Browsing by Author "Bayram, Haluk."

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    Multirobot communication and task coordination
    (Thesis (Ph.D.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 2013., 2013.) Bayram, Haluk.; Bozma, H. Işıl.; Ertüzün, Ayşın.
    This thesis is concerned with multi-robot systems. Robots are increasingly being used in applications such as patrolling or exploration whose natures necessitate the consideration of multiple cooperating robots. The cooperation may vary from sole communication to collaboration. In this perspective, the cooperation problem is examined from three critical and related aspects. First, the problem of achieving effective communication topologies is considered. Two alternative approaches - namely decentralized or centralized network topologies - are proposed based on pairwise games. Depending on the robots’ objectives, the pairwise stability of the resulting networks and the convergence of the associated games are analyzed. Next, the problem of determining the assisting robots in tasks requiring the cooperation of robot pairs is considered. For this, the concept of assistance networks is introduced. Assistance networks designate potential helpmates in an implicit manner. The network topology is determined by a coordinator in a manner similar to that of centralized network topologies. Finally, the more general case of tasks requiring a multitude of resources is considered. A task coordinator is held responsible for finding robot coalitions endowed with these resources with minimal cost. This process is modeled as a series of coalition formation games. Extensive simulation and experimental results demonstrate the practical applicability of the proposed approaches in real-time multi-robot applications.
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    Robot parts' rearrangement-sensor uncertainty reduction using particle filters
    (Thesis (M.S.)-Bogazici University. Institute for Graduate Studies in Science and Engineering, 2006., 2006.) Bayram, Haluk.; Bozma, H. Işıl.; Ertüzün, Ayşın.
    This thesis adresses the parts’ moving problem under noisy sensory information. In this scenario, a 2D workspace contains an actuated robot and a set of unactuated parts. The discrepancy between the robot’s and/or the parts’ real and measured positions may lead to jerky movements or even collisions in the parts’ moving problem we are concerned with. In contrast to previous work, sensory data is no longer assumed to be perfect. Hence the robot needs to approximate state information, taking its highly nonlinear nature into account. It accomplishes this using particle filters, which implement a recursive Bayesian filter in nonlinear and/or nongaussian environments. For the model of parts which turns out to be linear, the approach reduces to Kalman filtering. First the robot’s dynamic model and the measurement model are modified to incorporate the inaccuracies in the sensory data; and then the particle filter is utilized to get improved positional estimates. Enhancements in the robot’s movements and reduction in the number of collisions have been verified through extensive computer simulations. An evaluation of its theoretical performance is presented based on the Cramer-Rao lower bound. Finally, a series of experiments with EDAR provide insight into real-time performance.