Sistem ve Kontrol Mühendisliği

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Browsing Sistem ve Kontrol Mühendisliği by Author "Bozma, H. Işıl."

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    Depth-based scene mapping through spatio-temporal knowledge integration
    (Thesis (M.S.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 2021., 2021.) Durukan, Meriç.; Bozma, H. Işıl.
    This thesis is concerned with scene mapping by a mobile robot using point cloud data. It is a complex process that requires the robot to segment the incoming data, represent it compactly and e ciently, and then use the resulting knowledge in its learning and decision-making. Segmentation enables the robot to determine the point cloud object candidates. The robot bases its learning and reasoning on the detected segments. Range sensors, such as LIDAR, are essential for a robot to extract environmental information. However, they generally create sparse data. For this reason, the sparse data should be considered specially. A novel approach to segmentation is proposed based on an extension of density-based clustering in the spherical coordinate system. We present the deformable sphere approximation (DSA) descriptor as a novel 3D descriptor that encodes point cloud objects. Experimental results show that our representation method is capable of classifying the objects. Finally, we consider how the robot can use all knowledge available to it. We propose an approach in which the robot also considers the knowledge accumulated through tracking the objects' temporal continuity. For this, we propose the temporal deformable sphere approximation (TDSA) descriptor. Its construction requires the robot to track object candidates. For this, we propose a novel multi-tracking approach based on combining Kalman Filtering and multi-object matching considering position and shape similarity. We then compare the various schemes the robot can use in order to utilize the resulting knowledge. Our experimental results show that the T-DSA descriptor improves the classi cation performance compared to only the instantaneous DSA descriptors. As such, the robot is able to build and evolve a scene map as it is navigating in it.
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    Design of a social robot and safe social navigation with deep reinforcement learning
    (Thesis (M.S.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 2022., 2022) Bektaş, Kemal.; Bozma, H. Işıl.
    This thesis is concerned with the design and development of a social robot that can navigate around in a socially compliant manner. The importance of this problem is due to the growing demand of using robots in human-populated environments. In this thesis, this problem is addressed in two concurrent parts. The first part has focused on the physical design and development of a social robot - named as SempRob. SempRob is aimed to have a sympathetic appearance while also having a design in which its visual sensors are located appropriately for environmental sensing. In the second part, the social navigation capability of the social robot is developed. First, a novel navigation method referred to as artificial potential function with reinforcement learning (APF-RL) method. In addition, an ellipse-based representation of obstacles is developed for efficient obstacle representation. Furthermore, environmental complexity measures are defined in order to ensure that learning scenarios incorporate a range of maneuvering difficulties. Both simulation and experimental results with SempRob demonstrate that APF-RL method enables the robot to move safely and efficiently in complex environments. Following, APF-RL method is extended to Social APF-RL method so that the robot additionally respects the comfort zones of the humans while navigating. This requires the robot to detect the humans in its surroundings and to track them spatially. A deep learning based human detection algorithm is combined with a Kalman filter for this purpose. Finally, Social APF-RL method is modified to be applicable in human following as well. All the proposed methods are tested on the developed robot successfully.
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    Human-like coordination of body-assisted arm movements for object manipulation
    (Thesis (M.S.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 2019., 2019.) Cumali, Kadir.; Bozma, H. Işıl.
    Manipulation is an integral capability for service robots. The goal of this thesis is to design and develop an approach that enables a mobile robot to mimic human manipulation abilities. We consider a differential type of mobile robot that is endowed with an arm and gripper. The robot is assumed to have visual sensing so that it can determine the relative position of the object of interest. First, it is observed that hu mans exhibit various basic modes of interaction with an object of interest, including extension, flexion, gripping, release and translation. As such, the robot can be pro grammed to have similar capabilities through establishing the correspondence between the robot and a human with respect to the underlying manipulation mechanisms. More complex behaviors such as putting, pulling, pushing, and shaking are defined using a sequential composition of basic operations. Second, humans are observed to achieve these tasks through the coordination of their body and arm movements. For this, a control approach in which the movements of the robot body and manipulator are cou pled temporally and spatially is proposed. As such, if the object of interest is within the robot’s reach, then only arm movements are made. If this is not the case, the robot starts moving its body. Depending on the vicinity of the object, this may be accom panied by arm motion or not. The control algorithm results in the robot’s body and arm movements to be done in a coupled manner. The proposed approach is evaluated through an extensive set of experiments involving various manipulation tasks.
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    Multi-robot coordination based on noncooperative game approach
    (Thesis (M.S.)-Bogazici University. Institute for Graduate Studies in Science and Engineering, 2009., 2009.) Kalalıoğlu, Mehmet Emin.; Bozma, H. Işıl.
    The aim of this thesis is to develop a multi-robot coordination algorithm based on a noncooperative game theoretic approach. The robots are assigned the task of picking up and packaging the goods flowing in on a conveyor band. All the robots are identical and have non-overlapping workspaces on the conveyor band. The workspace of each robot is divided into a finite number of equal-sized subregions. During the course of the tasks, each robot has to first decide which subspace to pick up from and then do the picking. This cycle is repeated in a continual manner as long as the conveyor band is flowing. Optimality requires the packaging of maximum number of products within minimum amount of time. Our approach is based on noncooperative game formulation of the problem where the robots operate in a coordinated manner. Before a pickup, each robot decides on its action based on its gain estimated using its respective payoff function. Each payoff function takes its possible action and the actions of its neighboring robots into consideration. An algorithm based on this approach has been developed. The developed algorithm has been implemented and tested in a simulated environment containing a conveyor band with multiple robots. This environment has been programmed using the Webots simulation software. Its design and development has been such that different scenarios can be generated in a programmable manner via varying the initialization of the production parameters such as the number of robots, product feeding period and the speed of the robots accordingly. Our proposed algorithm has been employed in totally 27 scenarios. Results obtained from the simulations are analyzed using a variety of statistical measures.
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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.