsequently, the second part of this volume is about applications of DSmT in correlation with Electronic Support Measures, belief function, sensor networks, Ground Moving Target and Multiple target tracking, Vehicle-Born Improvised Explosive Device, Belief Interacting Multiple Model filter, seismic and acoustic sensor, Support Vector Machines, Alarm classification, ability of human visual system, Uncertainty Representation and Reasoning Evaluation Framework, Threat Assessment, Handwritten Signature Verification, Automatic Aircraft Recognition, Dynamic Data-Driven Application System, adjustment of secure communication trust analysis, and so on. Finally, the third part presents a List of References related with DSmT published or presented along the years since its inception in 2004, chronologically ordered

Probabilistic fuzzy logic framework in reinforcement learning for decision making

This dissertation focuses on the problem of uncertainty handling during learning by agents dealing in stochastic environments by means of reinforcement learning. Most previous investigations in reinforcement learning have proposed algorithms to deal with the learning performance issues but neglecting the uncertainty present in stochastic environments. Reinforcement learning is a valuable learning method when a system requires a selection of actions whose consequences emerge over long periods for which input-output data are not available. In most combinations of fuzzy systems with reinforcement learning, the environment is considered deterministic. However, for many cases, the consequence of an action may be uncertain or stochastic in nature. This work proposes a novel reinforcement learning approach combined with the universal function approximation capability of fuzzy systems within a probabilistic fuzzy logic theory framework, where the information from the environment is not interpreted in a deterministic way as in classic approaches but rather, in a statistical way that considers a probability distribution of long term consequences. The generalized probabilistic fuzzy reinforcement learning (GPFRL) method, presented in this dissertation, is a modified version of the actor-critic learning architecture where the learning is enhanced by the introduction of a probability measure into the learning structure where an incremental gradient descent weight- updating algorithm provides convergence. XXIABSTRACT Experiments were performed on simulated and real environments based on a travel planning spoken dialogue system. Experimental results provided evidence to support the following claims: first, the GPFRL have shown a robust performance when used in control optimization tasks. Second, its learning speed outperforms most of other similar methods. Third, GPFRL agents are feasible and promising for the design of adaptive behaviour robotics systems.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Proceedings of the 5th MIT/ONR Workshop on C[3] Systems, held at Naval Postgraduate School, Monterey, California, August 23 to 27, 1982

"December 1982."Includes bibliographies and index.Office of Naval Research Contract no. ONR/N00014-77-C-0532 NR041-519edited by Michael Athans ... [et al.]