Bilaloğlu, Cem
This thesis introduces Kobot -- an extensible heterogeneous swarm robot platform. Kobot platform uses a common hardware and software architecture based on off-the-shelf components, 3-D printing, and open-source software that evolves with state of the art. Robots built using this common architecture range from wheeled to flying robots and formed a heterogeneous swarm. The common architecture enabled developing and testing systems for the lightweight flying robots on resourceful ground robots. As a result, Kobot platform proved its significance for the future of swarm robotics research with multiple novel swarm behaviors implemented for the first time in real robots.


Improving the accuracy of a mobile robot for localization and mapping of an unknown environment
Gümrükcü, Gülşah; Konukseven, Erhan İlhan; Department of Mechanical Engineering (2003)
This thesis deals with sensor based motion planning of a mobile robot for localization in an unknown environment. Using the developed algorithm the robot may construct the map (GVG) of any bounded environment, and the minimum distance between any two locations in the mapped environment can be determined. In addition, the accuracy of the robot, facing dead-reckoning error can be improved. With this study, the mobile robot finds the optimum path between any two locations in any bounded environment and traces ...
Güneşdoğdu, Ali Nuri; Şahin, Erol; Acartürk, Cengiz; Department of Computer Engineering (2022-8-31)
Collaborative robots, a.k.a cobots, are industrial robotic manipulators that have no built-in capabilities for social human-robot interaction (HRI). In the thesis, we implemented breathing for a cobot as a social behavior inspired by the secondary action animation principle. We automatically generated breathing of a cobot as HRI behavior with its waveform borrowed from human breathing; its amplitude and frequency are parametrized. We conducted a user study to measure the effect of parameters of breathing b...
Development of a visual object localization module for mobile robots
Şahin, Erol (1999-01-01)
Reports preliminary results from the design and implementation of a visual object localization module for mobile robots. The module takes an object-based approach to visual processing and relies on a preprocessing step that segments objects from the image. By tracking the size and the eccentricity of the objects in the image while the robot is moving, the visual object localization module can determine the position of objects relative to the robot using the displacement obtained from its odometry. In locali...
The learning and use of traversability affordance using range images on a mobile robot
Ugur, Emre; Dogar, Mehmet R.; Cakmak, Maya; Şahin, Erol (2007-04-14)
We are interested in how the concept of affordances can affect our view to autonomous robot control, and how the results obtained from autonomous robotics can be reflected back upon the discussion and studies on the concept of affordances. In this paper, we studied how a mobile robot, equipped with a 3D laser scanner, can learn to perceive the traversability affordance and use it to wander in a room filled with spheres, cylinders and boxes. The results showed that after learning, the robot can wander around...
Design of a low-costs warm robotic system for flocking
Demir, Çağrı Ata; Turgut, Ali Emre; Department of Mechanical Engineering (2019)
Swarm robotics is an approach to the coordination of large numbers of robots. The main motivation of this thesis is to study a robotic system designed to do flocking both indoors and outdoors. A walking robot is designed parallel to this purpose. In the first part of thesis, a leg is designed to minimize the displacement of center of mass of robot in vertical axis to eliminate mechanical noise. Mechanism analysis and Matlab optimization tools are utilized in this process. Then, electronic components of robo...
Citation Formats
C. Bilaloğlu, “DEVELOPMENT OF AN EXTENSIBLE HETEROGENEOUS SWARM ROBOT PLATFORM,” M.S. - Master of Science, Middle East Technical University, 2022.