Ph.D. Theses
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Browsing Ph.D. Theses by Subject "Autonomous robots."
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Item A collaborative multi-robot localization technique for autonomous robots(Thesis (Ph.D.)-Bogazici University. Institute for Graduate Studies in Science and Engineering, 2007., 2007.) Bağcı, Hatice Köse.; Akın, H. Levent.This work proposes a novel method for collaborative global localization of a team of soccer playing autonomous robots. It is also applicable to other indoor real-time robot applications in noisy, unpredictable environments, with insufficient perception. A novel solution, Reverse Monte Carlo Localization (R-MCL) is designed to solve single self-localization problem using local perception and action about the surrounding environment for each robot. R-MCL is a hybrid method based on Markov Localization (ML) and Monte Carlo Localization (MCL) where the ML based part finds the region where the robot should be and the MCL based part predicts the geometrical location with high precision by selecting samples in this region. In the multi-robot localization problem, robots use their own local position estimations, and the shared information from other team mates, to localize themselves. To integrate the local information and beliefs optimally, avoid conflicts and support collaboration among team members, a novel collaborative multi-robot localization method called Collaborative Reverse Monte Carlo Localization (CR-MCL), based on R-MCL, is presented. When robots detect each other, they share the grid cells representing this observation. The power of the method comes from its hybrid nature. It uses a grid based approach to handle detections which can not be accurate in real-time applications, and sample based approach in self-localization to improve its success, although it uses lower amount of samples compared to similar methods. Both methods are tested using simulated robots and real robots and results show that they are fast, robust, accurate and cheap in terms of communication, memory and computational costs.Item Multi-resolution model plus correction paradigm for task and skill refinement on autonomous robots(Thesis (Ph.D.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 2011., 2011.) Meriçli, Çetin.; Akın, H. Levent.; Veloso, Manuela.Robots need to be taught what type of tasks or skills they are expected to perform, and how to perform those particular tasks or skills. However, there is no universally accepted single approach for transferring the task and skill knowledge to a robot. Among several popular approaches, the most widely adopted method for transferring the task or skill knowledge to the robot is to develop an algorithm for performing the task or skill in question. Such a development requires a model of the system to be available. Moreover, despite that it usually is easier to develop a simple algorithm to handle trivial cases, it becomes a time consuming process to keep re ning the algorithm by modifying the underlying model to handle more complex situations. Learning from Demonstration (LfD) is another popular approach for transferring the task and skill knowledge to the robot. Instead of explicit programming, a teacher demonstrates the robot how to perform the task or skill and the robot records the demonstrated action together with the perceived state of the system at the time of demonstration. An execution policy is then derived out of the recorded demonstration data for reproducing the task or skill. Depending on the complexity of the task or skill in question and the robotic platform to be used, providing su cient number of examples in order to be able to extract a generalized execution policy can be a very time consuming process. This thesis contributes a novel complementary corrective demonstration paradigm called Model Plus Correction (M+C) for task and skill re nement on autonomous robots. The M+C approach strikes a balance between model-based and data-driven methods by combining them in a complementary manner. We assume the availability of an algorithm capable of performing the task or skill in question with limited success in terms of performance. Our approach utilizes a human teacher who observes the partially successful execution of the task, and corrects the action of the robot when the default algorithm is unable to select an appropriate action to be executed. The collected demonstration data stamped with the state of the system at the time of demonstration is then used to augment the default algorithm by modifying the action computed by the algorithm according to a correction reuse function, and the state of the system. This thesis also introduces an algorithm for using the same complementary corrective demonstration approach at multiple detail resolutions. The Multi-Resolution Model Plus Correction (MRM+C) algorithm assumes that a set of detail levels are de- ned with di erent state and action representations together with a di erent modelbased controller for each detail level are available at hand. The teacher provides demonstration for which detail resolution to use at a particular state of the system in addition to delivering corrective demonstration for the controller associated with the current detail resolution. Having multiple detail resolutions with di erent complexities allows the system to use more detailed state and action representations and more complex model-based controllers only when needed. Using a less detailed state and action representation with a simpler controller makes it possible to cover the solution space at a lower computational cost and using fewer number of demonstrations. The learned detail resolution selection policy favors the least detailed resolution by default and switches to a more detailed resolution if commanded to do so in a similar state before. We present experiment results where the M+C approach is rst applied to a complex biped walk stability improvement problem as an example to the skill refinement, and to a ball dribbling problem in a robot soccer environment as an example to the task re nement. We also present experiment results where the MRM+C approach is applied to a humanoid obstacle avoidance task on a robot soccer eld. Finally, we present an experimental analysis of the proposed algorithms in terms of their robustness against uncertainty and the cost analysis of using multiple detail resolutions over using a single detail resolution in a simulated version of the obstacle avoidance task.Item Person detection and tracking using omnidirectional cameras, and rectangle blanket problem(Thesis (Ph.D.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 2019., 2019.) Demiröz, Barış Evrim.; Akarun, Lale.; Salah, Albert Ali.Person detection and tracking can provide the crucial analysis needed to avoid accidents with autonomous machinery, optimize environments for effciency and assist the elderly. Omnidirectional cameras have a large field of view that allow them to cover more ground at the expense of resolution. Omnidirectional cameras can decrease setup, maintenance and computational costs by reducing the number of cameras and the bandwidth required. Computer vision methods developed for conventional cameras usually fail for omnidirectional cameras due to their di erent image formation geometry. In this thesis, rst, a novel dataset for person tracking in omnidirectional cameras is introduced. The dataset, namely BOMNI, contains 46 videos of persons moving inside a room; where the bounding boxes and the identity of the persons are annotated at every frame. Second, a generative Bayesian framework is developed for coupling person tracking and fall detection. The method is evaluated on BOMNI dataset, producing 93% tracking accuracy and fall detection within a few frames of the event. Third, a similar method for multiple person tracking is developed and evaluated on BOMNI dataset. The method reaches 86% tracking accuracy, increasing a previous approach by 18%. Fourth, a discriminative method for person detection is presented. Also a novel structure called Radial Integral Image that speeds up feature extraction step is introduced. This method achieves state of the art detection performance on IYTE dataset: 4.5% miss rate for one false positive per image. Finally, the problem of representing a shape with multiple rectangles, Rectangle Blanket Problem, is formulated as an integer programming problem and a branch-and-bound scheme is presented along with a novel branching rule to solve it optimally. This problem is encountered in the earlier sections of this thesis, but it is a general problem that is present in the literature.