In this thesis, performance of the Contribution Reward Routing Protocol with Shapley Value (CAP-SV), a power-aware routing protocol for ad hoc networking is analyzed. Literature study on ad hoc network routing and ower-awareness is given. The overhead induced by the extra packets of the redirection mechanism of CAP-SV is formulized and the factors affecting this overhead are discussed. Then, the power consumption of CAP-SV is analytically analized using a linear power consumption model. It is shown that CAP-SV performs better than AODV regarding power consumption. The analysis validates the simulation results reported in the literature and provides general principles of how protocol and scenario parameters affect the performance.

In this thesis, a new real-time multi-agent moving target pursuit algorithm and a moving target algorithm are developed and implemented. The environment is a grid world, in which a coordinated team of agents cooperatively blocks the possible escape routes of an intelligent target in real-time. Most of the moving target search algorithms presume that the agents are faster than the targets, so the pursuit is sure to end in favor of the agents. In this work, we relax this assumption and assume that all the moving objects have the same speed. This means that the agents must find a new approach for success in the pursuit, other than just chasing the targets. When the search agents and the moving targets are moving with the same speed, we need more than one search agent which can coordinate with the other agents to capture the target. Agents are allowed to communicate with each other. We propose a multi-agent search algorithm for this problem. To our best knowledge, there is no alternative algorithm designed based on these assumptions. The proposed algorithm is compared to the modified versions of its counterparts (A*, MTS and its derivatives) experimentally.

The problem of providing Quality of Service (QoS) in the Internet has been an extremely active area of research and various mechanisms have been proposed related to this subject. Developing network applications have requirements such as bounded delay, jitter, minimum bandwidth and maximum loss rate. There is also a need to support large bandwidth networks because of growing link speeds. Previous QoS efforts did not fully satisfy all these needs of future networks but more recent approaches aim to be both scalable and rich in the provision of guaranteed services. Consequently core-stateless systems received much attention in recent years because of their scalability in supporting per-flow QoS. The property of not maintaining any per-flow state in the core routers is known as being core-stateless. In this thesis study, the need for core-stateless network architectures is pointed out and a literature survey about these schemes is carried out. Core-Stateless Guaranteed Fair (CSGF) network architecture, which provides deterministic fairness guarantees in a work-conserving manner, is selected and evaluated. Simulation studies about stateful Virtual Clock (VC) algorithm and CSGF&rsquo / s sub-protocols Core-Stateless Virtual Clock (CSVC), Core-Stateless Guaranteed Throughput (CSGT) and Core-Stateless Guaranteed Fairness (CSGF) are presented. Finally, the deficiencies in fairness of CSGF are demonstrated.

This thesis proposes a framework for controlling mobile robot networks based on a fluid dynamics paradigm. The approach is inspired by natural behaviors of fluids demonstrating desirable characteristics for collective robots. The underlying mathematical formalism is developed through establishing analogies between fluid bodies and multi-robot systems such that robots are modeled as fluid elements that constitute a fluid body. The governing equations of fluid dynamics are adapted to multi-robot systems and applied on control of robots. The model governs flow of a robot based on its local interactions with neighboring robots and surrounding environment. Therefore, it provides a layer of decentralized reactive control on low level behaviors, such as obstacle avoidance, deployment, and flow. These behaviors are inherent to the nature of fluids and provide emergent coordination among robots. The framework also introduces a high-level control layer that can be designed according to requirements of the particular task. Emergence of cooperation and collective behavior can be controlled in this layer via a set of parameters obtained from the mathematical description of the system in the lower layer. Validity and potential of the approach have been experimented through simulations primarily on two common collective robotic tasks / deployment and navigation. It is shown that gas-like mobile sensor networks can provide effective coverage in unknown, unstructured, and dynamically changing environments through self-spreading. On the other hand, robots can also demonstrate directional flow in navigation or path following tasks, showing that a wide range of multi-robot applications can potentially be developed using the framework.

&ldquo / Developmental robotics&rdquo / proposes that, instead of trying to build a robot that shows intelligence once and for all, what one must do is to build robots that can develop. A robot should go through cognitive development just like an animal baby does. These robots should be equipped with behaviors that are simple but enough to bootstrap the system. Then, as the robot interacts with its environment, it should display increasingly complex behaviors. Studies in developmental psychology and neurophysiology provide support for the view that, the animals start with innate simple behaviors, and develop more complex behaviors through the differentiation, sequencing, and combination of these primitive behaviors. In this thesis, we propose such a development scheme for a mobile robot. J.J. Gibson&#039 / s concept of &ldquo / affordances&rdquo / provides the basis of this development scheme, and we use a formalization of affordances to make the robot learn about the dynamics of its interactions with its environment. We show that an autonomous robot can start with pre-coded primitive behaviors, and as it executes its behaviors randomly in an environment, it can learn the affordance relations between the environment and its behaviors. We then present two ways of using these learned structures, in achieving more complex, voluntary behaviors. In the first case, the robot still uses its pre-coded primitive behaviors only, but the sequencing of these are such that new more complex behaviors emerge. In the second case, the robot uses its pre-coded primitive behaviors to create new behaviors.

In the last decade, computational power, memory bandwidth and programmability capabilities of graphics processing units (GPU) have rapidly evolved. Therefore, many researches have been performed to use GPUs in advanced graphics rendering. Because of its high degree of parallelism, ray tracing has been one of the rst algorithms studied on GPUs. However, the rendering of large scenes with ray tracing can easily exceed the GPU&#039 / s memory capacity. The algorithm proposed in this work uses a data parallel approach where the scene is partitioned and assigned to CPU-GPU couples in a cluster to overcome this problem. Our algorithm focuses on ray casting which is a special case of ray tracing mainly used in visualization of volumetric data. CPUs are pretty ecient in ow control and branching while GPUs are very fast performing intense oating point operations. Using these facts, the GPUs in the cluster are assigned the task of performing ray casting while the CPUs are responsible for traversing the rays. In the end, we were able to visualize large scenes successfully by utilizing CPU-GPU couples eectively and observed that the performance is highly dependent on the viewing angle as a result of load imbalance.

The aim of this thesis is to reconstruct 3D representation of underwater scenes from uncalibrated video sequences. Underwater visualization is important for underwater Remotely Operated Vehicles and underwater is a complex structured environment because of inhomogeneous light absorption and light scattering by the environment. These factors make 3D reconstruction in underwater more challenging. The reconstruction consists of the following stages: Image enhancement, feature detection and matching, fundamental matrix estimation, auto-calibration, recovery of extrinsic parameters, rectification, stereo matching and triangulation. For image enhancement, a pre-processing filter is used to remove the effects of water and to enhance the images. Two feature extraction methods are examined: 1. Difference of Gaussian with SIFT feature descriptor, 2. Harris Corner Detector with grey level around the feature point. Matching is performed by finding similarities of SIFT features and by finding correlated grey levels respectively for each feature extraction method. The results show that SIFT performs better than Harris with grey level information. RANSAC method with normalized 8-point algorithm is used to estimate fundamental matrix and to reject outliers. Because of the difficulties of calibrating the cameras in underwater, auto-calibration process is examined. Rectification is also performed since it provides epipolar lines coincide with image scan lines which is helpful to stereo matching algorithms. The Graph-Cut stereo matching algorithm is used to compute corresponding pixel of each pixel in the stereo image pair. For the last stage triangulation is used to compute 3D points from the corresponding pixel pairs.

For an autonomous mobile robot, localization and map building are vital capabilities. The localization ability provides the robot location information, so the robot can navigate in the environment. On the other hand, the robot can interact with its environment using a model of the environment (map information) which is provided by map building mechanism. These two capabilities depends on each other and simultaneous operation of them is called SLAM (Simultaneous Localization and Map Building). While various sensors are used for this algorithm, vision-based approaches are relatively new and have attracted more interest in recent years. In this thesis work, a versatile Visual SLAM system is constructed and presented. In the core of this work is a vision-based simultaneous localization and map building algorithm which uses point features in the environment as visual landmarks and Extended Kalman Filter for state estimation. A detailed analysis of this algorithm is made including state estimation, feature extraction and data association steps. The algorithm is extended to be used for both stereo and single camera systems. The core of both algorithms is same and we mention the differences of both algorithms originated from the measurement dissimilarity. The algorithm is run also in different motion modes, namely predefined, manual and autonomous. Secondly, a map management strategy is developed especially for extended environments. When the robot runs the SLAM algorithm in large environments, the constructed map contains a great number of landmarks obviously. The efficiency algorithm takes part, when the total number of features exceeds a critical value for the system. In this case, the current map is rarefied without losing the geometrical distribution of the landmarks. Furthermore, a well-organized graphical user interface is implemented which enables the operator to select operational modes, change various parameters of the main SLAM algorithm and see the results of the SLAM operation both textually and graphically. Finally, a basic mission concept is defined in our system, in order to illustrate what robot can do using the outputs of the SLAM algorithm. All of these ideas mentioned are implemented in this thesis, experiments are conducted using a real robot and the analysis results are discussed by comparing the algorithm outputs with ground-truth measurements.

An inspection approach is proposed for improving the quality of conceptual models developed in a domain specific notation. First, the process of identification of desirable properties of conceptual models in a domain specific notation is described. Intra- and interview properties are considered. Semantic properties are defined considering the conceptual modeling notation. A systematic inspection process is proposed for checking semantic properties of different types of diagrams and of the relations between these diagrams. This process is applied to two real mission space conceptual models. With the proposed inspection approach, it is possible to identify subtle semantic issues which are not identified by many of the contemporary UML CASE tools and other inspection methods.

In recent years, web services have become an emerging technology for communication and integration between applications in many areas such as business to business (B2B) or business to commerce (B2C). In this growing technology, it is hard to compose web services manually because of the increasing number and compexity of web services. Therefore, automation of this composition process has gained a considerable amount of popularity. Automated web service composition can be achieved either by generating the composition plan dynamically using given inputs and outputs, or by locating the correct services if an abstract process model is given. This thesis investigates the former method which is dynamicly generating the composition by using the abductive lanning capabilities of the Event Calculus. Event calculus axioms in Prolog language, are generated using the available OWL-S web service descriptions in the service repository, values given to selected inputs from ontologies used by those semantic web services and desired output types selected again from the ontologies. Abductive Theorem Prover which is the AI planner used in this thesis, generates composition plans and execution results according to the generated event calculus axioms. In this thesis, it is shown that abductive event calculus can be used for generating web services composition plans automatically, and returning the results of the generated plans by executing the necessary web services.